diff --git a/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb b/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb
index cb0b39b..6eb615e 100644
--- a/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb
+++ b/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -28,7 +28,9 @@
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -46,32 +48,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fea26320400>]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
    "source": [
-    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n"
+    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
    ]
   },
   {
diff --git a/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.py b/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.py
new file mode 100644
index 0000000..5e40e2c
--- /dev/null
+++ b/0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.py
@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+# ---
+# jupyter:
+#   jupytext_format_version: '1.2'
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+#   language_info:
+#     codemirror_mode:
+#       name: ipython
+#       version: 3
+#     file_extension: .py
+#     mimetype: text/x-python
+#     name: python
+#     nbconvert_exporter: python
+#     pygments_lexer: ipython3
+#     version: 3.5.2
+# ---
+
+# # matplotlib
+#
+#
+
+# ## 1. pyplot
+# matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that “axes” here and in most places in the documentation refers to the axes part of a figure and not the strict mathematical term for more than one axis).
+
+# +
+# This line configures matplotlib to show figures embedded in the notebook, 
+# instead of opening a new window for each figure. More about that later. 
+# If you are using an old version of IPython, try using '%pylab inline' instead.
+# %matplotlib inline
+
+import matplotlib.pyplot as plt
+plt.plot([1,2,3,4])
+plt.ylabel('some numbers')
+plt.show()
+# -
+
+plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
+
+
+# For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is ‘b-‘, which is a solid blue line. For example, to plot the above with red circles, you would issue
+
+import matplotlib.pyplot as plt
+plt.plot([1,2,3,4], [1,4,9,16], 'ro')
+plt.axis([0, 6, 0, 20])
+plt.show()
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+
+# evenly sampled time at 200ms intervals
+t = np.arange(0., 5., 0.2)
+
+# red dashes, blue squares and green triangles
+plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
+plt.show()
+# -
+
+# ### [Controlling line properties](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)
+#
+# Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2D. There are several ways to set line properties
+#
+
+# ### Working with multiple figures and axes
+#
+# MATLAB, and pyplot, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function gca() returns the current axes (a matplotlib.axes.Axes instance), and gcf() returns the current figure (matplotlib.figure.Figure instance). Normally, you don’t have to worry about this, because it is all taken care of behind the scenes. Below is a script to create two subplots.
+#
+#
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+
+def f(t):
+    return np.exp(-t) * np.cos(2*np.pi*t)
+
+t1 = np.arange(0.0, 5.0, 0.1)
+t2 = np.arange(0.0, 5.0, 0.02)
+
+plt.figure(1)
+plt.subplot(211)
+plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
+
+plt.subplot(212)
+plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
+plt.show()
+# -
+
+# ## 2. Image 
+
+# +
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import numpy as np
+
+# load image
+img=mpimg.imread('example.png')
+
+imgplot = plt.imshow(img)
+
+# -
+
+# ### Applying pseudocolor schemes to image plots
+
+lum_img = img[:,:,0]
+plt.imshow(lum_img)
+
+# use 'hot' color map
+plt.imshow(lum_img, cmap="hot")
+plt.colorbar()
+
+# ### Examining a specific data range
+#
+
+plt.hist(lum_img.ravel(), bins=256, range=(0.0, 1.0), fc='k', ec='k')
+
+# ## References
+#
+# * [Pyplot tutorial](https://matplotlib.org/users/pyplot_tutorial.html)
+# * [Image tutorial](https://matplotlib.org/users/image_tutorial.html)
diff --git a/0_numpy_matplotlib_scipy_sympy/numpy.ipynb b/0_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb
similarity index 100%
rename from 0_numpy_matplotlib_scipy_sympy/numpy.ipynb
rename to 0_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb
diff --git a/1_logistic_regression/Least_squares.ipynb b/1_logistic_regression/Least_squares.ipynb
index e6996ac..10cd6c9 100644
--- a/1_logistic_regression/Least_squares.ipynb
+++ b/1_logistic_regression/Least_squares.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -497,13 +497,7 @@
       "epoch  275: loss = 2018321.374339, a = 403.082778, b = 152.868098\n",
       "epoch  276: loss = 2017130.524440, a = 404.173271, b = 152.867402\n",
       "epoch  277: loss = 2015944.427495, a = 405.261583, b = 152.866707\n",
-      "epoch  278: loss = 2014763.064523, a = 406.347719, b = 152.866013\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch  278: loss = 2014763.064523, a = 406.347719, b = 152.866013\n",
       "epoch  279: loss = 2013586.416620, a = 407.431683, b = 152.865321\n",
       "epoch  280: loss = 2012414.464959, a = 408.513480, b = 152.864630\n",
       "epoch  281: loss = 2011247.190786, a = 409.593113, b = 152.863940\n",
@@ -975,7 +969,13 @@
       "epoch  747: loss = 1765080.409388, a = 736.413451, b = 152.655201\n",
       "epoch  748: loss = 1764899.846541, a = 736.837393, b = 152.654930\n",
       "epoch  749: loss = 1764720.003201, a = 737.260487, b = 152.654660\n",
-      "epoch  750: loss = 1764540.876496, a = 737.682735, b = 152.654390\n",
+      "epoch  750: loss = 1764540.876496, a = 737.682735, b = 152.654390\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  751: loss = 1764362.463568, a = 738.104139, b = 152.654121\n",
       "epoch  752: loss = 1764184.761567, a = 738.524700, b = 152.653853\n",
       "epoch  753: loss = 1764007.767659, a = 738.944420, b = 152.653585\n",
@@ -1500,7 +1500,13 @@
       "epoch 1272: loss = 1725310.896600, a = 874.295618, b = 152.567136\n",
       "epoch 1273: loss = 1725288.607410, a = 874.443841, b = 152.567042\n",
       "epoch 1274: loss = 1725266.406608, a = 874.591768, b = 152.566947\n",
-      "epoch 1275: loss = 1725244.293841, a = 874.739399, b = 152.566853\n",
+      "epoch 1275: loss = 1725244.293841, a = 874.739399, b = 152.566853\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1276: loss = 1725222.268760, a = 874.886735, b = 152.566759\n",
       "epoch 1277: loss = 1725200.331014, a = 875.033776, b = 152.566665\n",
       "epoch 1278: loss = 1725178.480257, a = 875.180523, b = 152.566571\n",
@@ -1691,13 +1697,7 @@
       "epoch 1463: loss = 1722320.669650, a = 897.846452, b = 152.552095\n",
       "epoch 1464: loss = 1722310.221696, a = 897.947581, b = 152.552030\n",
       "epoch 1465: loss = 1722299.815032, a = 898.048508, b = 152.551965\n",
-      "epoch 1466: loss = 1722289.449493, a = 898.149233, b = 152.551901\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1466: loss = 1722289.449493, a = 898.149233, b = 152.551901\n",
       "epoch 1467: loss = 1722279.124916, a = 898.249757, b = 152.551837\n",
       "epoch 1468: loss = 1722268.841137, a = 898.350080, b = 152.551773\n",
       "epoch 1469: loss = 1722258.597995, a = 898.450202, b = 152.551709\n",
@@ -1948,13 +1948,7 @@
       "epoch 1714: loss = 1720654.171533, a = 917.819508, b = 152.539338\n",
       "epoch 1715: loss = 1720650.292133, a = 917.880698, b = 152.539299\n",
       "epoch 1716: loss = 1720646.427958, a = 917.941766, b = 152.539260\n",
-      "epoch 1717: loss = 1720642.578948, a = 918.002711, b = 152.539221\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1717: loss = 1720642.578948, a = 918.002711, b = 152.539221\n",
       "epoch 1718: loss = 1720638.745042, a = 918.063534, b = 152.539182\n",
       "epoch 1719: loss = 1720634.926181, a = 918.124236, b = 152.539143\n",
       "epoch 1720: loss = 1720631.122305, a = 918.184817, b = 152.539104\n",
@@ -2202,13 +2196,7 @@
       "epoch 1962: loss = 1720037.716759, a = 929.792988, b = 152.531690\n",
       "epoch 1963: loss = 1720036.246549, a = 929.830235, b = 152.531667\n",
       "epoch 1964: loss = 1720034.782046, a = 929.867408, b = 152.531643\n",
-      "epoch 1965: loss = 1720033.323226, a = 929.904506, b = 152.531619\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1965: loss = 1720033.323226, a = 929.904506, b = 152.531619\n",
       "epoch 1966: loss = 1720031.870069, a = 929.941529, b = 152.531596\n",
       "epoch 1967: loss = 1720030.422552, a = 929.978479, b = 152.531572\n",
       "epoch 1968: loss = 1720028.980651, a = 930.015355, b = 152.531548\n",
@@ -2285,7 +2273,13 @@
       "epoch 2039: loss = 1719939.679051, a = 932.453563, b = 152.529991\n",
       "epoch 2040: loss = 1719938.588590, a = 932.485490, b = 152.529971\n",
       "epoch 2041: loss = 1719937.502342, a = 932.517352, b = 152.529950\n",
-      "epoch 2042: loss = 1719936.420291, a = 932.549151, b = 152.529930\n",
+      "epoch 2042: loss = 1719936.420291, a = 932.549151, b = 152.529930\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2043: loss = 1719935.342420, a = 932.580887, b = 152.529910\n",
       "epoch 2044: loss = 1719934.268714, a = 932.612559, b = 152.529890\n",
       "epoch 2045: loss = 1719933.199154, a = 932.644167, b = 152.529869\n",
@@ -2562,13 +2556,7 @@
       "epoch 2316: loss = 1719753.548720, a = 939.248962, b = 152.525651\n",
       "epoch 2317: loss = 1719753.171404, a = 939.267300, b = 152.525639\n",
       "epoch 2318: loss = 1719752.795512, a = 939.285601, b = 152.525627\n",
-      "epoch 2319: loss = 1719752.421041, a = 939.303866, b = 152.525616\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2319: loss = 1719752.421041, a = 939.303866, b = 152.525616\n",
       "epoch 2320: loss = 1719752.047983, a = 939.322094, b = 152.525604\n",
       "epoch 2321: loss = 1719751.676335, a = 939.340286, b = 152.525593\n",
       "epoch 2322: loss = 1719751.306089, a = 939.358441, b = 152.525581\n",
@@ -2706,7 +2694,13 @@
       "epoch 2454: loss = 1719712.865738, a = 941.462375, b = 152.524237\n",
       "epoch 2455: loss = 1719712.641027, a = 941.476287, b = 152.524228\n",
       "epoch 2456: loss = 1719712.417151, a = 941.490171, b = 152.524219\n",
-      "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n",
+      "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2458: loss = 1719711.971892, a = 941.517856, b = 152.524202\n",
       "epoch 2459: loss = 1719711.750502, a = 941.531657, b = 152.524193\n",
       "epoch 2460: loss = 1719711.529935, a = 941.545430, b = 152.524184\n",
@@ -3213,13 +3207,7 @@
       "epoch 2961: loss = 1719660.837327, a = 945.897818, b = 152.521404\n",
       "epoch 2962: loss = 1719660.800573, a = 945.902860, b = 152.521401\n",
       "epoch 2963: loss = 1719660.763943, a = 945.907892, b = 152.521398\n",
-      "epoch 2964: loss = 1719660.727437, a = 945.912915, b = 152.521395\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2964: loss = 1719660.727437, a = 945.912915, b = 152.521395\n",
       "epoch 2965: loss = 1719660.691055, a = 945.917927, b = 152.521391\n",
       "epoch 2966: loss = 1719660.654795, a = 945.922929, b = 152.521388\n",
       "epoch 2967: loss = 1719660.618658, a = 945.927922, b = 152.521385\n",
@@ -3305,7 +3293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -4091,799 +4079,7 @@
     {
      "data": {
       "text/html": [
-       "<img src=\"data:image/png;base64,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\" width=\"432\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"data:image/png;base64,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\" width=\"432\">"
+       "<img src=\"data:image/png;base64,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\" width=\"432\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/README.md b/README.md
index 25dfadb..4d2237d 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,9 @@
 
 本notebook教程包含了一些使用Python来学习机器学习的教程，通过本教程能够引导学习Python的基础知识和机器学习的背景和实际编程。
 
+由于本课程需要大量的编程练习才能取得比较好的学习效果，因此需要认真把作业和报告完成。作业的地址是：https://gitee.com/machinelearning2018/pr_homework 请按照里面的说明进行操作。
+
+
 ## 内容
 1. [Python基础](0_python/)
 2. [numpy & matplotlib](0_numpy_matplotlib_scipy_sympy/)
@@ -13,6 +16,6 @@
 8. PyTorch
 
 ## 其他参考
-* [安装Python环境](InstallPython.md)
+* [安装Python环境](tips/InstallPython.md)
 * [参考资料等](References.md)
-* [confusion matrix](metric/confusion_matrix.ipynb)
\ No newline at end of file
+* [confusion matrix](tips/confusion_matrix.ipynb)
\ No newline at end of file
diff --git a/References_notes.md b/References_notes.md
deleted file mode 100644
index ddf01ad..0000000
--- a/References_notes.md
+++ /dev/null
@@ -1,49 +0,0 @@
-## Notebooks:
-
-machineLearning/10_digits_classification.ipynb
-
-MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
-
-MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb
-
-
-## Exercise
-http://sofasofa.io/competitions.php?type=practice
-https://www.kaggle.com/competitions
-
-
-Titanic
-notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb
-
-
-## Method
-
-Programming Multiclass Logistic Regression
-http://localhost:8889/notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
-
-Equation for MLP
-http://localhost:8889/notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb
-
-Optimization methods
-http://localhost:8889/notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb
-
-
-https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md
-
-evaluation metrics
-http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb
-
-
-model selection and assessment
-http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb
-
-
-NN
-神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317
-零基础入门深度学习(3) - 神经网络和反向传播算法 https://www.zybuluo.com/hanbingtao/note/476663
-如何直观地解释 backpropagation 算法？ https://www.zhihu.com/question/27239198
-一文弄懂神经网络中的反向传播法——BackPropagation https://www.cnblogs.com/charlotte77/p/5629865.html
-
-https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf
-https://enlight.nyc/projects/neural-network/
-https://www.python-course.eu/neural_networks_with_python_numpy.php
\ No newline at end of file
diff --git a/nn/mlp_bp.ipynb b/nn/mlp_bp.ipynb
index 6385ea9..31189b4 100644
--- a/nn/mlp_bp.ipynb
+++ b/nn/mlp_bp.ipynb
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -297,6 +297,11 @@
     "np.random.seed(0)\n",
     "X, y = datasets.make_moons(200, noise=0.20)\n",
     "\n",
+    "# generate nn output target\n",
+    "t = np.zeros((X.shape[0], 2))\n",
+    "t[np.where(y==0), 0] = 1\n",
+    "t[np.where(y==1), 1] = 1\n",
+    "\n",
     "# plot data\n",
     "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n",
     "plt.show()"
@@ -304,223 +309,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
-      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      "[[0.64542838 0.53253913]\n",
-      " [0.69587439 0.42127568]\n",
-      " [0.6467884  0.5194472 ]\n",
-      " [0.66961109 0.4603445 ]\n",
-      " [0.68655514 0.45194099]\n",
-      " [0.68403036 0.46245535]\n",
-      " [0.65214419 0.52105738]\n",
-      " [0.68892948 0.43762808]\n",
-      " [0.65145727 0.5206233 ]\n",
-      " [0.69530054 0.42184819]\n",
-      " [0.63375592 0.54536443]\n",
-      " [0.67734017 0.45826309]\n",
-      " [0.68873965 0.44569752]\n",
-      " [0.68078977 0.46324812]\n",
-      " [0.62047762 0.57572069]\n",
-      " [0.66201234 0.50066075]\n",
-      " [0.62652672 0.57428354]\n",
-      " [0.67509297 0.46349122]\n",
-      " [0.62805646 0.56459649]\n",
-      " [0.6152309  0.59291485]\n",
-      " [0.68078014 0.47463854]\n",
-      " [0.68748493 0.441169  ]\n",
-      " [0.62239908 0.58064425]\n",
-      " [0.67780698 0.46845549]\n",
-      " [0.61527952 0.59674834]\n",
-      " [0.65301299 0.51009919]\n",
-      " [0.68376885 0.46380142]\n",
-      " [0.67950967 0.47280018]\n",
-      " [0.65645741 0.50068735]\n",
-      " [0.63190383 0.55322732]\n",
-      " [0.64037549 0.54669781]\n",
-      " [0.63787445 0.53626206]\n",
-      " [0.68467178 0.44815997]\n",
-      " [0.64570275 0.52307364]\n",
-      " [0.63344061 0.55925768]\n",
-      " [0.66650866 0.50490272]\n",
-      " [0.66778592 0.48305992]\n",
-      " [0.66821283 0.4855335 ]\n",
-      " [0.62708697 0.55849838]\n",
-      " [0.63539954 0.54308505]\n",
-      " [0.6904015  0.43232959]\n",
-      " [0.62327583 0.57132177]\n",
-      " [0.63599942 0.5506892 ]\n",
-      " [0.6639626  0.48714273]\n",
-      " [0.68848035 0.44364042]\n",
-      " [0.61757474 0.58765271]\n",
-      " [0.67112162 0.48276929]\n",
-      " [0.63035903 0.55087898]\n",
-      " [0.67762199 0.45834684]\n",
-      " [0.68731172 0.44302289]\n",
-      " [0.62012115 0.57656213]\n",
-      " [0.67576847 0.46515467]\n",
-      " [0.68195096 0.47546646]\n",
-      " [0.65345847 0.50121404]\n",
-      " [0.6912795  0.43349233]\n",
-      " [0.64241185 0.54058069]\n",
-      " [0.64086261 0.53843287]\n",
-      " [0.69231373 0.42931476]\n",
-      " [0.62351095 0.57425031]\n",
-      " [0.61877032 0.58419728]\n",
-      " [0.66997231 0.47376432]\n",
-      " [0.63513368 0.53712308]\n",
-      " [0.68923874 0.44208853]\n",
-      " [0.65058673 0.52584242]\n",
-      " [0.68680992 0.44426858]\n",
-      " [0.63646722 0.53146383]\n",
-      " [0.61773704 0.58747212]\n",
-      " [0.68683432 0.44921554]\n",
-      " [0.62428515 0.5741634 ]\n",
-      " [0.6265398  0.55867386]\n",
-      " [0.68940031 0.43474757]\n",
-      " [0.62197316 0.57076654]\n",
-      " [0.68821242 0.43274634]\n",
-      " [0.62632075 0.56167933]\n",
-      " [0.67996616 0.45840974]\n",
-      " [0.62840877 0.55422297]\n",
-      " [0.65735698 0.50007275]\n",
-      " [0.653575   0.51892012]\n",
-      " [0.62728417 0.56933143]\n",
-      " [0.6722307  0.47508204]\n",
-      " [0.67766291 0.48462976]\n",
-      " [0.62045451 0.5777636 ]\n",
-      " [0.69462011 0.42118748]\n",
-      " [0.65379768 0.54029292]\n",
-      " [0.65015355 0.51394856]\n",
-      " [0.61399683 0.60063388]\n",
-      " [0.66967155 0.48178393]\n",
-      " [0.61883545 0.58214799]\n",
-      " [0.68330641 0.44954534]\n",
-      " [0.66958159 0.50147962]\n",
-      " [0.63123528 0.56360973]\n",
-      " [0.64975211 0.52940887]\n",
-      " [0.6791724  0.47711072]\n",
-      " [0.62127769 0.57407004]\n",
-      " [0.67138342 0.49887158]\n",
-      " [0.68166774 0.46593973]\n",
-      " [0.68914381 0.44418348]\n",
-      " [0.6814121  0.45406161]\n",
-      " [0.63320629 0.54115769]\n",
-      " [0.66525169 0.483365  ]\n",
-      " [0.70113207 0.4021057 ]\n",
-      " [0.66042727 0.5239104 ]\n",
-      " [0.6158391  0.5984706 ]\n",
-      " [0.67563006 0.47185796]\n",
-      " [0.62405364 0.56675149]\n",
-      " [0.6341422  0.54968736]\n",
-      " [0.65033621 0.50399789]\n",
-      " [0.61560564 0.59223984]\n",
-      " [0.68851143 0.44424884]\n",
-      " [0.63032714 0.55790845]\n",
-      " [0.62373144 0.56780916]\n",
-      " [0.6190955  0.58654498]\n",
-      " [0.61479149 0.59778757]\n",
-      " [0.64013597 0.52883008]\n",
-      " [0.65526041 0.51466915]\n",
-      " [0.69730956 0.41614571]\n",
-      " [0.63243373 0.55370584]\n",
-      " [0.67098643 0.49947521]\n",
-      " [0.68511739 0.44534543]\n",
-      " [0.65313156 0.51375309]\n",
-      " [0.63300568 0.56124219]\n",
-      " [0.63605962 0.539493  ]\n",
-      " [0.64836686 0.51628529]\n",
-      " [0.62139293 0.58008798]\n",
-      " [0.64932898 0.51333776]\n",
-      " [0.6235741  0.56673449]\n",
-      " [0.67305114 0.47308517]\n",
-      " [0.65700846 0.50320471]\n",
-      " [0.68682776 0.44420681]\n",
-      " [0.65349271 0.53822609]\n",
-      " [0.64658026 0.53509776]\n",
-      " [0.63130791 0.54998535]\n",
-      " [0.62871243 0.55143978]\n",
-      " [0.67641457 0.46208977]\n",
-      " [0.68083831 0.4618562 ]\n",
-      " [0.67051156 0.48964901]\n",
-      " [0.67839015 0.46427427]\n",
-      " [0.64545239 0.51292276]\n",
-      " [0.6606294  0.52549281]\n",
-      " [0.64222193 0.53932354]\n",
-      " [0.65220627 0.50861605]\n",
-      " [0.63349683 0.54876668]\n",
-      " [0.62113983 0.58530881]\n",
-      " [0.61760612 0.58478278]\n",
-      " [0.62154618 0.57528528]\n",
-      " [0.66276585 0.49998043]\n",
-      " [0.64789962 0.52213533]\n",
-      " [0.69171714 0.43236179]\n",
-      " [0.61884282 0.59008273]\n",
-      " [0.66202646 0.48904737]\n",
-      " [0.68505323 0.44422315]\n",
-      " [0.69703152 0.41719461]\n",
-      " [0.65463078 0.51385956]\n",
-      " [0.62727924 0.56710318]\n",
-      " [0.68234907 0.45343016]\n",
-      " [0.62152597 0.57502776]\n",
-      " [0.69106941 0.43467972]\n",
-      " [0.6577279  0.50070683]\n",
-      " [0.62167217 0.58763578]\n",
-      " [0.6319471  0.56142611]\n",
-      " [0.66526845 0.48615861]\n",
-      " [0.69239083 0.43076428]\n",
-      " [0.6302144  0.56671889]\n",
-      " [0.68269858 0.45823332]\n",
-      " [0.69473345 0.42048606]\n",
-      " [0.66914479 0.49183979]\n",
-      " [0.62590343 0.57079016]\n",
-      " [0.67882189 0.45883859]\n",
-      " [0.69099277 0.43617846]\n",
-      " [0.69857324 0.4111444 ]\n",
-      " [0.6257689  0.57580567]\n",
-      " [0.64428699 0.51370314]\n",
-      " [0.62812104 0.5542497 ]\n",
-      " [0.64162491 0.52666474]\n",
-      " [0.66370428 0.48858184]\n",
-      " [0.68245629 0.45374376]\n",
-      " [0.6439251  0.54817104]\n",
-      " [0.63229486 0.54634744]\n",
-      " [0.63368004 0.54360187]\n",
-      " [0.62920379 0.56252525]\n",
-      " [0.68802781 0.44443285]\n",
-      " [0.63367224 0.54096919]\n",
-      " [0.66908967 0.50101927]\n",
-      " [0.69333237 0.43288566]\n",
-      " [0.66294924 0.5164213 ]\n",
-      " [0.62071028 0.58516989]\n",
-      " [0.63089179 0.55649663]\n",
-      " [0.6960798  0.41867759]\n",
-      " [0.63428896 0.54164095]\n",
-      " [0.62316292 0.56879113]\n",
-      " [0.63333752 0.54240036]\n",
-      " [0.62334574 0.57923263]\n",
-      " [0.65510668 0.51380363]\n",
-      " [0.65758931 0.50857068]\n",
-      " [0.64630815 0.52118855]\n",
-      " [0.62065332 0.5763107 ]\n",
-      " [0.67037756 0.49168361]\n",
-      " [0.66297331 0.4903177 ]\n",
-      " [0.62110186 0.58268245]\n",
-      " [0.68025074 0.46575819]]\n"
-     ]
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -532,7 +334,10 @@
     "nn = NN_Model()\n",
     "nn.n_input_dim = X.shape[1]      # input size\n",
     "nn.n_output_dim = 2              # output node size\n",
-    "nn.n_hide_dim = 3                # hidden node size\n",
+    "nn.n_hide_dim = 4                # hidden node size\n",
+    "\n",
+    "nn.X = X\n",
+    "nn.y = y\n",
     "\n",
     "# initial weight array\n",
     "nn.W1 = np.random.randn(nn.n_input_dim, nn.n_hide_dim) / np.sqrt(nn.n_input_dim)\n",
@@ -554,13 +359,2289 @@
     "    n.z2 = sigmod(n.z1.dot(n.W2) + n.b2)\n",
     "    return n\n",
     "\n",
+    "\n",
     "# use random weight to perdict\n",
     "forward(nn, X)\n",
-    "y = nn.z2[:, 0]>nn.z2[:,1]\n",
     "y_pred = np.zeros(nn.z2.shape[0])\n",
     "y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1\n",
-    "print(y_pred)\n",
-    "print(nn.z2)"
+    "\n",
+    "# plot data\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch [   0] L = 103.723994, acc = 0.500000\n",
+      "epoch [   1] L = 99.572368, acc = 0.500000\n",
+      "epoch [   2] L = 96.298444, acc = 0.695000\n",
+      "epoch [   3] L = 93.755262, acc = 0.745000\n",
+      "epoch [   4] L = 91.723534, acc = 0.725000\n",
+      "epoch [   5] L = 90.013431, acc = 0.710000\n",
+      "epoch [   6] L = 88.493838, acc = 0.720000\n",
+      "epoch [   7] L = 87.084077, acc = 0.740000\n",
+      "epoch [   8] L = 85.737551, acc = 0.755000\n",
+      "epoch [   9] L = 84.428420, acc = 0.760000\n",
+      "epoch [  10] L = 83.142909, acc = 0.770000\n",
+      "epoch [  11] L = 81.874127, acc = 0.775000\n",
+      "epoch [  12] L = 80.619101, acc = 0.775000\n",
+      "epoch [  13] L = 79.377104, acc = 0.775000\n",
+      "epoch [  14] L = 78.148705, acc = 0.775000\n",
+      "epoch [  15] L = 76.935216, acc = 0.785000\n",
+      "epoch [  16] L = 75.738363, acc = 0.790000\n",
+      "epoch [  17] L = 74.560076, acc = 0.795000\n",
+      "epoch [  18] L = 73.402344, acc = 0.795000\n",
+      "epoch [  19] L = 72.267120, acc = 0.800000\n",
+      "epoch [  20] L = 71.156245, acc = 0.805000\n",
+      "epoch [  21] L = 70.071400, acc = 0.805000\n",
+      "epoch [  22] L = 69.014062, acc = 0.810000\n",
+      "epoch [  23] L = 67.985488, acc = 0.810000\n",
+      "epoch [  24] L = 66.986694, acc = 0.810000\n",
+      "epoch [  25] L = 66.018452, acc = 0.810000\n",
+      "epoch [  26] L = 65.081296, acc = 0.810000\n",
+      "epoch [  27] L = 64.175529, acc = 0.810000\n",
+      "epoch [  28] L = 63.301240, acc = 0.810000\n",
+      "epoch [  29] L = 62.458318, acc = 0.810000\n",
+      "epoch [  30] L = 61.646476, acc = 0.810000\n",
+      "epoch [  31] L = 60.865270, acc = 0.810000\n",
+      "epoch [  32] L = 60.114123, acc = 0.810000\n",
+      "epoch [  33] L = 59.392345, acc = 0.815000\n",
+      "epoch [  34] L = 58.699157, acc = 0.815000\n",
+      "epoch [  35] L = 58.033705, acc = 0.815000\n",
+      "epoch [  36] L = 57.395081, acc = 0.815000\n",
+      "epoch [  37] L = 56.782341, acc = 0.815000\n",
+      "epoch [  38] L = 56.194515, acc = 0.815000\n",
+      "epoch [  39] L = 55.630622, acc = 0.815000\n",
+      "epoch [  40] L = 55.089680, acc = 0.815000\n",
+      "epoch [  41] L = 54.570712, acc = 0.825000\n",
+      "epoch [  42] L = 54.072759, acc = 0.820000\n",
+      "epoch [  43] L = 53.594882, acc = 0.820000\n",
+      "epoch [  44] L = 53.136167, acc = 0.825000\n",
+      "epoch [  45] L = 52.695728, acc = 0.825000\n",
+      "epoch [  46] L = 52.272712, acc = 0.825000\n",
+      "epoch [  47] L = 51.866299, acc = 0.825000\n",
+      "epoch [  48] L = 51.475703, acc = 0.825000\n",
+      "epoch [  49] L = 51.100173, acc = 0.825000\n",
+      "epoch [  50] L = 50.738994, acc = 0.830000\n",
+      "epoch [  51] L = 50.391484, acc = 0.830000\n",
+      "epoch [  52] L = 50.056996, acc = 0.830000\n",
+      "epoch [  53] L = 49.734917, acc = 0.830000\n",
+      "epoch [  54] L = 49.424664, acc = 0.830000\n",
+      "epoch [  55] L = 49.125686, acc = 0.830000\n",
+      "epoch [  56] L = 48.837462, acc = 0.830000\n",
+      "epoch [  57] L = 48.559498, acc = 0.830000\n",
+      "epoch [  58] L = 48.291329, acc = 0.830000\n",
+      "epoch [  59] L = 48.032513, acc = 0.830000\n",
+      "epoch [  60] L = 47.782634, acc = 0.830000\n",
+      "epoch [  61] L = 47.541299, acc = 0.830000\n",
+      "epoch [  62] L = 47.308135, acc = 0.830000\n",
+      "epoch [  63] L = 47.082790, acc = 0.830000\n",
+      "epoch [  64] L = 46.864932, acc = 0.830000\n",
+      "epoch [  65] L = 46.654245, acc = 0.830000\n",
+      "epoch [  66] L = 46.450431, acc = 0.830000\n",
+      "epoch [  67] L = 46.253208, acc = 0.830000\n",
+      "epoch [  68] L = 46.062309, acc = 0.830000\n",
+      "epoch [  69] L = 45.877480, acc = 0.830000\n",
+      "epoch [  70] L = 45.698480, acc = 0.830000\n",
+      "epoch [  71] L = 45.525081, acc = 0.835000\n",
+      "epoch [  72] L = 45.357065, acc = 0.835000\n",
+      "epoch [  73] L = 45.194228, acc = 0.835000\n",
+      "epoch [  74] L = 45.036371, acc = 0.835000\n",
+      "epoch [  75] L = 44.883309, acc = 0.835000\n",
+      "epoch [  76] L = 44.734863, acc = 0.840000\n",
+      "epoch [  77] L = 44.590864, acc = 0.840000\n",
+      "epoch [  78] L = 44.451150, acc = 0.835000\n",
+      "epoch [  79] L = 44.315567, acc = 0.835000\n",
+      "epoch [  80] L = 44.183966, acc = 0.835000\n",
+      "epoch [  81] L = 44.056207, acc = 0.835000\n",
+      "epoch [  82] L = 43.932155, acc = 0.835000\n",
+      "epoch [  83] L = 43.811681, acc = 0.835000\n",
+      "epoch [  84] L = 43.694661, acc = 0.835000\n",
+      "epoch [  85] L = 43.580977, acc = 0.835000\n",
+      "epoch [  86] L = 43.470515, acc = 0.835000\n",
+      "epoch [  87] L = 43.363166, acc = 0.835000\n",
+      "epoch [  88] L = 43.258826, acc = 0.835000\n",
+      "epoch [  89] L = 43.157394, acc = 0.835000\n",
+      "epoch [  90] L = 43.058774, acc = 0.835000\n",
+      "epoch [  91] L = 42.962874, acc = 0.835000\n",
+      "epoch [  92] L = 42.869604, acc = 0.835000\n",
+      "epoch [  93] L = 42.778879, acc = 0.835000\n",
+      "epoch [  94] L = 42.690616, acc = 0.835000\n",
+      "epoch [  95] L = 42.604736, acc = 0.835000\n",
+      "epoch [  96] L = 42.521163, acc = 0.835000\n",
+      "epoch [  97] L = 42.439823, acc = 0.830000\n",
+      "epoch [  98] L = 42.360646, acc = 0.830000\n",
+      "epoch [  99] L = 42.283563, acc = 0.830000\n",
+      "epoch [ 100] L = 42.208509, acc = 0.830000\n",
+      "epoch [ 101] L = 42.135420, acc = 0.830000\n",
+      "epoch [ 102] L = 42.064236, acc = 0.830000\n",
+      "epoch [ 103] L = 41.994896, acc = 0.830000\n",
+      "epoch [ 104] L = 41.927345, acc = 0.830000\n",
+      "epoch [ 105] L = 41.861528, acc = 0.830000\n",
+      "epoch [ 106] L = 41.797391, acc = 0.835000\n",
+      "epoch [ 107] L = 41.734884, acc = 0.835000\n",
+      "epoch [ 108] L = 41.673958, acc = 0.835000\n",
+      "epoch [ 109] L = 41.614563, acc = 0.835000\n",
+      "epoch [ 110] L = 41.556656, acc = 0.835000\n",
+      "epoch [ 111] L = 41.500191, acc = 0.835000\n",
+      "epoch [ 112] L = 41.445126, acc = 0.835000\n",
+      "epoch [ 113] L = 41.391418, acc = 0.835000\n",
+      "epoch [ 114] L = 41.339029, acc = 0.835000\n",
+      "epoch [ 115] L = 41.287919, acc = 0.835000\n",
+      "epoch [ 116] L = 41.238051, acc = 0.835000\n",
+      "epoch [ 117] L = 41.189388, acc = 0.835000\n",
+      "epoch [ 118] L = 41.141897, acc = 0.835000\n",
+      "epoch [ 119] L = 41.095542, acc = 0.835000\n",
+      "epoch [ 120] L = 41.050292, acc = 0.840000\n",
+      "epoch [ 121] L = 41.006114, acc = 0.840000\n",
+      "epoch [ 122] L = 40.962979, acc = 0.840000\n",
+      "epoch [ 123] L = 40.920857, acc = 0.840000\n",
+      "epoch [ 124] L = 40.879718, acc = 0.840000\n",
+      "epoch [ 125] L = 40.839536, acc = 0.840000\n",
+      "epoch [ 126] L = 40.800284, acc = 0.840000\n",
+      "epoch [ 127] L = 40.761935, acc = 0.845000\n",
+      "epoch [ 128] L = 40.724464, acc = 0.845000\n",
+      "epoch [ 129] L = 40.687848, acc = 0.845000\n",
+      "epoch [ 130] L = 40.652063, acc = 0.845000\n",
+      "epoch [ 131] L = 40.617086, acc = 0.845000\n",
+      "epoch [ 132] L = 40.582895, acc = 0.850000\n",
+      "epoch [ 133] L = 40.549468, acc = 0.850000\n",
+      "epoch [ 134] L = 40.516785, acc = 0.850000\n",
+      "epoch [ 135] L = 40.484827, acc = 0.850000\n",
+      "epoch [ 136] L = 40.453572, acc = 0.850000\n",
+      "epoch [ 137] L = 40.423004, acc = 0.850000\n",
+      "epoch [ 138] L = 40.393102, acc = 0.850000\n",
+      "epoch [ 139] L = 40.363851, acc = 0.850000\n",
+      "epoch [ 140] L = 40.335232, acc = 0.850000\n",
+      "epoch [ 141] L = 40.307229, acc = 0.850000\n",
+      "epoch [ 142] L = 40.279826, acc = 0.850000\n",
+      "epoch [ 143] L = 40.253008, acc = 0.850000\n",
+      "epoch [ 144] L = 40.226759, acc = 0.850000\n",
+      "epoch [ 145] L = 40.201064, acc = 0.850000\n",
+      "epoch [ 146] L = 40.175909, acc = 0.850000\n",
+      "epoch [ 147] L = 40.151281, acc = 0.850000\n",
+      "epoch [ 148] L = 40.127167, acc = 0.850000\n",
+      "epoch [ 149] L = 40.103552, acc = 0.850000\n",
+      "epoch [ 150] L = 40.080424, acc = 0.850000\n",
+      "epoch [ 151] L = 40.057772, acc = 0.850000\n",
+      "epoch [ 152] L = 40.035583, acc = 0.850000\n",
+      "epoch [ 153] L = 40.013846, acc = 0.850000\n",
+      "epoch [ 154] L = 39.992550, acc = 0.850000\n",
+      "epoch [ 155] L = 39.971683, acc = 0.850000\n",
+      "epoch [ 156] L = 39.951235, acc = 0.855000\n",
+      "epoch [ 157] L = 39.931196, acc = 0.855000\n",
+      "epoch [ 158] L = 39.911556, acc = 0.855000\n",
+      "epoch [ 159] L = 39.892306, acc = 0.855000\n",
+      "epoch [ 160] L = 39.873435, acc = 0.855000\n",
+      "epoch [ 161] L = 39.854934, acc = 0.855000\n",
+      "epoch [ 162] L = 39.836796, acc = 0.855000\n",
+      "epoch [ 163] L = 39.819011, acc = 0.855000\n",
+      "epoch [ 164] L = 39.801570, acc = 0.855000\n",
+      "epoch [ 165] L = 39.784466, acc = 0.855000\n",
+      "epoch [ 166] L = 39.767691, acc = 0.855000\n",
+      "epoch [ 167] L = 39.751236, acc = 0.855000\n",
+      "epoch [ 168] L = 39.735095, acc = 0.855000\n",
+      "epoch [ 169] L = 39.719261, acc = 0.855000\n",
+      "epoch [ 170] L = 39.703725, acc = 0.855000\n",
+      "epoch [ 171] L = 39.688481, acc = 0.855000\n",
+      "epoch [ 172] L = 39.673523, acc = 0.855000\n",
+      "epoch [ 173] L = 39.658844, acc = 0.855000\n",
+      "epoch [ 174] L = 39.644437, acc = 0.855000\n",
+      "epoch [ 175] L = 39.630297, acc = 0.855000\n",
+      "epoch [ 176] L = 39.616417, acc = 0.855000\n",
+      "epoch [ 177] L = 39.602791, acc = 0.855000\n",
+      "epoch [ 178] L = 39.589414, acc = 0.855000\n",
+      "epoch [ 179] L = 39.576281, acc = 0.855000\n",
+      "epoch [ 180] L = 39.563385, acc = 0.855000\n",
+      "epoch [ 181] L = 39.550721, acc = 0.855000\n",
+      "epoch [ 182] L = 39.538285, acc = 0.855000\n",
+      "epoch [ 183] L = 39.526072, acc = 0.855000\n",
+      "epoch [ 184] L = 39.514076, acc = 0.855000\n",
+      "epoch [ 185] L = 39.502292, acc = 0.855000\n",
+      "epoch [ 186] L = 39.490717, acc = 0.855000\n",
+      "epoch [ 187] L = 39.479346, acc = 0.855000\n",
+      "epoch [ 188] L = 39.468173, acc = 0.855000\n",
+      "epoch [ 189] L = 39.457196, acc = 0.855000\n",
+      "epoch [ 190] L = 39.446409, acc = 0.855000\n",
+      "epoch [ 191] L = 39.435809, acc = 0.855000\n",
+      "epoch [ 192] L = 39.425392, acc = 0.855000\n",
+      "epoch [ 193] L = 39.415154, acc = 0.855000\n",
+      "epoch [ 194] L = 39.405091, acc = 0.855000\n",
+      "epoch [ 195] L = 39.395199, acc = 0.855000\n",
+      "epoch [ 196] L = 39.385476, acc = 0.855000\n",
+      "epoch [ 197] L = 39.375916, acc = 0.855000\n",
+      "epoch [ 198] L = 39.366518, acc = 0.855000\n",
+      "epoch [ 199] L = 39.357277, acc = 0.855000\n",
+      "epoch [ 200] L = 39.348190, acc = 0.855000\n",
+      "epoch [ 201] L = 39.339255, acc = 0.855000\n",
+      "epoch [ 202] L = 39.330468, acc = 0.855000\n",
+      "epoch [ 203] L = 39.321826, acc = 0.855000\n",
+      "epoch [ 204] L = 39.313326, acc = 0.855000\n",
+      "epoch [ 205] L = 39.304965, acc = 0.855000\n",
+      "epoch [ 206] L = 39.296741, acc = 0.855000\n",
+      "epoch [ 207] L = 39.288650, acc = 0.855000\n",
+      "epoch [ 208] L = 39.280691, acc = 0.855000\n",
+      "epoch [ 209] L = 39.272860, acc = 0.855000\n",
+      "epoch [ 210] L = 39.265155, acc = 0.855000\n",
+      "epoch [ 211] L = 39.257573, acc = 0.855000\n",
+      "epoch [ 212] L = 39.250112, acc = 0.855000\n",
+      "epoch [ 213] L = 39.242770, acc = 0.855000\n",
+      "epoch [ 214] L = 39.235544, acc = 0.855000\n",
+      "epoch [ 215] L = 39.228431, acc = 0.855000\n",
+      "epoch [ 216] L = 39.221431, acc = 0.855000\n",
+      "epoch [ 217] L = 39.214540, acc = 0.855000\n",
+      "epoch [ 218] L = 39.207757, acc = 0.855000\n",
+      "epoch [ 219] L = 39.201079, acc = 0.855000\n",
+      "epoch [ 220] L = 39.194505, acc = 0.855000\n",
+      "epoch [ 221] L = 39.188032, acc = 0.855000\n",
+      "epoch [ 222] L = 39.181658, acc = 0.855000\n",
+      "epoch [ 223] L = 39.175382, acc = 0.855000\n",
+      "epoch [ 224] L = 39.169201, acc = 0.855000\n",
+      "epoch [ 225] L = 39.163115, acc = 0.855000\n",
+      "epoch [ 226] L = 39.157121, acc = 0.855000\n",
+      "epoch [ 227] L = 39.151217, acc = 0.855000\n",
+      "epoch [ 228] L = 39.145402, acc = 0.855000\n",
+      "epoch [ 229] L = 39.139673, acc = 0.855000\n",
+      "epoch [ 230] L = 39.134031, acc = 0.855000\n",
+      "epoch [ 231] L = 39.128472, acc = 0.855000\n",
+      "epoch [ 232] L = 39.122995, acc = 0.855000\n",
+      "epoch [ 233] L = 39.117600, acc = 0.855000\n",
+      "epoch [ 234] L = 39.112283, acc = 0.855000\n",
+      "epoch [ 235] L = 39.107045, acc = 0.855000\n",
+      "epoch [ 236] L = 39.101883, acc = 0.855000\n",
+      "epoch [ 237] L = 39.096796, acc = 0.855000\n",
+      "epoch [ 238] L = 39.091783, acc = 0.855000\n",
+      "epoch [ 239] L = 39.086842, acc = 0.855000\n",
+      "epoch [ 240] L = 39.081972, acc = 0.855000\n",
+      "epoch [ 241] L = 39.077172, acc = 0.855000\n",
+      "epoch [ 242] L = 39.072441, acc = 0.855000\n",
+      "epoch [ 243] L = 39.067776, acc = 0.855000\n",
+      "epoch [ 244] L = 39.063178, acc = 0.855000\n",
+      "epoch [ 245] L = 39.058645, acc = 0.855000\n",
+      "epoch [ 246] L = 39.054176, acc = 0.855000\n",
+      "epoch [ 247] L = 39.049770, acc = 0.855000\n",
+      "epoch [ 248] L = 39.045425, acc = 0.855000\n",
+      "epoch [ 249] L = 39.041140, acc = 0.855000\n",
+      "epoch [ 250] L = 39.036915, acc = 0.855000\n",
+      "epoch [ 251] L = 39.032748, acc = 0.855000\n",
+      "epoch [ 252] L = 39.028639, acc = 0.855000\n",
+      "epoch [ 253] L = 39.024586, acc = 0.855000\n",
+      "epoch [ 254] L = 39.020589, acc = 0.850000\n",
+      "epoch [ 255] L = 39.016646, acc = 0.850000\n",
+      "epoch [ 256] L = 39.012756, acc = 0.850000\n",
+      "epoch [ 257] L = 39.008920, acc = 0.850000\n",
+      "epoch [ 258] L = 39.005134, acc = 0.850000\n",
+      "epoch [ 259] L = 39.001400, acc = 0.850000\n",
+      "epoch [ 260] L = 38.997716, acc = 0.850000\n",
+      "epoch [ 261] L = 38.994081, acc = 0.850000\n",
+      "epoch [ 262] L = 38.990494, acc = 0.850000\n",
+      "epoch [ 263] L = 38.986955, acc = 0.850000\n",
+      "epoch [ 264] L = 38.983462, acc = 0.845000\n",
+      "epoch [ 265] L = 38.980015, acc = 0.845000\n",
+      "epoch [ 266] L = 38.976614, acc = 0.845000\n",
+      "epoch [ 267] L = 38.973256, acc = 0.845000\n",
+      "epoch [ 268] L = 38.969943, acc = 0.845000\n",
+      "epoch [ 269] L = 38.966672, acc = 0.845000\n",
+      "epoch [ 270] L = 38.963444, acc = 0.845000\n",
+      "epoch [ 271] L = 38.960257, acc = 0.845000\n",
+      "epoch [ 272] L = 38.957111, acc = 0.845000\n",
+      "epoch [ 273] L = 38.954005, acc = 0.845000\n",
+      "epoch [ 274] L = 38.950939, acc = 0.845000\n",
+      "epoch [ 275] L = 38.947911, acc = 0.845000\n",
+      "epoch [ 276] L = 38.944922, acc = 0.845000\n",
+      "epoch [ 277] L = 38.941970, acc = 0.845000\n",
+      "epoch [ 278] L = 38.939056, acc = 0.845000\n",
+      "epoch [ 279] L = 38.936178, acc = 0.845000\n",
+      "epoch [ 280] L = 38.933335, acc = 0.845000\n",
+      "epoch [ 281] L = 38.930528, acc = 0.845000\n",
+      "epoch [ 282] L = 38.927756, acc = 0.845000\n",
+      "epoch [ 283] L = 38.925018, acc = 0.845000\n",
+      "epoch [ 284] L = 38.922313, acc = 0.845000\n",
+      "epoch [ 285] L = 38.919641, acc = 0.845000\n",
+      "epoch [ 286] L = 38.917002, acc = 0.845000\n",
+      "epoch [ 287] L = 38.914395, acc = 0.845000\n",
+      "epoch [ 288] L = 38.911820, acc = 0.845000\n",
+      "epoch [ 289] L = 38.909275, acc = 0.845000\n",
+      "epoch [ 290] L = 38.906761, acc = 0.845000\n",
+      "epoch [ 291] L = 38.904278, acc = 0.845000\n",
+      "epoch [ 292] L = 38.901823, acc = 0.845000\n",
+      "epoch [ 293] L = 38.899398, acc = 0.845000\n",
+      "epoch [ 294] L = 38.897002, acc = 0.845000\n",
+      "epoch [ 295] L = 38.894633, acc = 0.845000\n",
+      "epoch [ 296] L = 38.892293, acc = 0.845000\n",
+      "epoch [ 297] L = 38.889980, acc = 0.845000\n",
+      "epoch [ 298] L = 38.887694, acc = 0.845000\n",
+      "epoch [ 299] L = 38.885434, acc = 0.845000\n",
+      "epoch [ 300] L = 38.883201, acc = 0.845000\n",
+      "epoch [ 301] L = 38.880993, acc = 0.845000\n",
+      "epoch [ 302] L = 38.878811, acc = 0.845000\n",
+      "epoch [ 303] L = 38.876653, acc = 0.845000\n",
+      "epoch [ 304] L = 38.874521, acc = 0.845000\n",
+      "epoch [ 305] L = 38.872412, acc = 0.845000\n",
+      "epoch [ 306] L = 38.870327, acc = 0.845000\n",
+      "epoch [ 307] L = 38.868266, acc = 0.845000\n",
+      "epoch [ 308] L = 38.866228, acc = 0.845000\n",
+      "epoch [ 309] L = 38.864212, acc = 0.845000\n",
+      "epoch [ 310] L = 38.862219, acc = 0.845000\n",
+      "epoch [ 311] L = 38.860249, acc = 0.845000\n",
+      "epoch [ 312] L = 38.858300, acc = 0.845000\n",
+      "epoch [ 313] L = 38.856372, acc = 0.845000\n",
+      "epoch [ 314] L = 38.854466, acc = 0.845000\n",
+      "epoch [ 315] L = 38.852580, acc = 0.845000\n",
+      "epoch [ 316] L = 38.850715, acc = 0.845000\n",
+      "epoch [ 317] L = 38.848870, acc = 0.845000\n",
+      "epoch [ 318] L = 38.847045, acc = 0.845000\n",
+      "epoch [ 319] L = 38.845240, acc = 0.845000\n",
+      "epoch [ 320] L = 38.843454, acc = 0.845000\n",
+      "epoch [ 321] L = 38.841687, acc = 0.845000\n",
+      "epoch [ 322] L = 38.839939, acc = 0.845000\n",
+      "epoch [ 323] L = 38.838209, acc = 0.845000\n",
+      "epoch [ 324] L = 38.836498, acc = 0.845000\n",
+      "epoch [ 325] L = 38.834804, acc = 0.845000\n",
+      "epoch [ 326] L = 38.833128, acc = 0.845000\n",
+      "epoch [ 327] L = 38.831470, acc = 0.845000\n",
+      "epoch [ 328] L = 38.829829, acc = 0.845000\n",
+      "epoch [ 329] L = 38.828205, acc = 0.845000\n",
+      "epoch [ 330] L = 38.826598, acc = 0.845000\n",
+      "epoch [ 331] L = 38.825007, acc = 0.845000\n",
+      "epoch [ 332] L = 38.823432, acc = 0.845000\n",
+      "epoch [ 333] L = 38.821874, acc = 0.845000\n",
+      "epoch [ 334] L = 38.820331, acc = 0.845000\n",
+      "epoch [ 335] L = 38.818804, acc = 0.845000\n",
+      "epoch [ 336] L = 38.817292, acc = 0.845000\n",
+      "epoch [ 337] L = 38.815795, acc = 0.845000\n",
+      "epoch [ 338] L = 38.814314, acc = 0.845000\n",
+      "epoch [ 339] L = 38.812847, acc = 0.845000\n",
+      "epoch [ 340] L = 38.811394, acc = 0.845000\n",
+      "epoch [ 341] L = 38.809956, acc = 0.845000\n",
+      "epoch [ 342] L = 38.808532, acc = 0.845000\n",
+      "epoch [ 343] L = 38.807122, acc = 0.845000\n",
+      "epoch [ 344] L = 38.805725, acc = 0.845000\n",
+      "epoch [ 345] L = 38.804342, acc = 0.845000\n",
+      "epoch [ 346] L = 38.802972, acc = 0.845000\n",
+      "epoch [ 347] L = 38.801616, acc = 0.845000\n",
+      "epoch [ 348] L = 38.800273, acc = 0.845000\n",
+      "epoch [ 349] L = 38.798942, acc = 0.845000\n",
+      "epoch [ 350] L = 38.797624, acc = 0.845000\n",
+      "epoch [ 351] L = 38.796318, acc = 0.845000\n",
+      "epoch [ 352] L = 38.795025, acc = 0.845000\n",
+      "epoch [ 353] L = 38.793744, acc = 0.845000\n",
+      "epoch [ 354] L = 38.792475, acc = 0.845000\n",
+      "epoch [ 355] L = 38.791217, acc = 0.845000\n",
+      "epoch [ 356] L = 38.789971, acc = 0.845000\n",
+      "epoch [ 357] L = 38.788737, acc = 0.845000\n",
+      "epoch [ 358] L = 38.787514, acc = 0.845000\n",
+      "epoch [ 359] L = 38.786302, acc = 0.845000\n",
+      "epoch [ 360] L = 38.785101, acc = 0.845000\n",
+      "epoch [ 361] L = 38.783911, acc = 0.845000\n",
+      "epoch [ 362] L = 38.782732, acc = 0.845000\n",
+      "epoch [ 363] L = 38.781564, acc = 0.845000\n",
+      "epoch [ 364] L = 38.780405, acc = 0.845000\n",
+      "epoch [ 365] L = 38.779258, acc = 0.845000\n",
+      "epoch [ 366] L = 38.778120, acc = 0.845000\n",
+      "epoch [ 367] L = 38.776992, acc = 0.845000\n",
+      "epoch [ 368] L = 38.775874, acc = 0.845000\n",
+      "epoch [ 369] L = 38.774766, acc = 0.845000\n",
+      "epoch [ 370] L = 38.773668, acc = 0.845000\n",
+      "epoch [ 371] L = 38.772579, acc = 0.845000\n",
+      "epoch [ 372] L = 38.771500, acc = 0.845000\n",
+      "epoch [ 373] L = 38.770430, acc = 0.845000\n",
+      "epoch [ 374] L = 38.769369, acc = 0.845000\n",
+      "epoch [ 375] L = 38.768317, acc = 0.845000\n",
+      "epoch [ 376] L = 38.767273, acc = 0.845000\n",
+      "epoch [ 377] L = 38.766239, acc = 0.845000\n",
+      "epoch [ 378] L = 38.765214, acc = 0.845000\n",
+      "epoch [ 379] L = 38.764197, acc = 0.845000\n",
+      "epoch [ 380] L = 38.763188, acc = 0.845000\n",
+      "epoch [ 381] L = 38.762188, acc = 0.845000\n",
+      "epoch [ 382] L = 38.761196, acc = 0.845000\n",
+      "epoch [ 383] L = 38.760212, acc = 0.845000\n",
+      "epoch [ 384] L = 38.759236, acc = 0.845000\n",
+      "epoch [ 385] L = 38.758269, acc = 0.845000\n",
+      "epoch [ 386] L = 38.757309, acc = 0.845000\n",
+      "epoch [ 387] L = 38.756356, acc = 0.845000\n",
+      "epoch [ 388] L = 38.755412, acc = 0.845000\n",
+      "epoch [ 389] L = 38.754475, acc = 0.845000\n",
+      "epoch [ 390] L = 38.753545, acc = 0.845000\n",
+      "epoch [ 391] L = 38.752623, acc = 0.845000\n",
+      "epoch [ 392] L = 38.751708, acc = 0.845000\n",
+      "epoch [ 393] L = 38.750800, acc = 0.845000\n",
+      "epoch [ 394] L = 38.749899, acc = 0.845000\n",
+      "epoch [ 395] L = 38.749006, acc = 0.845000\n",
+      "epoch [ 396] L = 38.748119, acc = 0.845000\n",
+      "epoch [ 397] L = 38.747239, acc = 0.845000\n",
+      "epoch [ 398] L = 38.746366, acc = 0.845000\n",
+      "epoch [ 399] L = 38.745499, acc = 0.845000\n",
+      "epoch [ 400] L = 38.744639, acc = 0.845000\n",
+      "epoch [ 401] L = 38.743785, acc = 0.850000\n",
+      "epoch [ 402] L = 38.742938, acc = 0.850000\n",
+      "epoch [ 403] L = 38.742097, acc = 0.850000\n",
+      "epoch [ 404] L = 38.741263, acc = 0.850000\n",
+      "epoch [ 405] L = 38.740435, acc = 0.850000\n",
+      "epoch [ 406] L = 38.739612, acc = 0.850000\n",
+      "epoch [ 407] L = 38.738796, acc = 0.850000\n",
+      "epoch [ 408] L = 38.737986, acc = 0.850000\n",
+      "epoch [ 409] L = 38.737181, acc = 0.850000\n",
+      "epoch [ 410] L = 38.736383, acc = 0.850000\n",
+      "epoch [ 411] L = 38.735590, acc = 0.850000\n",
+      "epoch [ 412] L = 38.734803, acc = 0.850000\n",
+      "epoch [ 413] L = 38.734021, acc = 0.850000\n",
+      "epoch [ 414] L = 38.733245, acc = 0.850000\n",
+      "epoch [ 415] L = 38.732475, acc = 0.850000\n",
+      "epoch [ 416] L = 38.731710, acc = 0.850000\n",
+      "epoch [ 417] L = 38.730950, acc = 0.850000\n",
+      "epoch [ 418] L = 38.730195, acc = 0.850000\n",
+      "epoch [ 419] L = 38.729446, acc = 0.850000\n",
+      "epoch [ 420] L = 38.728702, acc = 0.850000\n",
+      "epoch [ 421] L = 38.727963, acc = 0.850000\n",
+      "epoch [ 422] L = 38.727229, acc = 0.850000\n",
+      "epoch [ 423] L = 38.726500, acc = 0.850000\n",
+      "epoch [ 424] L = 38.725776, acc = 0.850000\n",
+      "epoch [ 425] L = 38.725057, acc = 0.850000\n",
+      "epoch [ 426] L = 38.724342, acc = 0.850000\n",
+      "epoch [ 427] L = 38.723633, acc = 0.850000\n",
+      "epoch [ 428] L = 38.722928, acc = 0.850000\n",
+      "epoch [ 429] L = 38.722227, acc = 0.850000\n",
+      "epoch [ 430] L = 38.721532, acc = 0.850000\n",
+      "epoch [ 431] L = 38.720840, acc = 0.850000\n",
+      "epoch [ 432] L = 38.720154, acc = 0.850000\n",
+      "epoch [ 433] L = 38.719471, acc = 0.850000\n",
+      "epoch [ 434] L = 38.718794, acc = 0.850000\n",
+      "epoch [ 435] L = 38.718120, acc = 0.850000\n",
+      "epoch [ 436] L = 38.717451, acc = 0.850000\n",
+      "epoch [ 437] L = 38.716786, acc = 0.850000\n",
+      "epoch [ 438] L = 38.716125, acc = 0.850000\n",
+      "epoch [ 439] L = 38.715468, acc = 0.850000\n",
+      "epoch [ 440] L = 38.714815, acc = 0.850000\n",
+      "epoch [ 441] L = 38.714167, acc = 0.850000\n",
+      "epoch [ 442] L = 38.713522, acc = 0.850000\n",
+      "epoch [ 443] L = 38.712881, acc = 0.850000\n",
+      "epoch [ 444] L = 38.712245, acc = 0.850000\n",
+      "epoch [ 445] L = 38.711612, acc = 0.850000\n",
+      "epoch [ 446] L = 38.710983, acc = 0.850000\n",
+      "epoch [ 447] L = 38.710357, acc = 0.850000\n",
+      "epoch [ 448] L = 38.709736, acc = 0.850000\n",
+      "epoch [ 449] L = 38.709118, acc = 0.850000\n",
+      "epoch [ 450] L = 38.708504, acc = 0.850000\n",
+      "epoch [ 451] L = 38.707893, acc = 0.850000\n",
+      "epoch [ 452] L = 38.707286, acc = 0.850000\n",
+      "epoch [ 453] L = 38.706683, acc = 0.850000\n",
+      "epoch [ 454] L = 38.706083, acc = 0.850000\n",
+      "epoch [ 455] L = 38.705486, acc = 0.850000\n",
+      "epoch [ 456] L = 38.704893, acc = 0.850000\n",
+      "epoch [ 457] L = 38.704304, acc = 0.850000\n",
+      "epoch [ 458] L = 38.703717, acc = 0.850000\n",
+      "epoch [ 459] L = 38.703134, acc = 0.850000\n",
+      "epoch [ 460] L = 38.702554, acc = 0.850000\n",
+      "epoch [ 461] L = 38.701978, acc = 0.850000\n",
+      "epoch [ 462] L = 38.701405, acc = 0.850000\n",
+      "epoch [ 463] L = 38.700834, acc = 0.850000\n",
+      "epoch [ 464] L = 38.700267, acc = 0.850000\n",
+      "epoch [ 465] L = 38.699704, acc = 0.850000\n",
+      "epoch [ 466] L = 38.699143, acc = 0.850000\n",
+      "epoch [ 467] L = 38.698585, acc = 0.850000\n",
+      "epoch [ 468] L = 38.698030, acc = 0.850000\n",
+      "epoch [ 469] L = 38.697478, acc = 0.850000\n",
+      "epoch [ 470] L = 38.696930, acc = 0.850000\n",
+      "epoch [ 471] L = 38.696384, acc = 0.850000\n",
+      "epoch [ 472] L = 38.695841, acc = 0.850000\n",
+      "epoch [ 473] L = 38.695300, acc = 0.850000\n",
+      "epoch [ 474] L = 38.694763, acc = 0.850000\n",
+      "epoch [ 475] L = 38.694228, acc = 0.850000\n",
+      "epoch [ 476] L = 38.693697, acc = 0.850000\n",
+      "epoch [ 477] L = 38.693168, acc = 0.850000\n",
+      "epoch [ 478] L = 38.692641, acc = 0.850000\n",
+      "epoch [ 479] L = 38.692118, acc = 0.850000\n",
+      "epoch [ 480] L = 38.691597, acc = 0.850000\n",
+      "epoch [ 481] L = 38.691078, acc = 0.850000\n",
+      "epoch [ 482] L = 38.690562, acc = 0.850000\n",
+      "epoch [ 483] L = 38.690049, acc = 0.850000\n",
+      "epoch [ 484] L = 38.689538, acc = 0.850000\n",
+      "epoch [ 485] L = 38.689030, acc = 0.850000\n",
+      "epoch [ 486] L = 38.688525, acc = 0.850000\n",
+      "epoch [ 487] L = 38.688021, acc = 0.850000\n",
+      "epoch [ 488] L = 38.687521, acc = 0.850000\n",
+      "epoch [ 489] L = 38.687022, acc = 0.850000\n",
+      "epoch [ 490] L = 38.686526, acc = 0.850000\n",
+      "epoch [ 491] L = 38.686033, acc = 0.850000\n",
+      "epoch [ 492] L = 38.685542, acc = 0.850000\n",
+      "epoch [ 493] L = 38.685053, acc = 0.850000\n",
+      "epoch [ 494] L = 38.684566, acc = 0.850000\n",
+      "epoch [ 495] L = 38.684082, acc = 0.850000\n",
+      "epoch [ 496] L = 38.683600, acc = 0.850000\n",
+      "epoch [ 497] L = 38.683120, acc = 0.850000\n",
+      "epoch [ 498] L = 38.682643, acc = 0.850000\n",
+      "epoch [ 499] L = 38.682167, acc = 0.850000\n",
+      "epoch [ 500] L = 38.681694, acc = 0.850000\n",
+      "epoch [ 501] L = 38.681223, acc = 0.850000\n",
+      "epoch [ 502] L = 38.680754, acc = 0.850000\n",
+      "epoch [ 503] L = 38.680287, acc = 0.850000\n",
+      "epoch [ 504] L = 38.679823, acc = 0.850000\n",
+      "epoch [ 505] L = 38.679360, acc = 0.850000\n",
+      "epoch [ 506] L = 38.678899, acc = 0.850000\n",
+      "epoch [ 507] L = 38.678441, acc = 0.850000\n",
+      "epoch [ 508] L = 38.677984, acc = 0.850000\n",
+      "epoch [ 509] L = 38.677530, acc = 0.850000\n",
+      "epoch [ 510] L = 38.677077, acc = 0.850000\n",
+      "epoch [ 511] L = 38.676627, acc = 0.850000\n",
+      "epoch [ 512] L = 38.676178, acc = 0.850000\n",
+      "epoch [ 513] L = 38.675731, acc = 0.850000\n",
+      "epoch [ 514] L = 38.675286, acc = 0.850000\n",
+      "epoch [ 515] L = 38.674843, acc = 0.850000\n",
+      "epoch [ 516] L = 38.674402, acc = 0.850000\n",
+      "epoch [ 517] L = 38.673963, acc = 0.850000\n",
+      "epoch [ 518] L = 38.673526, acc = 0.850000\n",
+      "epoch [ 519] L = 38.673090, acc = 0.850000\n",
+      "epoch [ 520] L = 38.672656, acc = 0.850000\n",
+      "epoch [ 521] L = 38.672224, acc = 0.850000\n",
+      "epoch [ 522] L = 38.671794, acc = 0.850000\n",
+      "epoch [ 523] L = 38.671365, acc = 0.850000\n",
+      "epoch [ 524] L = 38.670939, acc = 0.850000\n",
+      "epoch [ 525] L = 38.670514, acc = 0.850000\n",
+      "epoch [ 526] L = 38.670090, acc = 0.850000\n",
+      "epoch [ 527] L = 38.669669, acc = 0.850000\n",
+      "epoch [ 528] L = 38.669249, acc = 0.850000\n",
+      "epoch [ 529] L = 38.668830, acc = 0.850000\n",
+      "epoch [ 530] L = 38.668414, acc = 0.850000\n",
+      "epoch [ 531] L = 38.667999, acc = 0.850000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch [ 532] L = 38.667585, acc = 0.850000\n",
+      "epoch [ 533] L = 38.667173, acc = 0.850000\n",
+      "epoch [ 534] L = 38.666763, acc = 0.850000\n",
+      "epoch [ 535] L = 38.666354, acc = 0.850000\n",
+      "epoch [ 536] L = 38.665947, acc = 0.850000\n",
+      "epoch [ 537] L = 38.665542, acc = 0.850000\n",
+      "epoch [ 538] L = 38.665138, acc = 0.850000\n",
+      "epoch [ 539] L = 38.664735, acc = 0.850000\n",
+      "epoch [ 540] L = 38.664334, acc = 0.850000\n",
+      "epoch [ 541] L = 38.663935, acc = 0.850000\n",
+      "epoch [ 542] L = 38.663537, acc = 0.850000\n",
+      "epoch [ 543] L = 38.663140, acc = 0.850000\n",
+      "epoch [ 544] L = 38.662745, acc = 0.850000\n",
+      "epoch [ 545] L = 38.662351, acc = 0.850000\n",
+      "epoch [ 546] L = 38.661959, acc = 0.850000\n",
+      "epoch [ 547] L = 38.661568, acc = 0.850000\n",
+      "epoch [ 548] L = 38.661179, acc = 0.850000\n",
+      "epoch [ 549] L = 38.660791, acc = 0.850000\n",
+      "epoch [ 550] L = 38.660404, acc = 0.850000\n",
+      "epoch [ 551] L = 38.660019, acc = 0.850000\n",
+      "epoch [ 552] L = 38.659635, acc = 0.850000\n",
+      "epoch [ 553] L = 38.659253, acc = 0.850000\n",
+      "epoch [ 554] L = 38.658872, acc = 0.850000\n",
+      "epoch [ 555] L = 38.658492, acc = 0.850000\n",
+      "epoch [ 556] L = 38.658113, acc = 0.850000\n",
+      "epoch [ 557] L = 38.657736, acc = 0.850000\n",
+      "epoch [ 558] L = 38.657360, acc = 0.850000\n",
+      "epoch [ 559] L = 38.656986, acc = 0.850000\n",
+      "epoch [ 560] L = 38.656612, acc = 0.850000\n",
+      "epoch [ 561] L = 38.656240, acc = 0.850000\n",
+      "epoch [ 562] L = 38.655869, acc = 0.850000\n",
+      "epoch [ 563] L = 38.655500, acc = 0.850000\n",
+      "epoch [ 564] L = 38.655131, acc = 0.850000\n",
+      "epoch [ 565] L = 38.654764, acc = 0.850000\n",
+      "epoch [ 566] L = 38.654398, acc = 0.850000\n",
+      "epoch [ 567] L = 38.654034, acc = 0.850000\n",
+      "epoch [ 568] L = 38.653670, acc = 0.850000\n",
+      "epoch [ 569] L = 38.653308, acc = 0.850000\n",
+      "epoch [ 570] L = 38.652947, acc = 0.850000\n",
+      "epoch [ 571] L = 38.652587, acc = 0.850000\n",
+      "epoch [ 572] L = 38.652228, acc = 0.850000\n",
+      "epoch [ 573] L = 38.651870, acc = 0.850000\n",
+      "epoch [ 574] L = 38.651514, acc = 0.850000\n",
+      "epoch [ 575] L = 38.651159, acc = 0.850000\n",
+      "epoch [ 576] L = 38.650804, acc = 0.850000\n",
+      "epoch [ 577] L = 38.650451, acc = 0.850000\n",
+      "epoch [ 578] L = 38.650099, acc = 0.850000\n",
+      "epoch [ 579] L = 38.649748, acc = 0.850000\n",
+      "epoch [ 580] L = 38.649399, acc = 0.850000\n",
+      "epoch [ 581] L = 38.649050, acc = 0.850000\n",
+      "epoch [ 582] L = 38.648702, acc = 0.850000\n",
+      "epoch [ 583] L = 38.648356, acc = 0.850000\n",
+      "epoch [ 584] L = 38.648010, acc = 0.850000\n",
+      "epoch [ 585] L = 38.647666, acc = 0.850000\n",
+      "epoch [ 586] L = 38.647322, acc = 0.850000\n",
+      "epoch [ 587] L = 38.646980, acc = 0.850000\n",
+      "epoch [ 588] L = 38.646639, acc = 0.850000\n",
+      "epoch [ 589] L = 38.646299, acc = 0.850000\n",
+      "epoch [ 590] L = 38.645959, acc = 0.850000\n",
+      "epoch [ 591] L = 38.645621, acc = 0.850000\n",
+      "epoch [ 592] L = 38.645284, acc = 0.850000\n",
+      "epoch [ 593] L = 38.644948, acc = 0.850000\n",
+      "epoch [ 594] L = 38.644612, acc = 0.850000\n",
+      "epoch [ 595] L = 38.644278, acc = 0.850000\n",
+      "epoch [ 596] L = 38.643945, acc = 0.850000\n",
+      "epoch [ 597] L = 38.643612, acc = 0.850000\n",
+      "epoch [ 598] L = 38.643281, acc = 0.850000\n",
+      "epoch [ 599] L = 38.642951, acc = 0.850000\n",
+      "epoch [ 600] L = 38.642621, acc = 0.850000\n",
+      "epoch [ 601] L = 38.642293, acc = 0.850000\n",
+      "epoch [ 602] L = 38.641965, acc = 0.850000\n",
+      "epoch [ 603] L = 38.641638, acc = 0.850000\n",
+      "epoch [ 604] L = 38.641313, acc = 0.850000\n",
+      "epoch [ 605] L = 38.640988, acc = 0.850000\n",
+      "epoch [ 606] L = 38.640664, acc = 0.850000\n",
+      "epoch [ 607] L = 38.640341, acc = 0.850000\n",
+      "epoch [ 608] L = 38.640019, acc = 0.850000\n",
+      "epoch [ 609] L = 38.639698, acc = 0.850000\n",
+      "epoch [ 610] L = 38.639377, acc = 0.850000\n",
+      "epoch [ 611] L = 38.639058, acc = 0.850000\n",
+      "epoch [ 612] L = 38.638739, acc = 0.850000\n",
+      "epoch [ 613] L = 38.638422, acc = 0.850000\n",
+      "epoch [ 614] L = 38.638105, acc = 0.850000\n",
+      "epoch [ 615] L = 38.637789, acc = 0.850000\n",
+      "epoch [ 616] L = 38.637474, acc = 0.850000\n",
+      "epoch [ 617] L = 38.637160, acc = 0.850000\n",
+      "epoch [ 618] L = 38.636846, acc = 0.850000\n",
+      "epoch [ 619] L = 38.636534, acc = 0.850000\n",
+      "epoch [ 620] L = 38.636222, acc = 0.850000\n",
+      "epoch [ 621] L = 38.635911, acc = 0.850000\n",
+      "epoch [ 622] L = 38.635601, acc = 0.850000\n",
+      "epoch [ 623] L = 38.635292, acc = 0.850000\n",
+      "epoch [ 624] L = 38.634983, acc = 0.850000\n",
+      "epoch [ 625] L = 38.634676, acc = 0.850000\n",
+      "epoch [ 626] L = 38.634369, acc = 0.850000\n",
+      "epoch [ 627] L = 38.634063, acc = 0.850000\n",
+      "epoch [ 628] L = 38.633757, acc = 0.850000\n",
+      "epoch [ 629] L = 38.633453, acc = 0.850000\n",
+      "epoch [ 630] L = 38.633149, acc = 0.850000\n",
+      "epoch [ 631] L = 38.632846, acc = 0.850000\n",
+      "epoch [ 632] L = 38.632544, acc = 0.850000\n",
+      "epoch [ 633] L = 38.632243, acc = 0.850000\n",
+      "epoch [ 634] L = 38.631942, acc = 0.850000\n",
+      "epoch [ 635] L = 38.631642, acc = 0.850000\n",
+      "epoch [ 636] L = 38.631343, acc = 0.850000\n",
+      "epoch [ 637] L = 38.631045, acc = 0.850000\n",
+      "epoch [ 638] L = 38.630747, acc = 0.850000\n",
+      "epoch [ 639] L = 38.630451, acc = 0.850000\n",
+      "epoch [ 640] L = 38.630154, acc = 0.850000\n",
+      "epoch [ 641] L = 38.629859, acc = 0.850000\n",
+      "epoch [ 642] L = 38.629564, acc = 0.850000\n",
+      "epoch [ 643] L = 38.629271, acc = 0.850000\n",
+      "epoch [ 644] L = 38.628977, acc = 0.850000\n",
+      "epoch [ 645] L = 38.628685, acc = 0.850000\n",
+      "epoch [ 646] L = 38.628393, acc = 0.850000\n",
+      "epoch [ 647] L = 38.628102, acc = 0.850000\n",
+      "epoch [ 648] L = 38.627812, acc = 0.850000\n",
+      "epoch [ 649] L = 38.627522, acc = 0.850000\n",
+      "epoch [ 650] L = 38.627233, acc = 0.850000\n",
+      "epoch [ 651] L = 38.626945, acc = 0.850000\n",
+      "epoch [ 652] L = 38.626657, acc = 0.850000\n",
+      "epoch [ 653] L = 38.626371, acc = 0.850000\n",
+      "epoch [ 654] L = 38.626084, acc = 0.850000\n",
+      "epoch [ 655] L = 38.625799, acc = 0.850000\n",
+      "epoch [ 656] L = 38.625514, acc = 0.850000\n",
+      "epoch [ 657] L = 38.625230, acc = 0.850000\n",
+      "epoch [ 658] L = 38.624946, acc = 0.850000\n",
+      "epoch [ 659] L = 38.624664, acc = 0.850000\n",
+      "epoch [ 660] L = 38.624381, acc = 0.850000\n",
+      "epoch [ 661] L = 38.624100, acc = 0.850000\n",
+      "epoch [ 662] L = 38.623819, acc = 0.850000\n",
+      "epoch [ 663] L = 38.623539, acc = 0.850000\n",
+      "epoch [ 664] L = 38.623259, acc = 0.850000\n",
+      "epoch [ 665] L = 38.622980, acc = 0.850000\n",
+      "epoch [ 666] L = 38.622702, acc = 0.850000\n",
+      "epoch [ 667] L = 38.622424, acc = 0.850000\n",
+      "epoch [ 668] L = 38.622147, acc = 0.850000\n",
+      "epoch [ 669] L = 38.621871, acc = 0.850000\n",
+      "epoch [ 670] L = 38.621595, acc = 0.850000\n",
+      "epoch [ 671] L = 38.621320, acc = 0.850000\n",
+      "epoch [ 672] L = 38.621046, acc = 0.850000\n",
+      "epoch [ 673] L = 38.620772, acc = 0.850000\n",
+      "epoch [ 674] L = 38.620499, acc = 0.850000\n",
+      "epoch [ 675] L = 38.620226, acc = 0.850000\n",
+      "epoch [ 676] L = 38.619954, acc = 0.850000\n",
+      "epoch [ 677] L = 38.619682, acc = 0.850000\n",
+      "epoch [ 678] L = 38.619412, acc = 0.850000\n",
+      "epoch [ 679] L = 38.619141, acc = 0.850000\n",
+      "epoch [ 680] L = 38.618872, acc = 0.850000\n",
+      "epoch [ 681] L = 38.618603, acc = 0.850000\n",
+      "epoch [ 682] L = 38.618334, acc = 0.850000\n",
+      "epoch [ 683] L = 38.618066, acc = 0.850000\n",
+      "epoch [ 684] L = 38.617799, acc = 0.850000\n",
+      "epoch [ 685] L = 38.617532, acc = 0.850000\n",
+      "epoch [ 686] L = 38.617266, acc = 0.850000\n",
+      "epoch [ 687] L = 38.617001, acc = 0.850000\n",
+      "epoch [ 688] L = 38.616736, acc = 0.850000\n",
+      "epoch [ 689] L = 38.616471, acc = 0.850000\n",
+      "epoch [ 690] L = 38.616208, acc = 0.850000\n",
+      "epoch [ 691] L = 38.615944, acc = 0.850000\n",
+      "epoch [ 692] L = 38.615682, acc = 0.850000\n",
+      "epoch [ 693] L = 38.615420, acc = 0.850000\n",
+      "epoch [ 694] L = 38.615158, acc = 0.850000\n",
+      "epoch [ 695] L = 38.614897, acc = 0.850000\n",
+      "epoch [ 696] L = 38.614637, acc = 0.850000\n",
+      "epoch [ 697] L = 38.614377, acc = 0.850000\n",
+      "epoch [ 698] L = 38.614117, acc = 0.850000\n",
+      "epoch [ 699] L = 38.613859, acc = 0.850000\n",
+      "epoch [ 700] L = 38.613600, acc = 0.850000\n",
+      "epoch [ 701] L = 38.613343, acc = 0.850000\n",
+      "epoch [ 702] L = 38.613085, acc = 0.850000\n",
+      "epoch [ 703] L = 38.612829, acc = 0.850000\n",
+      "epoch [ 704] L = 38.612573, acc = 0.850000\n",
+      "epoch [ 705] L = 38.612317, acc = 0.850000\n",
+      "epoch [ 706] L = 38.612062, acc = 0.850000\n",
+      "epoch [ 707] L = 38.611808, acc = 0.850000\n",
+      "epoch [ 708] L = 38.611554, acc = 0.850000\n",
+      "epoch [ 709] L = 38.611300, acc = 0.850000\n",
+      "epoch [ 710] L = 38.611047, acc = 0.850000\n",
+      "epoch [ 711] L = 38.610795, acc = 0.850000\n",
+      "epoch [ 712] L = 38.610543, acc = 0.850000\n",
+      "epoch [ 713] L = 38.610291, acc = 0.850000\n",
+      "epoch [ 714] L = 38.610041, acc = 0.850000\n",
+      "epoch [ 715] L = 38.609790, acc = 0.850000\n",
+      "epoch [ 716] L = 38.609540, acc = 0.850000\n",
+      "epoch [ 717] L = 38.609291, acc = 0.850000\n",
+      "epoch [ 718] L = 38.609042, acc = 0.850000\n",
+      "epoch [ 719] L = 38.608794, acc = 0.850000\n",
+      "epoch [ 720] L = 38.608546, acc = 0.850000\n",
+      "epoch [ 721] L = 38.608299, acc = 0.850000\n",
+      "epoch [ 722] L = 38.608052, acc = 0.850000\n",
+      "epoch [ 723] L = 38.607805, acc = 0.850000\n",
+      "epoch [ 724] L = 38.607559, acc = 0.850000\n",
+      "epoch [ 725] L = 38.607314, acc = 0.850000\n",
+      "epoch [ 726] L = 38.607069, acc = 0.850000\n",
+      "epoch [ 727] L = 38.606825, acc = 0.850000\n",
+      "epoch [ 728] L = 38.606581, acc = 0.850000\n",
+      "epoch [ 729] L = 38.606337, acc = 0.850000\n",
+      "epoch [ 730] L = 38.606094, acc = 0.850000\n",
+      "epoch [ 731] L = 38.605852, acc = 0.850000\n",
+      "epoch [ 732] L = 38.605610, acc = 0.850000\n",
+      "epoch [ 733] L = 38.605368, acc = 0.850000\n",
+      "epoch [ 734] L = 38.605127, acc = 0.850000\n",
+      "epoch [ 735] L = 38.604887, acc = 0.850000\n",
+      "epoch [ 736] L = 38.604647, acc = 0.850000\n",
+      "epoch [ 737] L = 38.604407, acc = 0.850000\n",
+      "epoch [ 738] L = 38.604168, acc = 0.850000\n",
+      "epoch [ 739] L = 38.603929, acc = 0.850000\n",
+      "epoch [ 740] L = 38.603691, acc = 0.850000\n",
+      "epoch [ 741] L = 38.603453, acc = 0.850000\n",
+      "epoch [ 742] L = 38.603216, acc = 0.850000\n",
+      "epoch [ 743] L = 38.602979, acc = 0.850000\n",
+      "epoch [ 744] L = 38.602742, acc = 0.850000\n",
+      "epoch [ 745] L = 38.602506, acc = 0.850000\n",
+      "epoch [ 746] L = 38.602271, acc = 0.850000\n",
+      "epoch [ 747] L = 38.602036, acc = 0.850000\n",
+      "epoch [ 748] L = 38.601801, acc = 0.850000\n",
+      "epoch [ 749] L = 38.601567, acc = 0.850000\n",
+      "epoch [ 750] L = 38.601333, acc = 0.850000\n",
+      "epoch [ 751] L = 38.601100, acc = 0.850000\n",
+      "epoch [ 752] L = 38.600867, acc = 0.850000\n",
+      "epoch [ 753] L = 38.600635, acc = 0.850000\n",
+      "epoch [ 754] L = 38.600403, acc = 0.850000\n",
+      "epoch [ 755] L = 38.600171, acc = 0.850000\n",
+      "epoch [ 756] L = 38.599940, acc = 0.850000\n",
+      "epoch [ 757] L = 38.599709, acc = 0.850000\n",
+      "epoch [ 758] L = 38.599479, acc = 0.850000\n",
+      "epoch [ 759] L = 38.599249, acc = 0.850000\n",
+      "epoch [ 760] L = 38.599020, acc = 0.850000\n",
+      "epoch [ 761] L = 38.598791, acc = 0.850000\n",
+      "epoch [ 762] L = 38.598562, acc = 0.850000\n",
+      "epoch [ 763] L = 38.598334, acc = 0.850000\n",
+      "epoch [ 764] L = 38.598107, acc = 0.850000\n",
+      "epoch [ 765] L = 38.597879, acc = 0.850000\n",
+      "epoch [ 766] L = 38.597653, acc = 0.850000\n",
+      "epoch [ 767] L = 38.597426, acc = 0.850000\n",
+      "epoch [ 768] L = 38.597200, acc = 0.850000\n",
+      "epoch [ 769] L = 38.596975, acc = 0.850000\n",
+      "epoch [ 770] L = 38.596749, acc = 0.850000\n",
+      "epoch [ 771] L = 38.596525, acc = 0.850000\n",
+      "epoch [ 772] L = 38.596300, acc = 0.850000\n",
+      "epoch [ 773] L = 38.596076, acc = 0.850000\n",
+      "epoch [ 774] L = 38.595853, acc = 0.850000\n",
+      "epoch [ 775] L = 38.595630, acc = 0.850000\n",
+      "epoch [ 776] L = 38.595407, acc = 0.850000\n",
+      "epoch [ 777] L = 38.595185, acc = 0.850000\n",
+      "epoch [ 778] L = 38.594963, acc = 0.850000\n",
+      "epoch [ 779] L = 38.594741, acc = 0.850000\n",
+      "epoch [ 780] L = 38.594520, acc = 0.850000\n",
+      "epoch [ 781] L = 38.594299, acc = 0.850000\n",
+      "epoch [ 782] L = 38.594079, acc = 0.850000\n",
+      "epoch [ 783] L = 38.593859, acc = 0.850000\n",
+      "epoch [ 784] L = 38.593640, acc = 0.850000\n",
+      "epoch [ 785] L = 38.593421, acc = 0.850000\n",
+      "epoch [ 786] L = 38.593202, acc = 0.850000\n",
+      "epoch [ 787] L = 38.592984, acc = 0.850000\n",
+      "epoch [ 788] L = 38.592766, acc = 0.850000\n",
+      "epoch [ 789] L = 38.592548, acc = 0.850000\n",
+      "epoch [ 790] L = 38.592331, acc = 0.850000\n",
+      "epoch [ 791] L = 38.592114, acc = 0.850000\n",
+      "epoch [ 792] L = 38.591898, acc = 0.850000\n",
+      "epoch [ 793] L = 38.591682, acc = 0.850000\n",
+      "epoch [ 794] L = 38.591466, acc = 0.850000\n",
+      "epoch [ 795] L = 38.591251, acc = 0.850000\n",
+      "epoch [ 796] L = 38.591036, acc = 0.850000\n",
+      "epoch [ 797] L = 38.590821, acc = 0.850000\n",
+      "epoch [ 798] L = 38.590607, acc = 0.850000\n",
+      "epoch [ 799] L = 38.590394, acc = 0.850000\n",
+      "epoch [ 800] L = 38.590180, acc = 0.850000\n",
+      "epoch [ 801] L = 38.589967, acc = 0.850000\n",
+      "epoch [ 802] L = 38.589755, acc = 0.850000\n",
+      "epoch [ 803] L = 38.589542, acc = 0.850000\n",
+      "epoch [ 804] L = 38.589330, acc = 0.850000\n",
+      "epoch [ 805] L = 38.589119, acc = 0.850000\n",
+      "epoch [ 806] L = 38.588908, acc = 0.850000\n",
+      "epoch [ 807] L = 38.588697, acc = 0.850000\n",
+      "epoch [ 808] L = 38.588487, acc = 0.850000\n",
+      "epoch [ 809] L = 38.588277, acc = 0.850000\n",
+      "epoch [ 810] L = 38.588067, acc = 0.850000\n",
+      "epoch [ 811] L = 38.587858, acc = 0.850000\n",
+      "epoch [ 812] L = 38.587649, acc = 0.850000\n",
+      "epoch [ 813] L = 38.587440, acc = 0.850000\n",
+      "epoch [ 814] L = 38.587232, acc = 0.850000\n",
+      "epoch [ 815] L = 38.587024, acc = 0.850000\n",
+      "epoch [ 816] L = 38.586817, acc = 0.850000\n",
+      "epoch [ 817] L = 38.586609, acc = 0.850000\n",
+      "epoch [ 818] L = 38.586403, acc = 0.850000\n",
+      "epoch [ 819] L = 38.586196, acc = 0.850000\n",
+      "epoch [ 820] L = 38.585990, acc = 0.850000\n",
+      "epoch [ 821] L = 38.585784, acc = 0.850000\n",
+      "epoch [ 822] L = 38.585579, acc = 0.850000\n",
+      "epoch [ 823] L = 38.585374, acc = 0.850000\n",
+      "epoch [ 824] L = 38.585169, acc = 0.850000\n",
+      "epoch [ 825] L = 38.584965, acc = 0.850000\n",
+      "epoch [ 826] L = 38.584761, acc = 0.850000\n",
+      "epoch [ 827] L = 38.584557, acc = 0.850000\n",
+      "epoch [ 828] L = 38.584354, acc = 0.850000\n",
+      "epoch [ 829] L = 38.584151, acc = 0.850000\n",
+      "epoch [ 830] L = 38.583948, acc = 0.850000\n",
+      "epoch [ 831] L = 38.583746, acc = 0.850000\n",
+      "epoch [ 832] L = 38.583544, acc = 0.850000\n",
+      "epoch [ 833] L = 38.583342, acc = 0.850000\n",
+      "epoch [ 834] L = 38.583141, acc = 0.850000\n",
+      "epoch [ 835] L = 38.582940, acc = 0.850000\n",
+      "epoch [ 836] L = 38.582740, acc = 0.850000\n",
+      "epoch [ 837] L = 38.582539, acc = 0.850000\n",
+      "epoch [ 838] L = 38.582339, acc = 0.850000\n",
+      "epoch [ 839] L = 38.582140, acc = 0.850000\n",
+      "epoch [ 840] L = 38.581941, acc = 0.850000\n",
+      "epoch [ 841] L = 38.581742, acc = 0.850000\n",
+      "epoch [ 842] L = 38.581543, acc = 0.850000\n",
+      "epoch [ 843] L = 38.581345, acc = 0.850000\n",
+      "epoch [ 844] L = 38.581147, acc = 0.850000\n",
+      "epoch [ 845] L = 38.580949, acc = 0.850000\n",
+      "epoch [ 846] L = 38.580752, acc = 0.850000\n",
+      "epoch [ 847] L = 38.580555, acc = 0.850000\n",
+      "epoch [ 848] L = 38.580359, acc = 0.850000\n",
+      "epoch [ 849] L = 38.580162, acc = 0.850000\n",
+      "epoch [ 850] L = 38.579966, acc = 0.850000\n",
+      "epoch [ 851] L = 38.579771, acc = 0.850000\n",
+      "epoch [ 852] L = 38.579575, acc = 0.850000\n",
+      "epoch [ 853] L = 38.579380, acc = 0.850000\n",
+      "epoch [ 854] L = 38.579186, acc = 0.850000\n",
+      "epoch [ 855] L = 38.578991, acc = 0.850000\n",
+      "epoch [ 856] L = 38.578797, acc = 0.850000\n",
+      "epoch [ 857] L = 38.578603, acc = 0.850000\n",
+      "epoch [ 858] L = 38.578410, acc = 0.850000\n",
+      "epoch [ 859] L = 38.578217, acc = 0.850000\n",
+      "epoch [ 860] L = 38.578024, acc = 0.850000\n",
+      "epoch [ 861] L = 38.577832, acc = 0.850000\n",
+      "epoch [ 862] L = 38.577639, acc = 0.850000\n",
+      "epoch [ 863] L = 38.577448, acc = 0.850000\n",
+      "epoch [ 864] L = 38.577256, acc = 0.850000\n",
+      "epoch [ 865] L = 38.577065, acc = 0.850000\n",
+      "epoch [ 866] L = 38.576874, acc = 0.850000\n",
+      "epoch [ 867] L = 38.576683, acc = 0.850000\n",
+      "epoch [ 868] L = 38.576493, acc = 0.850000\n",
+      "epoch [ 869] L = 38.576303, acc = 0.850000\n",
+      "epoch [ 870] L = 38.576113, acc = 0.850000\n",
+      "epoch [ 871] L = 38.575924, acc = 0.850000\n",
+      "epoch [ 872] L = 38.575735, acc = 0.850000\n",
+      "epoch [ 873] L = 38.575546, acc = 0.850000\n",
+      "epoch [ 874] L = 38.575358, acc = 0.850000\n",
+      "epoch [ 875] L = 38.575169, acc = 0.850000\n",
+      "epoch [ 876] L = 38.574981, acc = 0.850000\n",
+      "epoch [ 877] L = 38.574794, acc = 0.850000\n",
+      "epoch [ 878] L = 38.574607, acc = 0.850000\n",
+      "epoch [ 879] L = 38.574420, acc = 0.850000\n",
+      "epoch [ 880] L = 38.574233, acc = 0.850000\n",
+      "epoch [ 881] L = 38.574047, acc = 0.850000\n",
+      "epoch [ 882] L = 38.573861, acc = 0.850000\n",
+      "epoch [ 883] L = 38.573675, acc = 0.850000\n",
+      "epoch [ 884] L = 38.573489, acc = 0.850000\n",
+      "epoch [ 885] L = 38.573304, acc = 0.850000\n",
+      "epoch [ 886] L = 38.573119, acc = 0.850000\n",
+      "epoch [ 887] L = 38.572934, acc = 0.850000\n",
+      "epoch [ 888] L = 38.572750, acc = 0.850000\n",
+      "epoch [ 889] L = 38.572566, acc = 0.850000\n",
+      "epoch [ 890] L = 38.572382, acc = 0.850000\n",
+      "epoch [ 891] L = 38.572199, acc = 0.850000\n",
+      "epoch [ 892] L = 38.572016, acc = 0.850000\n",
+      "epoch [ 893] L = 38.571833, acc = 0.850000\n",
+      "epoch [ 894] L = 38.571650, acc = 0.850000\n",
+      "epoch [ 895] L = 38.571468, acc = 0.850000\n",
+      "epoch [ 896] L = 38.571286, acc = 0.850000\n",
+      "epoch [ 897] L = 38.571104, acc = 0.850000\n",
+      "epoch [ 898] L = 38.570923, acc = 0.850000\n",
+      "epoch [ 899] L = 38.570742, acc = 0.850000\n",
+      "epoch [ 900] L = 38.570561, acc = 0.850000\n",
+      "epoch [ 901] L = 38.570380, acc = 0.850000\n",
+      "epoch [ 902] L = 38.570200, acc = 0.850000\n",
+      "epoch [ 903] L = 38.570020, acc = 0.850000\n",
+      "epoch [ 904] L = 38.569840, acc = 0.850000\n",
+      "epoch [ 905] L = 38.569660, acc = 0.850000\n",
+      "epoch [ 906] L = 38.569481, acc = 0.850000\n",
+      "epoch [ 907] L = 38.569302, acc = 0.850000\n",
+      "epoch [ 908] L = 38.569124, acc = 0.850000\n",
+      "epoch [ 909] L = 38.568945, acc = 0.850000\n",
+      "epoch [ 910] L = 38.568767, acc = 0.850000\n",
+      "epoch [ 911] L = 38.568589, acc = 0.850000\n",
+      "epoch [ 912] L = 38.568412, acc = 0.850000\n",
+      "epoch [ 913] L = 38.568234, acc = 0.850000\n",
+      "epoch [ 914] L = 38.568057, acc = 0.850000\n",
+      "epoch [ 915] L = 38.567881, acc = 0.850000\n",
+      "epoch [ 916] L = 38.567704, acc = 0.850000\n",
+      "epoch [ 917] L = 38.567528, acc = 0.850000\n",
+      "epoch [ 918] L = 38.567352, acc = 0.850000\n",
+      "epoch [ 919] L = 38.567176, acc = 0.850000\n",
+      "epoch [ 920] L = 38.567001, acc = 0.850000\n",
+      "epoch [ 921] L = 38.566826, acc = 0.850000\n",
+      "epoch [ 922] L = 38.566651, acc = 0.850000\n",
+      "epoch [ 923] L = 38.566476, acc = 0.850000\n",
+      "epoch [ 924] L = 38.566302, acc = 0.850000\n",
+      "epoch [ 925] L = 38.566128, acc = 0.850000\n",
+      "epoch [ 926] L = 38.565954, acc = 0.850000\n",
+      "epoch [ 927] L = 38.565780, acc = 0.850000\n",
+      "epoch [ 928] L = 38.565607, acc = 0.850000\n",
+      "epoch [ 929] L = 38.565434, acc = 0.850000\n",
+      "epoch [ 930] L = 38.565261, acc = 0.850000\n",
+      "epoch [ 931] L = 38.565089, acc = 0.850000\n",
+      "epoch [ 932] L = 38.564917, acc = 0.850000\n",
+      "epoch [ 933] L = 38.564745, acc = 0.850000\n",
+      "epoch [ 934] L = 38.564573, acc = 0.850000\n",
+      "epoch [ 935] L = 38.564401, acc = 0.850000\n",
+      "epoch [ 936] L = 38.564230, acc = 0.850000\n",
+      "epoch [ 937] L = 38.564059, acc = 0.850000\n",
+      "epoch [ 938] L = 38.563888, acc = 0.850000\n",
+      "epoch [ 939] L = 38.563718, acc = 0.850000\n",
+      "epoch [ 940] L = 38.563548, acc = 0.850000\n",
+      "epoch [ 941] L = 38.563378, acc = 0.850000\n",
+      "epoch [ 942] L = 38.563208, acc = 0.850000\n",
+      "epoch [ 943] L = 38.563039, acc = 0.850000\n",
+      "epoch [ 944] L = 38.562869, acc = 0.850000\n",
+      "epoch [ 945] L = 38.562701, acc = 0.850000\n",
+      "epoch [ 946] L = 38.562532, acc = 0.850000\n",
+      "epoch [ 947] L = 38.562363, acc = 0.850000\n",
+      "epoch [ 948] L = 38.562195, acc = 0.850000\n",
+      "epoch [ 949] L = 38.562027, acc = 0.850000\n",
+      "epoch [ 950] L = 38.561860, acc = 0.850000\n",
+      "epoch [ 951] L = 38.561692, acc = 0.850000\n",
+      "epoch [ 952] L = 38.561525, acc = 0.850000\n",
+      "epoch [ 953] L = 38.561358, acc = 0.850000\n",
+      "epoch [ 954] L = 38.561191, acc = 0.850000\n",
+      "epoch [ 955] L = 38.561025, acc = 0.850000\n",
+      "epoch [ 956] L = 38.560859, acc = 0.850000\n",
+      "epoch [ 957] L = 38.560693, acc = 0.850000\n",
+      "epoch [ 958] L = 38.560527, acc = 0.850000\n",
+      "epoch [ 959] L = 38.560361, acc = 0.850000\n",
+      "epoch [ 960] L = 38.560196, acc = 0.850000\n",
+      "epoch [ 961] L = 38.560031, acc = 0.850000\n",
+      "epoch [ 962] L = 38.559866, acc = 0.850000\n",
+      "epoch [ 963] L = 38.559702, acc = 0.850000\n",
+      "epoch [ 964] L = 38.559537, acc = 0.850000\n",
+      "epoch [ 965] L = 38.559373, acc = 0.850000\n",
+      "epoch [ 966] L = 38.559210, acc = 0.850000\n",
+      "epoch [ 967] L = 38.559046, acc = 0.850000\n",
+      "epoch [ 968] L = 38.558883, acc = 0.850000\n",
+      "epoch [ 969] L = 38.558719, acc = 0.850000\n",
+      "epoch [ 970] L = 38.558557, acc = 0.850000\n",
+      "epoch [ 971] L = 38.558394, acc = 0.850000\n",
+      "epoch [ 972] L = 38.558232, acc = 0.850000\n",
+      "epoch [ 973] L = 38.558069, acc = 0.850000\n",
+      "epoch [ 974] L = 38.557907, acc = 0.850000\n",
+      "epoch [ 975] L = 38.557746, acc = 0.850000\n",
+      "epoch [ 976] L = 38.557584, acc = 0.850000\n",
+      "epoch [ 977] L = 38.557423, acc = 0.850000\n",
+      "epoch [ 978] L = 38.557262, acc = 0.850000\n",
+      "epoch [ 979] L = 38.557101, acc = 0.850000\n",
+      "epoch [ 980] L = 38.556941, acc = 0.850000\n",
+      "epoch [ 981] L = 38.556780, acc = 0.850000\n",
+      "epoch [ 982] L = 38.556620, acc = 0.850000\n",
+      "epoch [ 983] L = 38.556460, acc = 0.850000\n",
+      "epoch [ 984] L = 38.556301, acc = 0.850000\n",
+      "epoch [ 985] L = 38.556141, acc = 0.850000\n",
+      "epoch [ 986] L = 38.555982, acc = 0.850000\n",
+      "epoch [ 987] L = 38.555823, acc = 0.850000\n",
+      "epoch [ 988] L = 38.555664, acc = 0.850000\n",
+      "epoch [ 989] L = 38.555506, acc = 0.850000\n",
+      "epoch [ 990] L = 38.555347, acc = 0.850000\n",
+      "epoch [ 991] L = 38.555189, acc = 0.850000\n",
+      "epoch [ 992] L = 38.555032, acc = 0.850000\n",
+      "epoch [ 993] L = 38.554874, acc = 0.850000\n",
+      "epoch [ 994] L = 38.554717, acc = 0.850000\n",
+      "epoch [ 995] L = 38.554559, acc = 0.850000\n",
+      "epoch [ 996] L = 38.554402, acc = 0.850000\n",
+      "epoch [ 997] L = 38.554246, acc = 0.850000\n",
+      "epoch [ 998] L = 38.554089, acc = 0.850000\n",
+      "epoch [ 999] L = 38.553933, acc = 0.850000\n",
+      "epoch [1000] L = 38.553777, acc = 0.850000\n",
+      "epoch [1001] L = 38.553621, acc = 0.850000\n",
+      "epoch [1002] L = 38.553465, acc = 0.850000\n",
+      "epoch [1003] L = 38.553310, acc = 0.850000\n",
+      "epoch [1004] L = 38.553154, acc = 0.850000\n",
+      "epoch [1005] L = 38.552999, acc = 0.850000\n",
+      "epoch [1006] L = 38.552845, acc = 0.850000\n",
+      "epoch [1007] L = 38.552690, acc = 0.850000\n",
+      "epoch [1008] L = 38.552536, acc = 0.850000\n",
+      "epoch [1009] L = 38.552382, acc = 0.850000\n",
+      "epoch [1010] L = 38.552228, acc = 0.850000\n",
+      "epoch [1011] L = 38.552074, acc = 0.850000\n",
+      "epoch [1012] L = 38.551920, acc = 0.850000\n",
+      "epoch [1013] L = 38.551767, acc = 0.850000\n",
+      "epoch [1014] L = 38.551614, acc = 0.850000\n",
+      "epoch [1015] L = 38.551461, acc = 0.850000\n",
+      "epoch [1016] L = 38.551308, acc = 0.850000\n",
+      "epoch [1017] L = 38.551156, acc = 0.850000\n",
+      "epoch [1018] L = 38.551004, acc = 0.850000\n",
+      "epoch [1019] L = 38.550852, acc = 0.850000\n",
+      "epoch [1020] L = 38.550700, acc = 0.850000\n",
+      "epoch [1021] L = 38.550548, acc = 0.850000\n",
+      "epoch [1022] L = 38.550397, acc = 0.850000\n",
+      "epoch [1023] L = 38.550245, acc = 0.850000\n",
+      "epoch [1024] L = 38.550094, acc = 0.850000\n",
+      "epoch [1025] L = 38.549944, acc = 0.850000\n",
+      "epoch [1026] L = 38.549793, acc = 0.850000\n",
+      "epoch [1027] L = 38.549642, acc = 0.850000\n",
+      "epoch [1028] L = 38.549492, acc = 0.850000\n",
+      "epoch [1029] L = 38.549342, acc = 0.850000\n",
+      "epoch [1030] L = 38.549192, acc = 0.850000\n",
+      "epoch [1031] L = 38.549043, acc = 0.850000\n",
+      "epoch [1032] L = 38.548893, acc = 0.850000\n",
+      "epoch [1033] L = 38.548744, acc = 0.850000\n",
+      "epoch [1034] L = 38.548595, acc = 0.850000\n",
+      "epoch [1035] L = 38.548446, acc = 0.850000\n",
+      "epoch [1036] L = 38.548298, acc = 0.850000\n",
+      "epoch [1037] L = 38.548149, acc = 0.850000\n",
+      "epoch [1038] L = 38.548001, acc = 0.850000\n",
+      "epoch [1039] L = 38.547853, acc = 0.850000\n",
+      "epoch [1040] L = 38.547705, acc = 0.850000\n",
+      "epoch [1041] L = 38.547558, acc = 0.850000\n",
+      "epoch [1042] L = 38.547410, acc = 0.850000\n",
+      "epoch [1043] L = 38.547263, acc = 0.850000\n",
+      "epoch [1044] L = 38.547116, acc = 0.850000\n",
+      "epoch [1045] L = 38.546969, acc = 0.850000\n",
+      "epoch [1046] L = 38.546823, acc = 0.850000\n",
+      "epoch [1047] L = 38.546676, acc = 0.850000\n",
+      "epoch [1048] L = 38.546530, acc = 0.850000\n",
+      "epoch [1049] L = 38.546384, acc = 0.845000\n",
+      "epoch [1050] L = 38.546238, acc = 0.845000\n",
+      "epoch [1051] L = 38.546092, acc = 0.845000\n",
+      "epoch [1052] L = 38.545947, acc = 0.845000\n",
+      "epoch [1053] L = 38.545802, acc = 0.845000\n",
+      "epoch [1054] L = 38.545656, acc = 0.845000\n",
+      "epoch [1055] L = 38.545512, acc = 0.845000\n",
+      "epoch [1056] L = 38.545367, acc = 0.845000\n",
+      "epoch [1057] L = 38.545222, acc = 0.845000\n",
+      "epoch [1058] L = 38.545078, acc = 0.845000\n",
+      "epoch [1059] L = 38.544934, acc = 0.845000\n",
+      "epoch [1060] L = 38.544790, acc = 0.845000\n",
+      "epoch [1061] L = 38.544646, acc = 0.845000\n",
+      "epoch [1062] L = 38.544502, acc = 0.845000\n",
+      "epoch [1063] L = 38.544359, acc = 0.845000\n",
+      "epoch [1064] L = 38.544216, acc = 0.845000\n",
+      "epoch [1065] L = 38.544073, acc = 0.845000\n",
+      "epoch [1066] L = 38.543930, acc = 0.845000\n",
+      "epoch [1067] L = 38.543787, acc = 0.845000\n",
+      "epoch [1068] L = 38.543645, acc = 0.845000\n",
+      "epoch [1069] L = 38.543502, acc = 0.845000\n",
+      "epoch [1070] L = 38.543360, acc = 0.845000\n",
+      "epoch [1071] L = 38.543218, acc = 0.845000\n",
+      "epoch [1072] L = 38.543077, acc = 0.845000\n",
+      "epoch [1073] L = 38.542935, acc = 0.845000\n",
+      "epoch [1074] L = 38.542794, acc = 0.845000\n",
+      "epoch [1075] L = 38.542652, acc = 0.845000\n",
+      "epoch [1076] L = 38.542511, acc = 0.845000\n",
+      "epoch [1077] L = 38.542370, acc = 0.845000\n",
+      "epoch [1078] L = 38.542230, acc = 0.845000\n",
+      "epoch [1079] L = 38.542089, acc = 0.845000\n",
+      "epoch [1080] L = 38.541949, acc = 0.845000\n",
+      "epoch [1081] L = 38.541809, acc = 0.845000\n",
+      "epoch [1082] L = 38.541669, acc = 0.845000\n",
+      "epoch [1083] L = 38.541529, acc = 0.845000\n",
+      "epoch [1084] L = 38.541389, acc = 0.845000\n",
+      "epoch [1085] L = 38.541250, acc = 0.845000\n",
+      "epoch [1086] L = 38.541111, acc = 0.845000\n",
+      "epoch [1087] L = 38.540972, acc = 0.845000\n",
+      "epoch [1088] L = 38.540833, acc = 0.845000\n",
+      "epoch [1089] L = 38.540694, acc = 0.845000\n",
+      "epoch [1090] L = 38.540555, acc = 0.845000\n",
+      "epoch [1091] L = 38.540417, acc = 0.845000\n",
+      "epoch [1092] L = 38.540279, acc = 0.845000\n",
+      "epoch [1093] L = 38.540141, acc = 0.845000\n",
+      "epoch [1094] L = 38.540003, acc = 0.845000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch [1095] L = 38.539865, acc = 0.845000\n",
+      "epoch [1096] L = 38.539728, acc = 0.845000\n",
+      "epoch [1097] L = 38.539590, acc = 0.845000\n",
+      "epoch [1098] L = 38.539453, acc = 0.845000\n",
+      "epoch [1099] L = 38.539316, acc = 0.845000\n",
+      "epoch [1100] L = 38.539179, acc = 0.845000\n",
+      "epoch [1101] L = 38.539043, acc = 0.845000\n",
+      "epoch [1102] L = 38.538906, acc = 0.845000\n",
+      "epoch [1103] L = 38.538770, acc = 0.845000\n",
+      "epoch [1104] L = 38.538634, acc = 0.845000\n",
+      "epoch [1105] L = 38.538498, acc = 0.845000\n",
+      "epoch [1106] L = 38.538362, acc = 0.845000\n",
+      "epoch [1107] L = 38.538226, acc = 0.845000\n",
+      "epoch [1108] L = 38.538090, acc = 0.845000\n",
+      "epoch [1109] L = 38.537955, acc = 0.845000\n",
+      "epoch [1110] L = 38.537820, acc = 0.845000\n",
+      "epoch [1111] L = 38.537685, acc = 0.845000\n",
+      "epoch [1112] L = 38.537550, acc = 0.845000\n",
+      "epoch [1113] L = 38.537415, acc = 0.845000\n",
+      "epoch [1114] L = 38.537281, acc = 0.845000\n",
+      "epoch [1115] L = 38.537147, acc = 0.845000\n",
+      "epoch [1116] L = 38.537012, acc = 0.845000\n",
+      "epoch [1117] L = 38.536878, acc = 0.845000\n",
+      "epoch [1118] L = 38.536744, acc = 0.845000\n",
+      "epoch [1119] L = 38.536611, acc = 0.845000\n",
+      "epoch [1120] L = 38.536477, acc = 0.845000\n",
+      "epoch [1121] L = 38.536344, acc = 0.845000\n",
+      "epoch [1122] L = 38.536211, acc = 0.845000\n",
+      "epoch [1123] L = 38.536078, acc = 0.845000\n",
+      "epoch [1124] L = 38.535945, acc = 0.845000\n",
+      "epoch [1125] L = 38.535812, acc = 0.845000\n",
+      "epoch [1126] L = 38.535679, acc = 0.845000\n",
+      "epoch [1127] L = 38.535547, acc = 0.845000\n",
+      "epoch [1128] L = 38.535415, acc = 0.845000\n",
+      "epoch [1129] L = 38.535283, acc = 0.845000\n",
+      "epoch [1130] L = 38.535151, acc = 0.845000\n",
+      "epoch [1131] L = 38.535019, acc = 0.845000\n",
+      "epoch [1132] L = 38.534887, acc = 0.845000\n",
+      "epoch [1133] L = 38.534756, acc = 0.845000\n",
+      "epoch [1134] L = 38.534624, acc = 0.845000\n",
+      "epoch [1135] L = 38.534493, acc = 0.845000\n",
+      "epoch [1136] L = 38.534362, acc = 0.845000\n",
+      "epoch [1137] L = 38.534231, acc = 0.845000\n",
+      "epoch [1138] L = 38.534101, acc = 0.845000\n",
+      "epoch [1139] L = 38.533970, acc = 0.845000\n",
+      "epoch [1140] L = 38.533840, acc = 0.845000\n",
+      "epoch [1141] L = 38.533709, acc = 0.845000\n",
+      "epoch [1142] L = 38.533579, acc = 0.845000\n",
+      "epoch [1143] L = 38.533449, acc = 0.845000\n",
+      "epoch [1144] L = 38.533320, acc = 0.845000\n",
+      "epoch [1145] L = 38.533190, acc = 0.845000\n",
+      "epoch [1146] L = 38.533060, acc = 0.845000\n",
+      "epoch [1147] L = 38.532931, acc = 0.845000\n",
+      "epoch [1148] L = 38.532802, acc = 0.845000\n",
+      "epoch [1149] L = 38.532673, acc = 0.845000\n",
+      "epoch [1150] L = 38.532544, acc = 0.845000\n",
+      "epoch [1151] L = 38.532415, acc = 0.845000\n",
+      "epoch [1152] L = 38.532287, acc = 0.845000\n",
+      "epoch [1153] L = 38.532158, acc = 0.845000\n",
+      "epoch [1154] L = 38.532030, acc = 0.845000\n",
+      "epoch [1155] L = 38.531902, acc = 0.845000\n",
+      "epoch [1156] L = 38.531774, acc = 0.845000\n",
+      "epoch [1157] L = 38.531646, acc = 0.845000\n",
+      "epoch [1158] L = 38.531518, acc = 0.845000\n",
+      "epoch [1159] L = 38.531391, acc = 0.845000\n",
+      "epoch [1160] L = 38.531263, acc = 0.845000\n",
+      "epoch [1161] L = 38.531136, acc = 0.845000\n",
+      "epoch [1162] L = 38.531009, acc = 0.845000\n",
+      "epoch [1163] L = 38.530882, acc = 0.845000\n",
+      "epoch [1164] L = 38.530755, acc = 0.845000\n",
+      "epoch [1165] L = 38.530628, acc = 0.845000\n",
+      "epoch [1166] L = 38.530502, acc = 0.845000\n",
+      "epoch [1167] L = 38.530376, acc = 0.845000\n",
+      "epoch [1168] L = 38.530249, acc = 0.845000\n",
+      "epoch [1169] L = 38.530123, acc = 0.845000\n",
+      "epoch [1170] L = 38.529997, acc = 0.845000\n",
+      "epoch [1171] L = 38.529871, acc = 0.845000\n",
+      "epoch [1172] L = 38.529746, acc = 0.845000\n",
+      "epoch [1173] L = 38.529620, acc = 0.845000\n",
+      "epoch [1174] L = 38.529495, acc = 0.845000\n",
+      "epoch [1175] L = 38.529369, acc = 0.845000\n",
+      "epoch [1176] L = 38.529244, acc = 0.845000\n",
+      "epoch [1177] L = 38.529119, acc = 0.845000\n",
+      "epoch [1178] L = 38.528995, acc = 0.845000\n",
+      "epoch [1179] L = 38.528870, acc = 0.845000\n",
+      "epoch [1180] L = 38.528745, acc = 0.845000\n",
+      "epoch [1181] L = 38.528621, acc = 0.845000\n",
+      "epoch [1182] L = 38.528497, acc = 0.845000\n",
+      "epoch [1183] L = 38.528372, acc = 0.845000\n",
+      "epoch [1184] L = 38.528248, acc = 0.845000\n",
+      "epoch [1185] L = 38.528125, acc = 0.845000\n",
+      "epoch [1186] L = 38.528001, acc = 0.845000\n",
+      "epoch [1187] L = 38.527877, acc = 0.845000\n",
+      "epoch [1188] L = 38.527754, acc = 0.845000\n",
+      "epoch [1189] L = 38.527630, acc = 0.845000\n",
+      "epoch [1190] L = 38.527507, acc = 0.845000\n",
+      "epoch [1191] L = 38.527384, acc = 0.845000\n",
+      "epoch [1192] L = 38.527261, acc = 0.845000\n",
+      "epoch [1193] L = 38.527139, acc = 0.845000\n",
+      "epoch [1194] L = 38.527016, acc = 0.845000\n",
+      "epoch [1195] L = 38.526893, acc = 0.845000\n",
+      "epoch [1196] L = 38.526771, acc = 0.845000\n",
+      "epoch [1197] L = 38.526649, acc = 0.845000\n",
+      "epoch [1198] L = 38.526527, acc = 0.845000\n",
+      "epoch [1199] L = 38.526405, acc = 0.845000\n",
+      "epoch [1200] L = 38.526283, acc = 0.845000\n",
+      "epoch [1201] L = 38.526161, acc = 0.845000\n",
+      "epoch [1202] L = 38.526040, acc = 0.845000\n",
+      "epoch [1203] L = 38.525918, acc = 0.845000\n",
+      "epoch [1204] L = 38.525797, acc = 0.845000\n",
+      "epoch [1205] L = 38.525676, acc = 0.845000\n",
+      "epoch [1206] L = 38.525555, acc = 0.845000\n",
+      "epoch [1207] L = 38.525434, acc = 0.845000\n",
+      "epoch [1208] L = 38.525313, acc = 0.845000\n",
+      "epoch [1209] L = 38.525192, acc = 0.845000\n",
+      "epoch [1210] L = 38.525072, acc = 0.845000\n",
+      "epoch [1211] L = 38.524951, acc = 0.845000\n",
+      "epoch [1212] L = 38.524831, acc = 0.845000\n",
+      "epoch [1213] L = 38.524711, acc = 0.845000\n",
+      "epoch [1214] L = 38.524591, acc = 0.845000\n",
+      "epoch [1215] L = 38.524471, acc = 0.845000\n",
+      "epoch [1216] L = 38.524351, acc = 0.845000\n",
+      "epoch [1217] L = 38.524231, acc = 0.845000\n",
+      "epoch [1218] L = 38.524112, acc = 0.845000\n",
+      "epoch [1219] L = 38.523993, acc = 0.845000\n",
+      "epoch [1220] L = 38.523873, acc = 0.845000\n",
+      "epoch [1221] L = 38.523754, acc = 0.845000\n",
+      "epoch [1222] L = 38.523635, acc = 0.845000\n",
+      "epoch [1223] L = 38.523516, acc = 0.845000\n",
+      "epoch [1224] L = 38.523397, acc = 0.845000\n",
+      "epoch [1225] L = 38.523279, acc = 0.845000\n",
+      "epoch [1226] L = 38.523160, acc = 0.845000\n",
+      "epoch [1227] L = 38.523042, acc = 0.845000\n",
+      "epoch [1228] L = 38.522924, acc = 0.845000\n",
+      "epoch [1229] L = 38.522806, acc = 0.845000\n",
+      "epoch [1230] L = 38.522688, acc = 0.845000\n",
+      "epoch [1231] L = 38.522570, acc = 0.845000\n",
+      "epoch [1232] L = 38.522452, acc = 0.845000\n",
+      "epoch [1233] L = 38.522334, acc = 0.845000\n",
+      "epoch [1234] L = 38.522217, acc = 0.845000\n",
+      "epoch [1235] L = 38.522099, acc = 0.845000\n",
+      "epoch [1236] L = 38.521982, acc = 0.845000\n",
+      "epoch [1237] L = 38.521865, acc = 0.845000\n",
+      "epoch [1238] L = 38.521748, acc = 0.845000\n",
+      "epoch [1239] L = 38.521631, acc = 0.845000\n",
+      "epoch [1240] L = 38.521514, acc = 0.845000\n",
+      "epoch [1241] L = 38.521397, acc = 0.845000\n",
+      "epoch [1242] L = 38.521281, acc = 0.845000\n",
+      "epoch [1243] L = 38.521164, acc = 0.845000\n",
+      "epoch [1244] L = 38.521048, acc = 0.845000\n",
+      "epoch [1245] L = 38.520932, acc = 0.845000\n",
+      "epoch [1246] L = 38.520816, acc = 0.845000\n",
+      "epoch [1247] L = 38.520700, acc = 0.845000\n",
+      "epoch [1248] L = 38.520584, acc = 0.845000\n",
+      "epoch [1249] L = 38.520468, acc = 0.845000\n",
+      "epoch [1250] L = 38.520353, acc = 0.845000\n",
+      "epoch [1251] L = 38.520237, acc = 0.845000\n",
+      "epoch [1252] L = 38.520122, acc = 0.845000\n",
+      "epoch [1253] L = 38.520006, acc = 0.845000\n",
+      "epoch [1254] L = 38.519891, acc = 0.845000\n",
+      "epoch [1255] L = 38.519776, acc = 0.845000\n",
+      "epoch [1256] L = 38.519661, acc = 0.845000\n",
+      "epoch [1257] L = 38.519547, acc = 0.845000\n",
+      "epoch [1258] L = 38.519432, acc = 0.845000\n",
+      "epoch [1259] L = 38.519317, acc = 0.845000\n",
+      "epoch [1260] L = 38.519203, acc = 0.845000\n",
+      "epoch [1261] L = 38.519089, acc = 0.845000\n",
+      "epoch [1262] L = 38.518974, acc = 0.845000\n",
+      "epoch [1263] L = 38.518860, acc = 0.845000\n",
+      "epoch [1264] L = 38.518746, acc = 0.845000\n",
+      "epoch [1265] L = 38.518632, acc = 0.845000\n",
+      "epoch [1266] L = 38.518519, acc = 0.845000\n",
+      "epoch [1267] L = 38.518405, acc = 0.845000\n",
+      "epoch [1268] L = 38.518291, acc = 0.845000\n",
+      "epoch [1269] L = 38.518178, acc = 0.845000\n",
+      "epoch [1270] L = 38.518065, acc = 0.845000\n",
+      "epoch [1271] L = 38.517951, acc = 0.845000\n",
+      "epoch [1272] L = 38.517838, acc = 0.845000\n",
+      "epoch [1273] L = 38.517725, acc = 0.845000\n",
+      "epoch [1274] L = 38.517612, acc = 0.845000\n",
+      "epoch [1275] L = 38.517500, acc = 0.845000\n",
+      "epoch [1276] L = 38.517387, acc = 0.845000\n",
+      "epoch [1277] L = 38.517274, acc = 0.845000\n",
+      "epoch [1278] L = 38.517162, acc = 0.845000\n",
+      "epoch [1279] L = 38.517050, acc = 0.845000\n",
+      "epoch [1280] L = 38.516937, acc = 0.845000\n",
+      "epoch [1281] L = 38.516825, acc = 0.845000\n",
+      "epoch [1282] L = 38.516713, acc = 0.845000\n",
+      "epoch [1283] L = 38.516601, acc = 0.845000\n",
+      "epoch [1284] L = 38.516490, acc = 0.845000\n",
+      "epoch [1285] L = 38.516378, acc = 0.845000\n",
+      "epoch [1286] L = 38.516266, acc = 0.845000\n",
+      "epoch [1287] L = 38.516155, acc = 0.845000\n",
+      "epoch [1288] L = 38.516044, acc = 0.845000\n",
+      "epoch [1289] L = 38.515932, acc = 0.845000\n",
+      "epoch [1290] L = 38.515821, acc = 0.845000\n",
+      "epoch [1291] L = 38.515710, acc = 0.845000\n",
+      "epoch [1292] L = 38.515599, acc = 0.845000\n",
+      "epoch [1293] L = 38.515488, acc = 0.845000\n",
+      "epoch [1294] L = 38.515378, acc = 0.845000\n",
+      "epoch [1295] L = 38.515267, acc = 0.845000\n",
+      "epoch [1296] L = 38.515157, acc = 0.845000\n",
+      "epoch [1297] L = 38.515046, acc = 0.845000\n",
+      "epoch [1298] L = 38.514936, acc = 0.845000\n",
+      "epoch [1299] L = 38.514826, acc = 0.845000\n",
+      "epoch [1300] L = 38.514716, acc = 0.845000\n",
+      "epoch [1301] L = 38.514606, acc = 0.845000\n",
+      "epoch [1302] L = 38.514496, acc = 0.845000\n",
+      "epoch [1303] L = 38.514386, acc = 0.845000\n",
+      "epoch [1304] L = 38.514276, acc = 0.845000\n",
+      "epoch [1305] L = 38.514167, acc = 0.845000\n",
+      "epoch [1306] L = 38.514057, acc = 0.845000\n",
+      "epoch [1307] L = 38.513948, acc = 0.845000\n",
+      "epoch [1308] L = 38.513839, acc = 0.845000\n",
+      "epoch [1309] L = 38.513729, acc = 0.845000\n",
+      "epoch [1310] L = 38.513620, acc = 0.845000\n",
+      "epoch [1311] L = 38.513511, acc = 0.845000\n",
+      "epoch [1312] L = 38.513403, acc = 0.845000\n",
+      "epoch [1313] L = 38.513294, acc = 0.845000\n",
+      "epoch [1314] L = 38.513185, acc = 0.845000\n",
+      "epoch [1315] L = 38.513077, acc = 0.845000\n",
+      "epoch [1316] L = 38.512968, acc = 0.845000\n",
+      "epoch [1317] L = 38.512860, acc = 0.845000\n",
+      "epoch [1318] L = 38.512752, acc = 0.845000\n",
+      "epoch [1319] L = 38.512643, acc = 0.845000\n",
+      "epoch [1320] L = 38.512535, acc = 0.845000\n",
+      "epoch [1321] L = 38.512427, acc = 0.845000\n",
+      "epoch [1322] L = 38.512320, acc = 0.845000\n",
+      "epoch [1323] L = 38.512212, acc = 0.845000\n",
+      "epoch [1324] L = 38.512104, acc = 0.845000\n",
+      "epoch [1325] L = 38.511997, acc = 0.845000\n",
+      "epoch [1326] L = 38.511889, acc = 0.845000\n",
+      "epoch [1327] L = 38.511782, acc = 0.845000\n",
+      "epoch [1328] L = 38.511674, acc = 0.845000\n",
+      "epoch [1329] L = 38.511567, acc = 0.845000\n",
+      "epoch [1330] L = 38.511460, acc = 0.845000\n",
+      "epoch [1331] L = 38.511353, acc = 0.845000\n",
+      "epoch [1332] L = 38.511246, acc = 0.845000\n",
+      "epoch [1333] L = 38.511140, acc = 0.845000\n",
+      "epoch [1334] L = 38.511033, acc = 0.845000\n",
+      "epoch [1335] L = 38.510926, acc = 0.845000\n",
+      "epoch [1336] L = 38.510820, acc = 0.845000\n",
+      "epoch [1337] L = 38.510713, acc = 0.845000\n",
+      "epoch [1338] L = 38.510607, acc = 0.845000\n",
+      "epoch [1339] L = 38.510501, acc = 0.845000\n",
+      "epoch [1340] L = 38.510395, acc = 0.845000\n",
+      "epoch [1341] L = 38.510289, acc = 0.845000\n",
+      "epoch [1342] L = 38.510183, acc = 0.845000\n",
+      "epoch [1343] L = 38.510077, acc = 0.845000\n",
+      "epoch [1344] L = 38.509971, acc = 0.845000\n",
+      "epoch [1345] L = 38.509866, acc = 0.845000\n",
+      "epoch [1346] L = 38.509760, acc = 0.845000\n",
+      "epoch [1347] L = 38.509655, acc = 0.845000\n",
+      "epoch [1348] L = 38.509549, acc = 0.845000\n",
+      "epoch [1349] L = 38.509444, acc = 0.845000\n",
+      "epoch [1350] L = 38.509339, acc = 0.845000\n",
+      "epoch [1351] L = 38.509234, acc = 0.845000\n",
+      "epoch [1352] L = 38.509129, acc = 0.845000\n",
+      "epoch [1353] L = 38.509024, acc = 0.845000\n",
+      "epoch [1354] L = 38.508919, acc = 0.845000\n",
+      "epoch [1355] L = 38.508814, acc = 0.845000\n",
+      "epoch [1356] L = 38.508710, acc = 0.845000\n",
+      "epoch [1357] L = 38.508605, acc = 0.845000\n",
+      "epoch [1358] L = 38.508501, acc = 0.845000\n",
+      "epoch [1359] L = 38.508396, acc = 0.845000\n",
+      "epoch [1360] L = 38.508292, acc = 0.845000\n",
+      "epoch [1361] L = 38.508188, acc = 0.845000\n",
+      "epoch [1362] L = 38.508084, acc = 0.845000\n",
+      "epoch [1363] L = 38.507980, acc = 0.845000\n",
+      "epoch [1364] L = 38.507876, acc = 0.845000\n",
+      "epoch [1365] L = 38.507772, acc = 0.845000\n",
+      "epoch [1366] L = 38.507669, acc = 0.845000\n",
+      "epoch [1367] L = 38.507565, acc = 0.845000\n",
+      "epoch [1368] L = 38.507461, acc = 0.845000\n",
+      "epoch [1369] L = 38.507358, acc = 0.845000\n",
+      "epoch [1370] L = 38.507255, acc = 0.845000\n",
+      "epoch [1371] L = 38.507151, acc = 0.845000\n",
+      "epoch [1372] L = 38.507048, acc = 0.845000\n",
+      "epoch [1373] L = 38.506945, acc = 0.845000\n",
+      "epoch [1374] L = 38.506842, acc = 0.845000\n",
+      "epoch [1375] L = 38.506739, acc = 0.845000\n",
+      "epoch [1376] L = 38.506636, acc = 0.845000\n",
+      "epoch [1377] L = 38.506534, acc = 0.845000\n",
+      "epoch [1378] L = 38.506431, acc = 0.845000\n",
+      "epoch [1379] L = 38.506328, acc = 0.845000\n",
+      "epoch [1380] L = 38.506226, acc = 0.845000\n",
+      "epoch [1381] L = 38.506123, acc = 0.845000\n",
+      "epoch [1382] L = 38.506021, acc = 0.845000\n",
+      "epoch [1383] L = 38.505919, acc = 0.845000\n",
+      "epoch [1384] L = 38.505817, acc = 0.845000\n",
+      "epoch [1385] L = 38.505715, acc = 0.845000\n",
+      "epoch [1386] L = 38.505613, acc = 0.845000\n",
+      "epoch [1387] L = 38.505511, acc = 0.845000\n",
+      "epoch [1388] L = 38.505409, acc = 0.845000\n",
+      "epoch [1389] L = 38.505307, acc = 0.845000\n",
+      "epoch [1390] L = 38.505206, acc = 0.845000\n",
+      "epoch [1391] L = 38.505104, acc = 0.845000\n",
+      "epoch [1392] L = 38.505003, acc = 0.845000\n",
+      "epoch [1393] L = 38.504901, acc = 0.845000\n",
+      "epoch [1394] L = 38.504800, acc = 0.845000\n",
+      "epoch [1395] L = 38.504699, acc = 0.845000\n",
+      "epoch [1396] L = 38.504598, acc = 0.845000\n",
+      "epoch [1397] L = 38.504497, acc = 0.845000\n",
+      "epoch [1398] L = 38.504396, acc = 0.845000\n",
+      "epoch [1399] L = 38.504295, acc = 0.845000\n",
+      "epoch [1400] L = 38.504194, acc = 0.845000\n",
+      "epoch [1401] L = 38.504093, acc = 0.845000\n",
+      "epoch [1402] L = 38.503993, acc = 0.845000\n",
+      "epoch [1403] L = 38.503892, acc = 0.845000\n",
+      "epoch [1404] L = 38.503792, acc = 0.845000\n",
+      "epoch [1405] L = 38.503691, acc = 0.845000\n",
+      "epoch [1406] L = 38.503591, acc = 0.845000\n",
+      "epoch [1407] L = 38.503491, acc = 0.845000\n",
+      "epoch [1408] L = 38.503391, acc = 0.845000\n",
+      "epoch [1409] L = 38.503291, acc = 0.845000\n",
+      "epoch [1410] L = 38.503191, acc = 0.845000\n",
+      "epoch [1411] L = 38.503091, acc = 0.845000\n",
+      "epoch [1412] L = 38.502991, acc = 0.845000\n",
+      "epoch [1413] L = 38.502891, acc = 0.845000\n",
+      "epoch [1414] L = 38.502792, acc = 0.845000\n",
+      "epoch [1415] L = 38.502692, acc = 0.845000\n",
+      "epoch [1416] L = 38.502593, acc = 0.845000\n",
+      "epoch [1417] L = 38.502493, acc = 0.845000\n",
+      "epoch [1418] L = 38.502394, acc = 0.845000\n",
+      "epoch [1419] L = 38.502295, acc = 0.845000\n",
+      "epoch [1420] L = 38.502195, acc = 0.845000\n",
+      "epoch [1421] L = 38.502096, acc = 0.845000\n",
+      "epoch [1422] L = 38.501997, acc = 0.845000\n",
+      "epoch [1423] L = 38.501898, acc = 0.845000\n",
+      "epoch [1424] L = 38.501800, acc = 0.845000\n",
+      "epoch [1425] L = 38.501701, acc = 0.845000\n",
+      "epoch [1426] L = 38.501602, acc = 0.845000\n",
+      "epoch [1427] L = 38.501503, acc = 0.845000\n",
+      "epoch [1428] L = 38.501405, acc = 0.845000\n",
+      "epoch [1429] L = 38.501307, acc = 0.845000\n",
+      "epoch [1430] L = 38.501208, acc = 0.845000\n",
+      "epoch [1431] L = 38.501110, acc = 0.845000\n",
+      "epoch [1432] L = 38.501012, acc = 0.845000\n",
+      "epoch [1433] L = 38.500913, acc = 0.845000\n",
+      "epoch [1434] L = 38.500815, acc = 0.845000\n",
+      "epoch [1435] L = 38.500717, acc = 0.845000\n",
+      "epoch [1436] L = 38.500619, acc = 0.845000\n",
+      "epoch [1437] L = 38.500522, acc = 0.845000\n",
+      "epoch [1438] L = 38.500424, acc = 0.845000\n",
+      "epoch [1439] L = 38.500326, acc = 0.845000\n",
+      "epoch [1440] L = 38.500229, acc = 0.845000\n",
+      "epoch [1441] L = 38.500131, acc = 0.845000\n",
+      "epoch [1442] L = 38.500034, acc = 0.845000\n",
+      "epoch [1443] L = 38.499936, acc = 0.845000\n",
+      "epoch [1444] L = 38.499839, acc = 0.845000\n",
+      "epoch [1445] L = 38.499742, acc = 0.845000\n",
+      "epoch [1446] L = 38.499644, acc = 0.845000\n",
+      "epoch [1447] L = 38.499547, acc = 0.845000\n",
+      "epoch [1448] L = 38.499450, acc = 0.845000\n",
+      "epoch [1449] L = 38.499353, acc = 0.845000\n",
+      "epoch [1450] L = 38.499257, acc = 0.845000\n",
+      "epoch [1451] L = 38.499160, acc = 0.845000\n",
+      "epoch [1452] L = 38.499063, acc = 0.845000\n",
+      "epoch [1453] L = 38.498966, acc = 0.845000\n",
+      "epoch [1454] L = 38.498870, acc = 0.845000\n",
+      "epoch [1455] L = 38.498773, acc = 0.845000\n",
+      "epoch [1456] L = 38.498677, acc = 0.845000\n",
+      "epoch [1457] L = 38.498581, acc = 0.845000\n",
+      "epoch [1458] L = 38.498484, acc = 0.845000\n",
+      "epoch [1459] L = 38.498388, acc = 0.845000\n",
+      "epoch [1460] L = 38.498292, acc = 0.845000\n",
+      "epoch [1461] L = 38.498196, acc = 0.845000\n",
+      "epoch [1462] L = 38.498100, acc = 0.845000\n",
+      "epoch [1463] L = 38.498004, acc = 0.845000\n",
+      "epoch [1464] L = 38.497908, acc = 0.845000\n",
+      "epoch [1465] L = 38.497812, acc = 0.845000\n",
+      "epoch [1466] L = 38.497717, acc = 0.845000\n",
+      "epoch [1467] L = 38.497621, acc = 0.845000\n",
+      "epoch [1468] L = 38.497526, acc = 0.845000\n",
+      "epoch [1469] L = 38.497430, acc = 0.845000\n",
+      "epoch [1470] L = 38.497335, acc = 0.845000\n",
+      "epoch [1471] L = 38.497239, acc = 0.845000\n",
+      "epoch [1472] L = 38.497144, acc = 0.845000\n",
+      "epoch [1473] L = 38.497049, acc = 0.845000\n",
+      "epoch [1474] L = 38.496954, acc = 0.845000\n",
+      "epoch [1475] L = 38.496859, acc = 0.845000\n",
+      "epoch [1476] L = 38.496764, acc = 0.845000\n",
+      "epoch [1477] L = 38.496669, acc = 0.845000\n",
+      "epoch [1478] L = 38.496574, acc = 0.845000\n",
+      "epoch [1479] L = 38.496479, acc = 0.845000\n",
+      "epoch [1480] L = 38.496385, acc = 0.845000\n",
+      "epoch [1481] L = 38.496290, acc = 0.845000\n",
+      "epoch [1482] L = 38.496195, acc = 0.845000\n",
+      "epoch [1483] L = 38.496101, acc = 0.845000\n",
+      "epoch [1484] L = 38.496006, acc = 0.845000\n",
+      "epoch [1485] L = 38.495912, acc = 0.845000\n",
+      "epoch [1486] L = 38.495818, acc = 0.845000\n",
+      "epoch [1487] L = 38.495724, acc = 0.845000\n",
+      "epoch [1488] L = 38.495629, acc = 0.845000\n",
+      "epoch [1489] L = 38.495535, acc = 0.845000\n",
+      "epoch [1490] L = 38.495441, acc = 0.845000\n",
+      "epoch [1491] L = 38.495347, acc = 0.845000\n",
+      "epoch [1492] L = 38.495254, acc = 0.845000\n",
+      "epoch [1493] L = 38.495160, acc = 0.845000\n",
+      "epoch [1494] L = 38.495066, acc = 0.845000\n",
+      "epoch [1495] L = 38.494972, acc = 0.845000\n",
+      "epoch [1496] L = 38.494879, acc = 0.845000\n",
+      "epoch [1497] L = 38.494785, acc = 0.845000\n",
+      "epoch [1498] L = 38.494692, acc = 0.845000\n",
+      "epoch [1499] L = 38.494598, acc = 0.845000\n",
+      "epoch [1500] L = 38.494505, acc = 0.845000\n",
+      "epoch [1501] L = 38.494412, acc = 0.845000\n",
+      "epoch [1502] L = 38.494319, acc = 0.845000\n",
+      "epoch [1503] L = 38.494225, acc = 0.845000\n",
+      "epoch [1504] L = 38.494132, acc = 0.845000\n",
+      "epoch [1505] L = 38.494039, acc = 0.845000\n",
+      "epoch [1506] L = 38.493946, acc = 0.845000\n",
+      "epoch [1507] L = 38.493854, acc = 0.845000\n",
+      "epoch [1508] L = 38.493761, acc = 0.845000\n",
+      "epoch [1509] L = 38.493668, acc = 0.845000\n",
+      "epoch [1510] L = 38.493575, acc = 0.845000\n",
+      "epoch [1511] L = 38.493483, acc = 0.845000\n",
+      "epoch [1512] L = 38.493390, acc = 0.845000\n",
+      "epoch [1513] L = 38.493298, acc = 0.845000\n",
+      "epoch [1514] L = 38.493205, acc = 0.845000\n",
+      "epoch [1515] L = 38.493113, acc = 0.845000\n",
+      "epoch [1516] L = 38.493021, acc = 0.845000\n",
+      "epoch [1517] L = 38.492929, acc = 0.845000\n",
+      "epoch [1518] L = 38.492836, acc = 0.845000\n",
+      "epoch [1519] L = 38.492744, acc = 0.845000\n",
+      "epoch [1520] L = 38.492652, acc = 0.845000\n",
+      "epoch [1521] L = 38.492560, acc = 0.845000\n",
+      "epoch [1522] L = 38.492468, acc = 0.845000\n",
+      "epoch [1523] L = 38.492377, acc = 0.845000\n",
+      "epoch [1524] L = 38.492285, acc = 0.845000\n",
+      "epoch [1525] L = 38.492193, acc = 0.845000\n",
+      "epoch [1526] L = 38.492101, acc = 0.845000\n",
+      "epoch [1527] L = 38.492010, acc = 0.845000\n",
+      "epoch [1528] L = 38.491918, acc = 0.845000\n",
+      "epoch [1529] L = 38.491827, acc = 0.845000\n",
+      "epoch [1530] L = 38.491736, acc = 0.845000\n",
+      "epoch [1531] L = 38.491644, acc = 0.845000\n",
+      "epoch [1532] L = 38.491553, acc = 0.845000\n",
+      "epoch [1533] L = 38.491462, acc = 0.845000\n",
+      "epoch [1534] L = 38.491371, acc = 0.845000\n",
+      "epoch [1535] L = 38.491280, acc = 0.845000\n",
+      "epoch [1536] L = 38.491189, acc = 0.845000\n",
+      "epoch [1537] L = 38.491098, acc = 0.845000\n",
+      "epoch [1538] L = 38.491007, acc = 0.845000\n",
+      "epoch [1539] L = 38.490916, acc = 0.845000\n",
+      "epoch [1540] L = 38.490825, acc = 0.845000\n",
+      "epoch [1541] L = 38.490735, acc = 0.845000\n",
+      "epoch [1542] L = 38.490644, acc = 0.845000\n",
+      "epoch [1543] L = 38.490553, acc = 0.845000\n",
+      "epoch [1544] L = 38.490463, acc = 0.845000\n",
+      "epoch [1545] L = 38.490372, acc = 0.845000\n",
+      "epoch [1546] L = 38.490282, acc = 0.845000\n",
+      "epoch [1547] L = 38.490192, acc = 0.845000\n",
+      "epoch [1548] L = 38.490101, acc = 0.845000\n",
+      "epoch [1549] L = 38.490011, acc = 0.845000\n",
+      "epoch [1550] L = 38.489921, acc = 0.845000\n",
+      "epoch [1551] L = 38.489831, acc = 0.845000\n",
+      "epoch [1552] L = 38.489741, acc = 0.845000\n",
+      "epoch [1553] L = 38.489651, acc = 0.845000\n",
+      "epoch [1554] L = 38.489561, acc = 0.845000\n",
+      "epoch [1555] L = 38.489471, acc = 0.845000\n",
+      "epoch [1556] L = 38.489381, acc = 0.845000\n",
+      "epoch [1557] L = 38.489292, acc = 0.845000\n",
+      "epoch [1558] L = 38.489202, acc = 0.845000\n",
+      "epoch [1559] L = 38.489112, acc = 0.845000\n",
+      "epoch [1560] L = 38.489023, acc = 0.845000\n",
+      "epoch [1561] L = 38.488933, acc = 0.845000\n",
+      "epoch [1562] L = 38.488844, acc = 0.845000\n",
+      "epoch [1563] L = 38.488755, acc = 0.845000\n",
+      "epoch [1564] L = 38.488665, acc = 0.845000\n",
+      "epoch [1565] L = 38.488576, acc = 0.845000\n",
+      "epoch [1566] L = 38.488487, acc = 0.845000\n",
+      "epoch [1567] L = 38.488398, acc = 0.845000\n",
+      "epoch [1568] L = 38.488309, acc = 0.845000\n",
+      "epoch [1569] L = 38.488220, acc = 0.845000\n",
+      "epoch [1570] L = 38.488131, acc = 0.845000\n",
+      "epoch [1571] L = 38.488042, acc = 0.845000\n",
+      "epoch [1572] L = 38.487953, acc = 0.845000\n",
+      "epoch [1573] L = 38.487864, acc = 0.845000\n",
+      "epoch [1574] L = 38.487775, acc = 0.845000\n",
+      "epoch [1575] L = 38.487687, acc = 0.845000\n",
+      "epoch [1576] L = 38.487598, acc = 0.845000\n",
+      "epoch [1577] L = 38.487510, acc = 0.845000\n",
+      "epoch [1578] L = 38.487421, acc = 0.845000\n",
+      "epoch [1579] L = 38.487333, acc = 0.845000\n",
+      "epoch [1580] L = 38.487244, acc = 0.845000\n",
+      "epoch [1581] L = 38.487156, acc = 0.845000\n",
+      "epoch [1582] L = 38.487068, acc = 0.845000\n",
+      "epoch [1583] L = 38.486979, acc = 0.845000\n",
+      "epoch [1584] L = 38.486891, acc = 0.845000\n",
+      "epoch [1585] L = 38.486803, acc = 0.845000\n",
+      "epoch [1586] L = 38.486715, acc = 0.845000\n",
+      "epoch [1587] L = 38.486627, acc = 0.845000\n",
+      "epoch [1588] L = 38.486539, acc = 0.845000\n",
+      "epoch [1589] L = 38.486451, acc = 0.845000\n",
+      "epoch [1590] L = 38.486364, acc = 0.845000\n",
+      "epoch [1591] L = 38.486276, acc = 0.845000\n",
+      "epoch [1592] L = 38.486188, acc = 0.845000\n",
+      "epoch [1593] L = 38.486101, acc = 0.845000\n",
+      "epoch [1594] L = 38.486013, acc = 0.845000\n",
+      "epoch [1595] L = 38.485925, acc = 0.845000\n",
+      "epoch [1596] L = 38.485838, acc = 0.845000\n",
+      "epoch [1597] L = 38.485750, acc = 0.845000\n",
+      "epoch [1598] L = 38.485663, acc = 0.845000\n",
+      "epoch [1599] L = 38.485576, acc = 0.845000\n",
+      "epoch [1600] L = 38.485489, acc = 0.845000\n",
+      "epoch [1601] L = 38.485401, acc = 0.845000\n",
+      "epoch [1602] L = 38.485314, acc = 0.845000\n",
+      "epoch [1603] L = 38.485227, acc = 0.845000\n",
+      "epoch [1604] L = 38.485140, acc = 0.845000\n",
+      "epoch [1605] L = 38.485053, acc = 0.845000\n",
+      "epoch [1606] L = 38.484966, acc = 0.845000\n",
+      "epoch [1607] L = 38.484879, acc = 0.845000\n",
+      "epoch [1608] L = 38.484792, acc = 0.845000\n",
+      "epoch [1609] L = 38.484706, acc = 0.845000\n",
+      "epoch [1610] L = 38.484619, acc = 0.845000\n",
+      "epoch [1611] L = 38.484532, acc = 0.845000\n",
+      "epoch [1612] L = 38.484446, acc = 0.845000\n",
+      "epoch [1613] L = 38.484359, acc = 0.845000\n",
+      "epoch [1614] L = 38.484273, acc = 0.845000\n",
+      "epoch [1615] L = 38.484186, acc = 0.845000\n",
+      "epoch [1616] L = 38.484100, acc = 0.845000\n",
+      "epoch [1617] L = 38.484014, acc = 0.845000\n",
+      "epoch [1618] L = 38.483927, acc = 0.845000\n",
+      "epoch [1619] L = 38.483841, acc = 0.845000\n",
+      "epoch [1620] L = 38.483755, acc = 0.845000\n",
+      "epoch [1621] L = 38.483669, acc = 0.845000\n",
+      "epoch [1622] L = 38.483583, acc = 0.845000\n",
+      "epoch [1623] L = 38.483497, acc = 0.845000\n",
+      "epoch [1624] L = 38.483411, acc = 0.845000\n",
+      "epoch [1625] L = 38.483325, acc = 0.845000\n",
+      "epoch [1626] L = 38.483239, acc = 0.845000\n",
+      "epoch [1627] L = 38.483153, acc = 0.845000\n",
+      "epoch [1628] L = 38.483067, acc = 0.845000\n",
+      "epoch [1629] L = 38.482982, acc = 0.845000\n",
+      "epoch [1630] L = 38.482896, acc = 0.845000\n",
+      "epoch [1631] L = 38.482810, acc = 0.845000\n",
+      "epoch [1632] L = 38.482725, acc = 0.845000\n",
+      "epoch [1633] L = 38.482639, acc = 0.845000\n",
+      "epoch [1634] L = 38.482554, acc = 0.845000\n",
+      "epoch [1635] L = 38.482469, acc = 0.845000\n",
+      "epoch [1636] L = 38.482383, acc = 0.845000\n",
+      "epoch [1637] L = 38.482298, acc = 0.845000\n",
+      "epoch [1638] L = 38.482213, acc = 0.845000\n",
+      "epoch [1639] L = 38.482127, acc = 0.845000\n",
+      "epoch [1640] L = 38.482042, acc = 0.845000\n",
+      "epoch [1641] L = 38.481957, acc = 0.845000\n",
+      "epoch [1642] L = 38.481872, acc = 0.845000\n",
+      "epoch [1643] L = 38.481787, acc = 0.845000\n",
+      "epoch [1644] L = 38.481702, acc = 0.845000\n",
+      "epoch [1645] L = 38.481617, acc = 0.845000\n",
+      "epoch [1646] L = 38.481533, acc = 0.845000\n",
+      "epoch [1647] L = 38.481448, acc = 0.845000\n",
+      "epoch [1648] L = 38.481363, acc = 0.845000\n",
+      "epoch [1649] L = 38.481278, acc = 0.845000\n",
+      "epoch [1650] L = 38.481194, acc = 0.845000\n",
+      "epoch [1651] L = 38.481109, acc = 0.845000\n",
+      "epoch [1652] L = 38.481025, acc = 0.845000\n",
+      "epoch [1653] L = 38.480940, acc = 0.845000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch [1654] L = 38.480856, acc = 0.845000\n",
+      "epoch [1655] L = 38.480771, acc = 0.845000\n",
+      "epoch [1656] L = 38.480687, acc = 0.845000\n",
+      "epoch [1657] L = 38.480603, acc = 0.845000\n",
+      "epoch [1658] L = 38.480519, acc = 0.845000\n",
+      "epoch [1659] L = 38.480434, acc = 0.845000\n",
+      "epoch [1660] L = 38.480350, acc = 0.845000\n",
+      "epoch [1661] L = 38.480266, acc = 0.845000\n",
+      "epoch [1662] L = 38.480182, acc = 0.845000\n",
+      "epoch [1663] L = 38.480098, acc = 0.845000\n",
+      "epoch [1664] L = 38.480014, acc = 0.845000\n",
+      "epoch [1665] L = 38.479930, acc = 0.845000\n",
+      "epoch [1666] L = 38.479847, acc = 0.845000\n",
+      "epoch [1667] L = 38.479763, acc = 0.845000\n",
+      "epoch [1668] L = 38.479679, acc = 0.845000\n",
+      "epoch [1669] L = 38.479595, acc = 0.845000\n",
+      "epoch [1670] L = 38.479512, acc = 0.845000\n",
+      "epoch [1671] L = 38.479428, acc = 0.845000\n",
+      "epoch [1672] L = 38.479345, acc = 0.845000\n",
+      "epoch [1673] L = 38.479261, acc = 0.845000\n",
+      "epoch [1674] L = 38.479178, acc = 0.845000\n",
+      "epoch [1675] L = 38.479094, acc = 0.845000\n",
+      "epoch [1676] L = 38.479011, acc = 0.845000\n",
+      "epoch [1677] L = 38.478928, acc = 0.845000\n",
+      "epoch [1678] L = 38.478844, acc = 0.845000\n",
+      "epoch [1679] L = 38.478761, acc = 0.845000\n",
+      "epoch [1680] L = 38.478678, acc = 0.845000\n",
+      "epoch [1681] L = 38.478595, acc = 0.845000\n",
+      "epoch [1682] L = 38.478512, acc = 0.845000\n",
+      "epoch [1683] L = 38.478429, acc = 0.845000\n",
+      "epoch [1684] L = 38.478346, acc = 0.845000\n",
+      "epoch [1685] L = 38.478263, acc = 0.845000\n",
+      "epoch [1686] L = 38.478180, acc = 0.845000\n",
+      "epoch [1687] L = 38.478097, acc = 0.845000\n",
+      "epoch [1688] L = 38.478014, acc = 0.845000\n",
+      "epoch [1689] L = 38.477932, acc = 0.845000\n",
+      "epoch [1690] L = 38.477849, acc = 0.845000\n",
+      "epoch [1691] L = 38.477766, acc = 0.845000\n",
+      "epoch [1692] L = 38.477684, acc = 0.845000\n",
+      "epoch [1693] L = 38.477601, acc = 0.845000\n",
+      "epoch [1694] L = 38.477519, acc = 0.845000\n",
+      "epoch [1695] L = 38.477436, acc = 0.845000\n",
+      "epoch [1696] L = 38.477354, acc = 0.845000\n",
+      "epoch [1697] L = 38.477271, acc = 0.845000\n",
+      "epoch [1698] L = 38.477189, acc = 0.845000\n",
+      "epoch [1699] L = 38.477107, acc = 0.845000\n",
+      "epoch [1700] L = 38.477025, acc = 0.845000\n",
+      "epoch [1701] L = 38.476942, acc = 0.845000\n",
+      "epoch [1702] L = 38.476860, acc = 0.845000\n",
+      "epoch [1703] L = 38.476778, acc = 0.845000\n",
+      "epoch [1704] L = 38.476696, acc = 0.845000\n",
+      "epoch [1705] L = 38.476614, acc = 0.845000\n",
+      "epoch [1706] L = 38.476532, acc = 0.845000\n",
+      "epoch [1707] L = 38.476450, acc = 0.845000\n",
+      "epoch [1708] L = 38.476368, acc = 0.845000\n",
+      "epoch [1709] L = 38.476287, acc = 0.845000\n",
+      "epoch [1710] L = 38.476205, acc = 0.845000\n",
+      "epoch [1711] L = 38.476123, acc = 0.845000\n",
+      "epoch [1712] L = 38.476041, acc = 0.845000\n",
+      "epoch [1713] L = 38.475960, acc = 0.845000\n",
+      "epoch [1714] L = 38.475878, acc = 0.845000\n",
+      "epoch [1715] L = 38.475797, acc = 0.845000\n",
+      "epoch [1716] L = 38.475715, acc = 0.845000\n",
+      "epoch [1717] L = 38.475634, acc = 0.845000\n",
+      "epoch [1718] L = 38.475552, acc = 0.845000\n",
+      "epoch [1719] L = 38.475471, acc = 0.845000\n",
+      "epoch [1720] L = 38.475390, acc = 0.845000\n",
+      "epoch [1721] L = 38.475308, acc = 0.845000\n",
+      "epoch [1722] L = 38.475227, acc = 0.845000\n",
+      "epoch [1723] L = 38.475146, acc = 0.845000\n",
+      "epoch [1724] L = 38.475065, acc = 0.845000\n",
+      "epoch [1725] L = 38.474984, acc = 0.845000\n",
+      "epoch [1726] L = 38.474903, acc = 0.845000\n",
+      "epoch [1727] L = 38.474822, acc = 0.845000\n",
+      "epoch [1728] L = 38.474741, acc = 0.845000\n",
+      "epoch [1729] L = 38.474660, acc = 0.845000\n",
+      "epoch [1730] L = 38.474579, acc = 0.845000\n",
+      "epoch [1731] L = 38.474498, acc = 0.845000\n",
+      "epoch [1732] L = 38.474417, acc = 0.845000\n",
+      "epoch [1733] L = 38.474337, acc = 0.845000\n",
+      "epoch [1734] L = 38.474256, acc = 0.845000\n",
+      "epoch [1735] L = 38.474175, acc = 0.845000\n",
+      "epoch [1736] L = 38.474095, acc = 0.845000\n",
+      "epoch [1737] L = 38.474014, acc = 0.845000\n",
+      "epoch [1738] L = 38.473934, acc = 0.845000\n",
+      "epoch [1739] L = 38.473853, acc = 0.845000\n",
+      "epoch [1740] L = 38.473773, acc = 0.845000\n",
+      "epoch [1741] L = 38.473692, acc = 0.845000\n",
+      "epoch [1742] L = 38.473612, acc = 0.845000\n",
+      "epoch [1743] L = 38.473532, acc = 0.845000\n",
+      "epoch [1744] L = 38.473451, acc = 0.845000\n",
+      "epoch [1745] L = 38.473371, acc = 0.845000\n",
+      "epoch [1746] L = 38.473291, acc = 0.845000\n",
+      "epoch [1747] L = 38.473211, acc = 0.845000\n",
+      "epoch [1748] L = 38.473131, acc = 0.845000\n",
+      "epoch [1749] L = 38.473051, acc = 0.845000\n",
+      "epoch [1750] L = 38.472970, acc = 0.845000\n",
+      "epoch [1751] L = 38.472891, acc = 0.845000\n",
+      "epoch [1752] L = 38.472811, acc = 0.845000\n",
+      "epoch [1753] L = 38.472731, acc = 0.845000\n",
+      "epoch [1754] L = 38.472651, acc = 0.845000\n",
+      "epoch [1755] L = 38.472571, acc = 0.845000\n",
+      "epoch [1756] L = 38.472491, acc = 0.845000\n",
+      "epoch [1757] L = 38.472412, acc = 0.845000\n",
+      "epoch [1758] L = 38.472332, acc = 0.845000\n",
+      "epoch [1759] L = 38.472252, acc = 0.845000\n",
+      "epoch [1760] L = 38.472173, acc = 0.845000\n",
+      "epoch [1761] L = 38.472093, acc = 0.845000\n",
+      "epoch [1762] L = 38.472014, acc = 0.845000\n",
+      "epoch [1763] L = 38.471934, acc = 0.845000\n",
+      "epoch [1764] L = 38.471855, acc = 0.845000\n",
+      "epoch [1765] L = 38.471775, acc = 0.845000\n",
+      "epoch [1766] L = 38.471696, acc = 0.845000\n",
+      "epoch [1767] L = 38.471617, acc = 0.845000\n",
+      "epoch [1768] L = 38.471537, acc = 0.845000\n",
+      "epoch [1769] L = 38.471458, acc = 0.845000\n",
+      "epoch [1770] L = 38.471379, acc = 0.845000\n",
+      "epoch [1771] L = 38.471300, acc = 0.845000\n",
+      "epoch [1772] L = 38.471221, acc = 0.845000\n",
+      "epoch [1773] L = 38.471142, acc = 0.845000\n",
+      "epoch [1774] L = 38.471063, acc = 0.845000\n",
+      "epoch [1775] L = 38.470984, acc = 0.845000\n",
+      "epoch [1776] L = 38.470905, acc = 0.845000\n",
+      "epoch [1777] L = 38.470826, acc = 0.845000\n",
+      "epoch [1778] L = 38.470747, acc = 0.845000\n",
+      "epoch [1779] L = 38.470668, acc = 0.845000\n",
+      "epoch [1780] L = 38.470589, acc = 0.845000\n",
+      "epoch [1781] L = 38.470511, acc = 0.845000\n",
+      "epoch [1782] L = 38.470432, acc = 0.845000\n",
+      "epoch [1783] L = 38.470353, acc = 0.845000\n",
+      "epoch [1784] L = 38.470275, acc = 0.845000\n",
+      "epoch [1785] L = 38.470196, acc = 0.845000\n",
+      "epoch [1786] L = 38.470118, acc = 0.845000\n",
+      "epoch [1787] L = 38.470039, acc = 0.845000\n",
+      "epoch [1788] L = 38.469961, acc = 0.845000\n",
+      "epoch [1789] L = 38.469882, acc = 0.845000\n",
+      "epoch [1790] L = 38.469804, acc = 0.845000\n",
+      "epoch [1791] L = 38.469725, acc = 0.845000\n",
+      "epoch [1792] L = 38.469647, acc = 0.845000\n",
+      "epoch [1793] L = 38.469569, acc = 0.845000\n",
+      "epoch [1794] L = 38.469491, acc = 0.845000\n",
+      "epoch [1795] L = 38.469413, acc = 0.845000\n",
+      "epoch [1796] L = 38.469334, acc = 0.845000\n",
+      "epoch [1797] L = 38.469256, acc = 0.845000\n",
+      "epoch [1798] L = 38.469178, acc = 0.845000\n",
+      "epoch [1799] L = 38.469100, acc = 0.845000\n",
+      "epoch [1800] L = 38.469022, acc = 0.845000\n",
+      "epoch [1801] L = 38.468944, acc = 0.845000\n",
+      "epoch [1802] L = 38.468866, acc = 0.845000\n",
+      "epoch [1803] L = 38.468789, acc = 0.845000\n",
+      "epoch [1804] L = 38.468711, acc = 0.845000\n",
+      "epoch [1805] L = 38.468633, acc = 0.845000\n",
+      "epoch [1806] L = 38.468555, acc = 0.845000\n",
+      "epoch [1807] L = 38.468477, acc = 0.845000\n",
+      "epoch [1808] L = 38.468400, acc = 0.845000\n",
+      "epoch [1809] L = 38.468322, acc = 0.845000\n",
+      "epoch [1810] L = 38.468245, acc = 0.845000\n",
+      "epoch [1811] L = 38.468167, acc = 0.845000\n",
+      "epoch [1812] L = 38.468089, acc = 0.845000\n",
+      "epoch [1813] L = 38.468012, acc = 0.845000\n",
+      "epoch [1814] L = 38.467935, acc = 0.845000\n",
+      "epoch [1815] L = 38.467857, acc = 0.845000\n",
+      "epoch [1816] L = 38.467780, acc = 0.845000\n",
+      "epoch [1817] L = 38.467702, acc = 0.845000\n",
+      "epoch [1818] L = 38.467625, acc = 0.845000\n",
+      "epoch [1819] L = 38.467548, acc = 0.845000\n",
+      "epoch [1820] L = 38.467471, acc = 0.845000\n",
+      "epoch [1821] L = 38.467394, acc = 0.845000\n",
+      "epoch [1822] L = 38.467316, acc = 0.845000\n",
+      "epoch [1823] L = 38.467239, acc = 0.845000\n",
+      "epoch [1824] L = 38.467162, acc = 0.845000\n",
+      "epoch [1825] L = 38.467085, acc = 0.845000\n",
+      "epoch [1826] L = 38.467008, acc = 0.845000\n",
+      "epoch [1827] L = 38.466931, acc = 0.845000\n",
+      "epoch [1828] L = 38.466854, acc = 0.845000\n",
+      "epoch [1829] L = 38.466777, acc = 0.845000\n",
+      "epoch [1830] L = 38.466701, acc = 0.845000\n",
+      "epoch [1831] L = 38.466624, acc = 0.845000\n",
+      "epoch [1832] L = 38.466547, acc = 0.845000\n",
+      "epoch [1833] L = 38.466470, acc = 0.845000\n",
+      "epoch [1834] L = 38.466394, acc = 0.845000\n",
+      "epoch [1835] L = 38.466317, acc = 0.845000\n",
+      "epoch [1836] L = 38.466240, acc = 0.845000\n",
+      "epoch [1837] L = 38.466164, acc = 0.845000\n",
+      "epoch [1838] L = 38.466087, acc = 0.845000\n",
+      "epoch [1839] L = 38.466011, acc = 0.845000\n",
+      "epoch [1840] L = 38.465934, acc = 0.845000\n",
+      "epoch [1841] L = 38.465858, acc = 0.845000\n",
+      "epoch [1842] L = 38.465781, acc = 0.845000\n",
+      "epoch [1843] L = 38.465705, acc = 0.845000\n",
+      "epoch [1844] L = 38.465629, acc = 0.845000\n",
+      "epoch [1845] L = 38.465553, acc = 0.845000\n",
+      "epoch [1846] L = 38.465476, acc = 0.845000\n",
+      "epoch [1847] L = 38.465400, acc = 0.845000\n",
+      "epoch [1848] L = 38.465324, acc = 0.845000\n",
+      "epoch [1849] L = 38.465248, acc = 0.845000\n",
+      "epoch [1850] L = 38.465172, acc = 0.845000\n",
+      "epoch [1851] L = 38.465096, acc = 0.845000\n",
+      "epoch [1852] L = 38.465020, acc = 0.845000\n",
+      "epoch [1853] L = 38.464944, acc = 0.845000\n",
+      "epoch [1854] L = 38.464868, acc = 0.845000\n",
+      "epoch [1855] L = 38.464792, acc = 0.845000\n",
+      "epoch [1856] L = 38.464716, acc = 0.845000\n",
+      "epoch [1857] L = 38.464640, acc = 0.845000\n",
+      "epoch [1858] L = 38.464564, acc = 0.845000\n",
+      "epoch [1859] L = 38.464488, acc = 0.845000\n",
+      "epoch [1860] L = 38.464413, acc = 0.845000\n",
+      "epoch [1861] L = 38.464337, acc = 0.845000\n",
+      "epoch [1862] L = 38.464261, acc = 0.845000\n",
+      "epoch [1863] L = 38.464186, acc = 0.845000\n",
+      "epoch [1864] L = 38.464110, acc = 0.845000\n",
+      "epoch [1865] L = 38.464034, acc = 0.845000\n",
+      "epoch [1866] L = 38.463959, acc = 0.845000\n",
+      "epoch [1867] L = 38.463883, acc = 0.845000\n",
+      "epoch [1868] L = 38.463808, acc = 0.845000\n",
+      "epoch [1869] L = 38.463733, acc = 0.845000\n",
+      "epoch [1870] L = 38.463657, acc = 0.845000\n",
+      "epoch [1871] L = 38.463582, acc = 0.845000\n",
+      "epoch [1872] L = 38.463506, acc = 0.845000\n",
+      "epoch [1873] L = 38.463431, acc = 0.845000\n",
+      "epoch [1874] L = 38.463356, acc = 0.845000\n",
+      "epoch [1875] L = 38.463281, acc = 0.845000\n",
+      "epoch [1876] L = 38.463206, acc = 0.845000\n",
+      "epoch [1877] L = 38.463130, acc = 0.845000\n",
+      "epoch [1878] L = 38.463055, acc = 0.845000\n",
+      "epoch [1879] L = 38.462980, acc = 0.845000\n",
+      "epoch [1880] L = 38.462905, acc = 0.845000\n",
+      "epoch [1881] L = 38.462830, acc = 0.845000\n",
+      "epoch [1882] L = 38.462755, acc = 0.845000\n",
+      "epoch [1883] L = 38.462680, acc = 0.845000\n",
+      "epoch [1884] L = 38.462605, acc = 0.845000\n",
+      "epoch [1885] L = 38.462530, acc = 0.845000\n",
+      "epoch [1886] L = 38.462456, acc = 0.845000\n",
+      "epoch [1887] L = 38.462381, acc = 0.845000\n",
+      "epoch [1888] L = 38.462306, acc = 0.845000\n",
+      "epoch [1889] L = 38.462231, acc = 0.845000\n",
+      "epoch [1890] L = 38.462157, acc = 0.845000\n",
+      "epoch [1891] L = 38.462082, acc = 0.845000\n",
+      "epoch [1892] L = 38.462007, acc = 0.845000\n",
+      "epoch [1893] L = 38.461933, acc = 0.845000\n",
+      "epoch [1894] L = 38.461858, acc = 0.845000\n",
+      "epoch [1895] L = 38.461784, acc = 0.845000\n",
+      "epoch [1896] L = 38.461709, acc = 0.845000\n",
+      "epoch [1897] L = 38.461635, acc = 0.845000\n",
+      "epoch [1898] L = 38.461560, acc = 0.845000\n",
+      "epoch [1899] L = 38.461486, acc = 0.845000\n",
+      "epoch [1900] L = 38.461412, acc = 0.845000\n",
+      "epoch [1901] L = 38.461337, acc = 0.845000\n",
+      "epoch [1902] L = 38.461263, acc = 0.845000\n",
+      "epoch [1903] L = 38.461189, acc = 0.845000\n",
+      "epoch [1904] L = 38.461114, acc = 0.845000\n",
+      "epoch [1905] L = 38.461040, acc = 0.845000\n",
+      "epoch [1906] L = 38.460966, acc = 0.845000\n",
+      "epoch [1907] L = 38.460892, acc = 0.845000\n",
+      "epoch [1908] L = 38.460818, acc = 0.845000\n",
+      "epoch [1909] L = 38.460744, acc = 0.845000\n",
+      "epoch [1910] L = 38.460670, acc = 0.845000\n",
+      "epoch [1911] L = 38.460596, acc = 0.845000\n",
+      "epoch [1912] L = 38.460522, acc = 0.845000\n",
+      "epoch [1913] L = 38.460448, acc = 0.845000\n",
+      "epoch [1914] L = 38.460374, acc = 0.845000\n",
+      "epoch [1915] L = 38.460300, acc = 0.845000\n",
+      "epoch [1916] L = 38.460226, acc = 0.845000\n",
+      "epoch [1917] L = 38.460153, acc = 0.845000\n",
+      "epoch [1918] L = 38.460079, acc = 0.845000\n",
+      "epoch [1919] L = 38.460005, acc = 0.845000\n",
+      "epoch [1920] L = 38.459931, acc = 0.845000\n",
+      "epoch [1921] L = 38.459858, acc = 0.845000\n",
+      "epoch [1922] L = 38.459784, acc = 0.845000\n",
+      "epoch [1923] L = 38.459711, acc = 0.845000\n",
+      "epoch [1924] L = 38.459637, acc = 0.845000\n",
+      "epoch [1925] L = 38.459563, acc = 0.845000\n",
+      "epoch [1926] L = 38.459490, acc = 0.845000\n",
+      "epoch [1927] L = 38.459416, acc = 0.845000\n",
+      "epoch [1928] L = 38.459343, acc = 0.845000\n",
+      "epoch [1929] L = 38.459270, acc = 0.845000\n",
+      "epoch [1930] L = 38.459196, acc = 0.845000\n",
+      "epoch [1931] L = 38.459123, acc = 0.845000\n",
+      "epoch [1932] L = 38.459050, acc = 0.845000\n",
+      "epoch [1933] L = 38.458976, acc = 0.845000\n",
+      "epoch [1934] L = 38.458903, acc = 0.845000\n",
+      "epoch [1935] L = 38.458830, acc = 0.845000\n",
+      "epoch [1936] L = 38.458757, acc = 0.845000\n",
+      "epoch [1937] L = 38.458684, acc = 0.845000\n",
+      "epoch [1938] L = 38.458610, acc = 0.845000\n",
+      "epoch [1939] L = 38.458537, acc = 0.845000\n",
+      "epoch [1940] L = 38.458464, acc = 0.845000\n",
+      "epoch [1941] L = 38.458391, acc = 0.845000\n",
+      "epoch [1942] L = 38.458318, acc = 0.845000\n",
+      "epoch [1943] L = 38.458245, acc = 0.845000\n",
+      "epoch [1944] L = 38.458172, acc = 0.845000\n",
+      "epoch [1945] L = 38.458100, acc = 0.845000\n",
+      "epoch [1946] L = 38.458027, acc = 0.845000\n",
+      "epoch [1947] L = 38.457954, acc = 0.845000\n",
+      "epoch [1948] L = 38.457881, acc = 0.845000\n",
+      "epoch [1949] L = 38.457808, acc = 0.845000\n",
+      "epoch [1950] L = 38.457736, acc = 0.845000\n",
+      "epoch [1951] L = 38.457663, acc = 0.845000\n",
+      "epoch [1952] L = 38.457590, acc = 0.845000\n",
+      "epoch [1953] L = 38.457518, acc = 0.845000\n",
+      "epoch [1954] L = 38.457445, acc = 0.845000\n",
+      "epoch [1955] L = 38.457372, acc = 0.845000\n",
+      "epoch [1956] L = 38.457300, acc = 0.845000\n",
+      "epoch [1957] L = 38.457227, acc = 0.845000\n",
+      "epoch [1958] L = 38.457155, acc = 0.845000\n",
+      "epoch [1959] L = 38.457082, acc = 0.845000\n",
+      "epoch [1960] L = 38.457010, acc = 0.845000\n",
+      "epoch [1961] L = 38.456938, acc = 0.845000\n",
+      "epoch [1962] L = 38.456865, acc = 0.845000\n",
+      "epoch [1963] L = 38.456793, acc = 0.845000\n",
+      "epoch [1964] L = 38.456721, acc = 0.845000\n",
+      "epoch [1965] L = 38.456648, acc = 0.845000\n",
+      "epoch [1966] L = 38.456576, acc = 0.845000\n",
+      "epoch [1967] L = 38.456504, acc = 0.845000\n",
+      "epoch [1968] L = 38.456432, acc = 0.845000\n",
+      "epoch [1969] L = 38.456360, acc = 0.845000\n",
+      "epoch [1970] L = 38.456287, acc = 0.845000\n",
+      "epoch [1971] L = 38.456215, acc = 0.845000\n",
+      "epoch [1972] L = 38.456143, acc = 0.845000\n",
+      "epoch [1973] L = 38.456071, acc = 0.845000\n",
+      "epoch [1974] L = 38.455999, acc = 0.845000\n",
+      "epoch [1975] L = 38.455927, acc = 0.845000\n",
+      "epoch [1976] L = 38.455855, acc = 0.845000\n",
+      "epoch [1977] L = 38.455784, acc = 0.845000\n",
+      "epoch [1978] L = 38.455712, acc = 0.845000\n",
+      "epoch [1979] L = 38.455640, acc = 0.845000\n",
+      "epoch [1980] L = 38.455568, acc = 0.845000\n",
+      "epoch [1981] L = 38.455496, acc = 0.845000\n",
+      "epoch [1982] L = 38.455425, acc = 0.845000\n",
+      "epoch [1983] L = 38.455353, acc = 0.845000\n",
+      "epoch [1984] L = 38.455281, acc = 0.845000\n",
+      "epoch [1985] L = 38.455209, acc = 0.845000\n",
+      "epoch [1986] L = 38.455138, acc = 0.845000\n",
+      "epoch [1987] L = 38.455066, acc = 0.845000\n",
+      "epoch [1988] L = 38.454995, acc = 0.845000\n",
+      "epoch [1989] L = 38.454923, acc = 0.845000\n",
+      "epoch [1990] L = 38.454852, acc = 0.845000\n",
+      "epoch [1991] L = 38.454780, acc = 0.845000\n",
+      "epoch [1992] L = 38.454709, acc = 0.845000\n",
+      "epoch [1993] L = 38.454637, acc = 0.845000\n",
+      "epoch [1994] L = 38.454566, acc = 0.845000\n",
+      "epoch [1995] L = 38.454494, acc = 0.845000\n",
+      "epoch [1996] L = 38.454423, acc = 0.845000\n",
+      "epoch [1997] L = 38.454352, acc = 0.845000\n",
+      "epoch [1998] L = 38.454281, acc = 0.845000\n",
+      "epoch [1999] L = 38.454209, acc = 0.845000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "y_true = np.array(nn.y).astype(float)\n",
+    "\n",
+    "# back-propagation\n",
+    "def backpropagation(n, X, y):\n",
+    "    for i in range(n.n_epoch):\n",
+    "        # forward to calculate each node's output\n",
+    "        forward(n, X)\n",
+    "        \n",
+    "        # print loss, accuracy\n",
+    "        L = np.sum((n.z2 - y)**2)\n",
+    "        \n",
+    "        y_pred = np.zeros(nn.z2.shape[0])\n",
+    "        y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1\n",
+    "        acc = accuracy_score(y_true, y_pred)\n",
+    "        \n",
+    "        print(\"epoch [%4d] L = %f, acc = %f\" % (i, L, acc))\n",
+    "        \n",
+    "        # calc weights update\n",
+    "        d2 = n.z2*(1-n.z2)*(y - n.z2)\n",
+    "        d1 = n.z1*(1-n.z1)*(np.dot(d2, n.W2.T))\n",
+    "        \n",
+    "        # update weights\n",
+    "        n.W2 += n.epsilon * np.dot(n.z1.T, d2)\n",
+    "        n.b2 += n.epsilon * np.sum(d2, axis=0)\n",
+    "        n.W1 += n.epsilon * np.dot(X.T, d1)\n",
+    "        n.b1 += n.epsilon * np.sum(d1, axis=0)\n",
+    "\n",
+    "nn.n_epoch = 2000\n",
+    "backpropagation(nn, X, t)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsnXd4VNXWh999pqeSTu+9V+kIIlKkioqK9arYsF8+e7le+7WLHS9NEFRAekcvvfcWCCWNkoT0MvXs748JgWEmkJ4A530eHpIz5+y9Msmss8/aa/2WkFKioaGhoXF9oVS2ARoaGhoaFY/m/DU0NDSuQzTnr6GhoXEdojl/DQ0NjesQzflraGhoXIdozl9DQ0PjOkRz/hoaV0AIMUUI8W7+172FENEVNK8UQjSuiLk0rj8056+hUQyklOuklM2udJ4Q4kEhxPqKsElDoyRozl/jukIIoa9sGzQ0qgKa89e4JhBCnBRCvCKEOCiESBNCTBZCmIUQfYUQCUKIl4QQZ4DJ+ecPFULsFkKkCyE2CiHaXjRWByHETiFElhBiNmC+6LW+QoiEi76vI4SYK4RIFkKcE0JMFEK0AL4HugshsoUQ6fnnmoQQnwgh4oQQZ4UQ3wshLBeNNUEIcVoIcUoI8Y/yf9c0rmc0569xLTEWGAg0ApoCr+cfrw6EAvWAcUKIDsB/gceAMOAHYEG+czYCfwLT86/5HRjtazIhhA5YBMQC9YFawCwp5SHgcWCTlDJASlkt/5IP8+1qDzTOP//N/LEGAf8EBgBNgJtL/3ZoaBSO5vw1riUmSinjpZSpwHvA3fnHVeAtKaVNSpkHjAN+kFJukVK6pJRTARvQLf+fAfhCSumQUv4BbCtkvhuAmsAEKWWOlNIqpfQZ5xdCiPx5n5dSpkops4D3gbvyT7kTmCyl3C+lzAHeLtU7oaFxBbT4p8a1RPxFX8fidswAyVJK60Wv1QMeEEI8fdExY/75EkiUnoqHsYXMVweIlVI6i2BbBOAH7HDfBwAQgC7/65rAjiLMqaFRJmgrf41riToXfV0XOJX/9aXStfHAe1LKahf985NS/gqcBmqJizx0/li+iAfqFrKJfOmcKUAe0OqiOYOllAH5r5/2Yb+GRrmhOX+Na4mnhBC1hRChwGvA7ELO+wl4XAjRVbjxF0LcKoQIBDYBTuAZIYRBCHEb7vCOL7bidtof5o9hFkL0zH/tLFA7fw8BKaWaP+/nQohIACFELSHEwPzzfwMeFEK0FEL4AW+V5o3Q0LgSmvPXuJaYCawAjgPHgHd9nSSl3A48CkwE0oAY4MH81+zAbfnfpwJjgLmFjOMChuHevI0DEvLPB1gDHADOCCFS8o+9lD/XZiFEJrAKaJY/1lLgi/zrYvL/19AoN4TWzEXjWkAIcRJ4REq5qrJt0dC4GtBW/hoaGhrXIZrz19DQ0LgO0cI+GhoaGtch2spfQ0ND4zqkyhZ5hYeHy/r161e2GRoaGhpXFTt27EiRUkZc6bwq6/zr16/P9u3bK9sMDQ0NjasKIUSRqsO1sI+GhobGdYjm/DU0NDSuQzTnr6GhoXEdojl/DQ0NjesQzflraGhoXIdozl+jVDhy8nDZ7JVthoaGRjGpsqmeGlWb1D3HWP/IJ6TuOYYQgtpDutLzpxcxhwdXtmkaGhpFQFv5axSbvKQ0ltz4HOd2HEE6XagOJwlLtrCs/4tUFbkQ1eVCqmplm6GhUWXRnL9GsTny8xJUu2fnQtXhJOvEGZI27K8kq9xkRMez5MbnmGoayDTLYP4e+x62tKxKtUlDoyqiOX+NYpN+MBaX1XecP+v46Qq25gLWcxks6jGes+v3gypRHU5i56xlWf9/VpknEg2NqoLm/DWKTUTXFuj8zF7HpaoS0qYBtvRsVJerwu06OnmZ+6Z0kaNX7U4yYxJJ2nigwu3R0KjKaM5fo9g0vv8WjMF+CL2u4JjObCSgXnWWD3yJWdVHMzN0JLv/Pb1C4+5p+0/gyvPxRCIlmUcSKswODY2rAc35axQbY5A/w7d9R4O7+mEI9sccFUKtwTeQHXsGW0oGqt2JIyuXvR/9yp73ZwIgpSRh2VbWPfwxG5/4nKTNB8vcrvCOTdH7eCIBqNa6fpnPp6FxNVNlm7l07txZaqqeVw9zWz1ExqE4r+OGID/uTpnH+oc+Ju7PDThzrKAIdGYjbf7vLjq8eX+Z2WDPyGZOswewpmRC/hOHzmQgrEszhvzvC4QQZTaXhkZVRQixQ0rZ+UrnaSt/jTIhJz7Z53FXno3TK3dccPwAqsSVa2Pfh7+SdfJMmdlgDA5g2NZvqTuiBzqLCWOwP03HDeWWpR9qjl9D4xK0Ii+NMqFaq3qkbDnsddwYEkjCiu04c23eFymCxOXbaP7YsDKzI6BuFP3n/KvMxtPQuFbRVv4aZUKXD8ehs5g8jun8THT+8FGMgX4InfefmlCUQmP0Ghoa5Yvm/DXKhOo3tuOWpR8S0a0FhkA/qrWqT59pr9DkwUE0uvdmFIPO+yIpqTuiR8Ubq6GhoYV9NMqO6n3aMnTjRK/jwU3r0O3rZ9g8/iuEQYdAIFWVm+b8C2OQfyVYqqGhoTl/jQqh6T8GU29kTxJX7kBnNFDzlk4Y/C2VbZaGxnWL5vw1KgxTaBANx/SrbDO8UB1OpJTojIbKNkVDo8LQnP81jCMrl+Rt0ZhCAwlt10hLd7yE3NPn2DDuUxKXbwcpierdhp4//ZOgRjUr2zQNjXJHc/5lgD0jmwNfzOHknLUYg/xp8fQoGtzZt1Kd7cGJ89j+0k8oRj3S6cK/dgQDln5IYP3qlWZTVUJ1uljc61ly4pOQTrcO0Zm1e1nUYzx3HJuBIaD8Q1Iuu4PjM1cTO3c9prAgmj8+jIiuLcp9Xg0N0Jx/qXHk5LHwhifJiU8uULpM3XOM5M0H6fr5U5Vi05l1e9n+8k+48my48tz59ZlHE1kx6CVuOzRFewIAEpZuwZaSXuD4gYLisxOz1tD0kVvLdX6Xzc6SG58n/cBJd/GbEJz4/W86f/AoLZ8eVa5za2iAlupZamKmrSQn8ZyHxLEzx0r0D4vISfRd9VreHPp6npfAmVRVchNTSN0dUyk2VTWyYk7hsjm9jjtzrKRXgAjc8ZlrLjh+AOm+8Wx/6Uds6dnlPr+Ghub8S8mp5dtw5Vq9jitGPcmbD1WCRZCXlO4ha3weodNhS9UamwCEtGmAYvR+8NUHWAhr37jc5z85Z+0Fx38RilFP0vp95T6/hobm/EuJX50In9WrUkos1UMrwSKoO7yHV7UtuLNawrs0qwSLqh41bupAUJNaKKYLGT7CoMMcHkz90b3LfX5TWBD4Cr9JiSFYq33QKH80519KWjw5wsOBgFu2wBIZQmSPVpViU7NxQwmoG+lxA9D7men0wcPlUlRlTclg/+d/sPHJL4iZtoK4hZvY/e4vxExfidPHU1FZkHc2le2v/MTCbk/x9z3vkrI9uljXC0Vh8F+f0fSRIRhDAjAE+dHonpsZunkimTGnOLc7xqshTdaJ06wa+QbT/IcwI2wkWyd8j7OQjmZXotljw9BZjF7H9YF+lfZ3o3F9oUk6lwFxCzay7h8fo9qdSJdKcPM69J/7DgH1oirNJkd2HtE/LiJu/gbMkdVo+fRtVO/TtsznObc7hqV9n0d1uNyby4oACQj3DUdnNnLrui8JblanzObMSUhmfsdxODLzUO2OAonoPtNeof5tJV+1p+0/weqRb5B3Ng2EQG8xceOvr1Pzpg5Yz2Uwt/mD2NOyCxrU6MxGovq0ZeCyj0o03/4v/mDnqz+7w08S9AFmBi7/mJDWDUr8M2hoFFXSWXP+ZYTqdJF+4CSGQAuBDa+fPPF5bR8hff+Jwk8QgrAOjRm+/fsym3PDuE85OmW5Z6YOYI6oxphTv6HofOgIXQGXzc7sOndhS8nwOK73MzH6yDRipq9k9zvTC7KnzqOzmBi25ZsSO2xbaiZn1+/HGOxPZK/WJbJdQ+Niiur8tVTPMkLR6wht16iyzahQrMnpV26PKCVp+09iTU7HHFGtTOZNXL7Ny/EDOHPyyIk9W6Kbb/yizag2h9dx1aVydNoKzm0/4uX4wf17T9t/osTO3xQaRN3hmridRsWjxfw1Soww6HHHeK5MWT5hmsKCfR5XXSrGagGXvdaZZ+Pshv2kHTjpYZM1KR3Vxw1FtTnITUwhpG1DFLN3jF6qKkFNaxfzJ9DQqHw0569RYkzVAojo3spntlMBQlCtVX0skSFlNm/rF+9A7+/ZB0Ax6ql5c0dMoUGFXnd06nJ+jRrNyltfYVHXp/izzcMFncSierfxeY0+wELN/h1pNm4ouktSQxWjnpA2DQnv2LSUP5GGRsWjOf+rBEdWLinbo8k9fa6yTfGgz7SX8a8dgT7AAj4yFxWDjhtnvFqmcza8pz8tn7kNndmIIdgfncVEZPdW9Jle+Dwp26PZ9OSXOLPzcGTm4sy1knE4nuW3TEBKSUjrBtS7rZfHTUUxGajWvC51hnbHr3ooQ9Z9SUT3lqAIFKOeBnf145ZlH5bpz6ahUVFoG75VHCklu9+Zxr6PZ6MYdKg2B7UG3UCfX16pMpLIqsvF3g9+Zc97v3jFzRWLkaHrvyKsQ5Myn9eWmknavhP41Y64ohjbuoc+Imb6SlA9/971ARYGrfqEiBuaI1WV6J8Wsf3lSTgyc91ZOELQ4a37afvS3QXXqE4XQhEIRVs7aVQ9tAbu1wjHZ6xi/39+w5Vnw5GZi8vmIHH5VjY+9nllm1aAotPhzM7zuWGKS3Lmf3vLZV5TaBDVb2xXJBXO3NOpXo4fQCgCa36Gj1AUEhZvcUt1SIlqc6Ba7ez59y+cnLsOe0Y2W1/8jt/q3c1v9e9hxxuTcfrYBNbQuBooE+cvhPivECJJCLG/kNeFEOIrIUSMEGKvEKJjWcx7PbD341lehVIuq4OTc9biyM4r8bhSVYmdv4E1d/6Lv+9+151BU4qnQHNUNXQ+NkQVox5zeOFx+NKQeyaVNXe8zVTzQKaaB/LXmHfIO5vq89w6Q7uh8/OuenbZHER0bQ64i9USV+7wuok5c63s/WgWi3o+w6Fv55N3+hy5Cckc+PS3grCRhsbVRlmt/KcAgy7z+mCgSf6/ccB3ZTTvNY81KcPncaEI7BklEwCTUvK/se+z9t73if1jLSdm/8Wa299my7PeLRiLSqN7+vsMgyg6hbqjepV43MJw2R0s6j6euPkbUe1OVLuT2HnrWdTjaVSHt2Bbk4cGEVAn0qOqVu9vpt1r92LOzx6ynctE0fvOs885eZqcuCSPG4PLaid1zzHOrtO0eDSuPsrE+Usp1wK+l1xuRgDTpJvNQDUhRI2ymPtap3rfdj6dqjHYH78aYSUaM2nDfuIXbfIQFnPmWDny8xLSD8WWaExLVCj9F7yLKTwYQ6AFfYAFv5phDFz5n3LZm4ibvxHbuUyPfH/pdGFNySBu4Sav8w3+FoZt/ZYObz9IRNcW1B7SlX6/v0X71+8tOCewUU0UH928hF6HpUYYTh9PWqrdWWRpiey4s2x4/DP+aHo/S/q9QMLSLUW6TkOjPKioIq9aQPxF3yfkHzt98UlCiHG4nwyoW7duBZlWten07j9IXL4NZ47V7eiEQGcx0u3rZ0q84ZiwdKtPzR2pShKXb6dai3olGrfmTR246/TvnNt5tKDorbw2RTMOxeLM8XbGzhwrGYfifF5jCPSjzYQxtJkwxufril5Ht6/Gs+Gxz3DlumP5ikGPIdCPBmP6khVzyut9U0wGAorQICc77izzO4zDkZWHdLrIiklkzdZDdHjnIdq8eOcVr9fQKGuqVIWvlPJH4EdwZ/tUsjlVgqDGtRi5+yf2fvgrZ9ftJbBxLdq+dBeR3Usu/mUI9kcxGNy6OBeh6HUYgvxKZa+i0xHRpXmpxigKwS3qofe3eK3G9f5mgluUfOHQaOzN+NeNZP9/ZpMdm0SNm9rTZsIY9P5m9n/yG+SJArlsoSgYA/2oM7TbFcfd8/7MAsd/Hleene0TfuDM33voM/0VTFcoUNPQKEvKLNVTCFEfWCSlbO3jtR+Av6WUv+Z/Hw30lVKevvTc82ipnuVHdnwSc5s/6CVXoPc3c8fxGShGPYYg/yrd8ctldzC3+QPkJKQUOFSh1+FfJ5LRh6egGMp+XZN24CRr7/+Q9ANuLaPwLs3pM/2VIrXGnNPsfjKPJvp8Tej1RPVqxeA1n5WpvRrXJ1VN22cBMF4IMQvoCmRczvFrlC8BdSLpM+1l1j340YXqXAlRfdryW727kS4XfrUi6PHdc9Qa2KVyjS0EndHA0E0T2fzMROLmbwAhqDuiJ92+Gn9Zx+/IySNu/kbs6dnUvLkjwU2LrjYa0qo+I3Z8j/VcBkKnK9ZK3a9WeKHOXzqdJG85TOaxU1rzeI0Ko0ycvxDiV6AvEC6ESADeAgwAUsrvgSXAECAGyAUeKot5NUpO/dF9qDWoC2f+3oOi1xE9aTEJS7YUtKPMPnmG1aPfYsjaL6qsfEHmsdP41QqnxTO30ejum65YSHZ24wFWDnkZKSXSqQKSpo/cStcvnirWU465EG2hy9H2pbtJ3nq4YC/hUhSjntyEZM35a1QYWoXvNUz6wZOcWrMbU2ggdUf0KDTrJu9sKr83GOvRhxgAIag/ujf9fnsLa0oGZzfsxxQSSFSv1pVe3br52Ykc/Xmpu8hKCHQmA+1evYd2r93r83zV4WRWzTuwncv0OK73N9N39pvUGdK13G0+/P0Ctjz3DardOxVVZzYyJmH2ZbWJNDSKQlUL+2jgLqzKPnkGQ6Bfmckb+5xHSjaM+5TjM9eAlAiDjk1PfcnA5R8TcYP3Zmx2bBKKyeDt/KUkbtEmlt/yf5xZtxedyeBuMxjkz8CV/6Fa88rJyErZHs2Rn5dcWEVLiSvPxp73ZtDwnv4ENvDOIj67Yb/P/P/zKa4V4fybPz6cWoO7Mr/9oziycgsqjvX+Zlo8NUJz/BoViibvUEEkLN3C7NpjmNf2EWbXvYtlAyZgTU4H3Lr4MdNWcGzGKmxppW+wHjtvPSdm/YUrz4bLaseZlYcjI4dVw1/zak0IENS0tm9pBkC1Oji1yl316sjMxZGVR+6pc6wY9FKlVbbG/rnB+0aVT/xi37nzvhz/edIPxfJn+0dZ0OUJon9c5PM98jVeUc67lMB6UYzcO4kmDw7Er2Y4IW0b0v2bZ+n0waPFHktDozRoK/8KIP3gSdbc8S+PeO+Z/+1hxeCXaf7kCDaP/wqhVwCBdKn0nvoSDW6/8YrjuuwO7GlZmMKDPTpAHZm02KOAq+D8PDspWw97pYmaqgXQfPxIor9dULSeu1JiS80iZethIrq2uPL5ZYzObEDoFKTq6XyForifTnwQ1bM10qV6v6AIso+fLgjFbDkUS+KK7dz0x9s+x8k4Es+Gxz4jad1+hE5Qd2Qvun/7bLH2AQLqRNJr0oTLnuOyO3BZ7RgC/ap01pXG1Yu28q8ADnw1z2tlLZ0u0g/FsempL9yr82wrzuw8XHk21j3wUcFTgS9Ul4ttL/3IzNAR/N5gLL9GjSb6p0UXXvcRUwZAgOrwvVrt8tE4On30aJHDUUJRsGfmFOncsqbBmH4+ZRikqlJvZE+f1+j9zPSe+jI6i8mt1gkoZiNCCI/3y5VrI3HZVlJ2HvEaw5aWxaIeT3N27T6kqqI6XMT9uYGl/V4os6cgZ66V9Y98woxqw5kZPoo5zR7g1JpdZTK2hsbFaM6/Asg6dsrnqlOqan7WySUIODl3XaHj7XztZw598yfOXHdYx56axZbnv+XknLUANLr3Zq9mJ+5xBRHdfK/UhRC0fGokN817x63NfwVUh5PI7q1I2nyQxb2eYVrAEH5vNJboSYvLPRwU3KQ2N3z2JDqzEb2/GX2ABZ3FRO9pL1/25lX/tt7cdnAy7V67l5bP3kbtITf4/L2oLpWkDQe8jsdMW1Gg+FlwrsNJ9smznPnfnjL52f6++12Oz1yNy2ovqARePfw10i7XJ1lDowRozr8CqHlTBw9BsfNIpwup+ropyEJj2qrDyaGJ871SBl25Nna/Mw2ARvcOILJna/QB7huAYjKg8zNx44zX0PnQrrmYyO4tsUSFFJrNIxQFnZ+Jrl88ScaReJbd/E+SNh7AlWsj+8QZtj73LXs//PWyc5QFzR8bxh0nZtDtq6fpPvEZxiTMLlKoLKBeFO3fuI+unz9FaNtGCIP3E4Ri1GOpHup1PG3/CZ+pmlJVr9zLuAhkx53l1ModXr97l83Bvk9ml3p8DY2L0WL+FUCzx4dxcOI8rM4MZH7YRe9nps6wbsQt3OTlUARQZ2h3n2PZM3IK3WjMiU8G3DINtyz5gFMrd5C4cjum8GAa3zsA/9oRV7RVCMGgVZ+wasTrZMYkuvcSFEHDe/qTG5+MObIazR8fRnjnZqwc+iquPE9H5cy1sveDmbR6/nb0PiSeyxJLVChNHrqcmGzhnJyzlr0f/lrw+7gYndFAnWHe7394p2acmPWX136KEIKQNiVr4H6e9EOxHJ263GdLTOlSST/oW69IQ6OkaM6/AjCFBDJi5w/sfvcX4hduwlgtgFbPjqbxgwPZ8uxEjk5ehjPXBsKd791mwphCi31MoYEYAv2w2bylnkPbNyr4WigKtQZ2KVGFbkC9KEbu/omMowk4MnMJbdvQZ9Vs6u4YjxBIAVKSdyqFwIZVs2DJlprJ2vs/RPXxdOVfN5IBiz/weeNqdO/N7P73NHdIJj9cpJgMhLRtSES3liWyRXW5WHv/B8T9uREEPp8shEFPZCHhOg2NkqI5/wrCEhVK96+fofvXz3gc7/rleBqM6ceJ2X8h9Doa3dOf8M7NCh1HKApdPh7HpvFfeTgKnZ+JzmWcLhjcpPZlXw9qUpvcU949haWqYo4qu4btZYUtLYvTf+0macN+hOKr4bCgwZi+hLSq7/N6Q4BbFnrri98Tv2gTOoOeRvffQqf3Hy5xRs6RSUuIn7/JS2epACHQW4y0eGoEh39YSOKK7QTUjaT548MJblZ0aQoNjUvRnH8RyTp5hm0Tvidx+Xb0fiaajRtKu9fvvWIM/UoIIYjq2Zqonl56eF44rXbi5q0nJy6Jls/cRsLSreTGJxHavjGd3n/EZwFXedL+rfvdoZ9LbkLNHr21yvQXPk/0T4vY8uw3KEY9qt3pe09F4nsD/iL8a0XQb9YbpbbHkZXL3o9msf8/swutQdAHWKjRrz3t3riPVSPeIDcxBWeOFaHXEf3jYvrNfqPQ8KCGxpXQnH8RsJ7LYOENT2BPzUaqKs7sPPZ/+jupe45x8/x3K8SG7PgkFncfjz0rF2d2nrtZSo0wboueWpBjnh2fxM7X/0vism0Ygvxo8fQoWo4fWW5SDDX6tufGX15ly/PfkpuQjM7PRMunR9HhXw+Wy3wlJW3/CbY89y0uq73QjXQAncVI/dv7lLs9qsPJ4l7PkHk0oXDH72em3x9vE96pCYcm/klO3FlcVne6sHS6cDldrHvoY+4684dHjYeGRlHRnH8ROPLjYpw5No/MHFeejVOrdpJ+OK5CZA42PPopeWfTCmLNzqw8sq1n2DbhB+qO6En6wVj2/2c2jqxcpEvFmpzOjlcnkb7/BD1/fLHc7Ko3shd1R/TElWdDZzZWuuaPL478d6lX7wJfmEICCet4eXG4siD2zw1knThT4Mx94cyzsea2N5EuFcWo93muy+Yg/cBJQts28jGChsblqXqf1CpI8pZDPmOyikFH2r7yz79WnS5Or97plZOuOpzETF3Ouvs/ZOebk7GnZ3uc48q1cWz6SnISk8vVPiEEej9zlXT8APaMbN/VvZdgS83iwOdzyt2epE0HfLaEBC68h1LiyrWh2hw4s31XXUuXC0Ng6ZrvaFy/VM1PaxWjWqv6KD5kA6RLJahx4RktOQnJrHv4Y2bVuoO5rf/BkZ+XlLwAqrANRemOH1OIc1PMRlJ3HyvZnNcI9Uf1Lqh5uByuPBtH/ruk3O0JqF8dncXkdVzodb43yn38zQhFIbhZHZ8idhoaRUFz/kWg+RPD0Rk9I2SK0UBIm4aFasjnJaUxv+NjHJu2krzTqWQcjGXLc9+w9YVviz2/otdRs39HnzngV0J1OIvUY/ZapvaQrlTv09Z31fMlXNxmsbxoNLa/tzyFIjCHB2MMCfR5jTDo3R3WAv3QB1oIqB9F/3n/LndbNa5dNOdfBPxrRzDor88I69gEoVNQjHrqje7NLUs/KPSag1/NK4i/n8eZYyX6h0XkJaUV24aeP72IpUYo+kALKMLtyK6QXagY9YS1b1xo6uL1glAU+s9/l95TXqL+nX1p8tAg/OtGep2nMxloeM/N5W6POSyYQWs+JahZbXRmI4rJQFiHJgxZ9yU1+3fwXXWs1zFi90/0/OlFBix6n9FHphFQL6rcbdW4dtGauRQTZ54NRa+7Yo/Yxb2fJWnDfq/jhmB/bvr9LWre3KnYc7vsDuL+3EDGkQRC2jRg20s/kHXEuzWg0CkInULtwV3pNfn/MFULIONoAinbogmoG0lkz9bXlFKkIzuPk3PWkncmlahebYjs0eqKP9+5XUdZetOLqA4nrlwb+gALgQ1rcOv6rzAUQduoLJBSkpOQjGLQ45cvJ5GTmMyf7R7FkZFTsHDQ+5tp+9pY2r18T4XYVVFkHT9FTkIKIW0aYCrkiUej+GjNXMoJvY9YrS+CGtciefNB701au9PnqrMo6IwGGtzZt+B7/9oRLOuf78Dy7OgDLPjXjaT/n+/gVz0MQ4AF1eXi77HvETdvPYpBh5TufrKD13yKX42wEtlRlTi3O4ZlN72I6nThstrRmQxE9W7DzfPfvewNOqxDE+44PoPjM1eTHXuWyO6tqDOsu0+10PJCCEFAHc+/Bf9aEe5q8HemcWrVLizVQ2gzYQz1R5d/CmpFYc/IZvWoN0nefAjFZEC1OWj53Gg6vVfyYjmN4qOt/MuJ1L3HWNTjaY8CKMWoJ6JrC4b874sym8d6LoNjv6wi6/hponq2pt6oXh5O7+DXc9n+yiQPO4ReR1Sv1gxe81mZ2VEZSCn5tfrt2C6Rv9b5mejy8ThaPDmykizTuByrRr5B4rJtHum3en8zPb7Vt7EgAAAgAElEQVR7jkb3DqhEy64Nirry12L+5URo20b0++0t/GqGuTXkTQZqDbqB/mVcFGYOC6bVs6Pp9uV4GtzZ12u1e+jbBV56MdLpImnTQawp3vpAVxP7P/nNy/GDO8X1yKSllWCRxpWwpWWRuHybV92FM8fKvk9/rySrSofLZmfvx7OY0/wB/mh6P7v/Pb1oTZEqGS3sU47UGdKVO+Nnk5OQjDHID2NwQIXb4KujF7hDDs7C9GSuEvZ/VrizcGTnsXXC9+iMBhre079SNr2zY8+ScTiOoKa1tZTMfOwZOSg6BV+JybarcDEipWTF4Fc8aoH2fjCT+EWbuHXj11W6+lpz/uWMr7huUZBScnr1To7NXI0Qgkb3DaD6je2KHROtN6oX0T8s9OruZakRWiSJ56qCy+5wZ1rlf5hUpwtrUuHdznLiznLgs98RisKBL/6g43sP0/q52yvM1rX3fUD8wk0FMe2at3Sm76w3yl3m+jy21ExUhxNLlHdfgsokoG4k+gCLW8X2IoReR62BV4xUVApSSlw2BzqTwevzd3bdPlK2HfYoAnVZ7aQfiiNx2Tbq3Nqtos0tMlrYp4xw2eyc/ON/HPxqLslbD5e6m9XGJz5n9ag3iZmynKNTlrNq2GslqhFo/8Z9WKqHovfLb+xi1KP3N9N7yktXxeZa2oGTLOr5NNP9hjDdbwh/3/MutvRsFL0Oc0ThfXNVu9Mt1OZSceXZ2fnqz+Ve6XyeXW9NIX7RZlxWO46MHFxWO6dWbGf7Sz+W+9w5Ccks6fs8s2rewW/172Fuq4dI2eHdkrKyEIpC9++eR+dnKihcVIwGjMH+tH/rgUq2zptjv67mt7p38UvArcwMH8W+T2Z7fLaTtxzCZfOW3nBm55G8+WBFmlpstA3fMiDjSDxL+jyHM8+Ganei6HVFyjgpjJQdR1hy43NesXqdxcSwrd8WO4ThyM4jZtoKzvy9m8DGtWj+2LCrIkc8LymNOc0ewJGZW1Dlqhj1hLRpwLCt33H4+wVsn/CjR3xV6BSfUg46PxNdP3+KZo/eWu52zwgdgT092+u43s/EvVmLy+2mq7pczGlyPznxSR7vgSHQj9FHp2GJrDoy2yk7jrD/09/IOnaK6n3b0+r52wvSXasKsfM38L+x73l8DvV+Ztq/eR9t/u8uAI7NXM3Gxz/3kuvQ+5np8unjNH9sWIXaDNqGb4Xy153vYE3OwJmV59ZiybFyZu1eDn49r0TjJSzZ4lPISzpdJCzZUuzxDAEWWjw5gn6/vUXn9x+5Khw/wJGfl7gb31/cM9fuJONwPMlbDtH88eF0/uhRTOHBoAgs1UOpeUtn8KHVLxSB4qN4qjwobJ/FmWf33fymjDi9aifWcxk+NaCOTl1ebvOWhPBOTek783WGbfmWLh+Nq3KOH2DXG5O9FmDOXCt7P5xZ0E2v3qhe6EwGL/kVYdDR8K5+FWZrSdCcfynJjk9y92+95EPtyrVx5OeSZZzo/c0+HZUw6CqsAKkqkLbvhG8JZiHIPJqIEIIWT43k7rNzuD9nCaMOTSbr2ClQvR2sdEnqDu9RAVZDZM9WPo+H39C8XMXvsmPP+nzqcVntZdJj+Hoj6+QZn8edObaClb7eYmLI2i8IaV0fndmIzmwkqFkdBq/5tFISPIqD5vxLiXS6ChVdUx1OpJTFjv83uLOvbychod7o3iUxs8ojpcR1SfpfeOdm7tjwpaiqR89cIQQ6k5Fdb04hO/as9/mKoM8vr2IKDSprs33S9cvxGAL9UPL1oIRBjz7AQvdvnrnClaUjvIvvDnD6AAtRvdqU69zXIoV1SjME+3moqVZrUY+ReyYx+sg0bjs8hdGHphSq+VWV0Jx/Mck9k8qpVTvIjHHLKgTUr45fDe9HVsVkQOgUppoGMtU0kDV3vE3e2dQizeFfO4Lek/8PncWEIcj9h6b3M9P319erVNy2LJCqyp4PZjIzbCTT/AbzW/27OTl3HQBN/jHY3RHsohuhzmwkoltLwto39hrr+K9r3GGiSxGC2hWYSRLapiEj9/9Mi/EjierdhuaPD2Pknp8I79i0XOcN69CE6n3aeiiGKkY9lqgQGoyp2iGIqkjnDx/1Ul/V+Zno+O7DPhdn/rUjCKh7dYRUQdvwLTJSVdn01JccnbIcndmIancS2aMV/ef+i/TDcSy/eYJbYiDPhs7fjLQ7kapa8Bgu9Dr860Qy+vCUIm8C2zNzOLVyBwhBrVs6l0nIJyM6nqzjpwhp2xD/WpWf6rnz7Skc+OR3j01bnZ+J/nPfodYtncmOO8vWF78jcek2FLOBJg8NouM7D/mU2ZgZMQrbuUyv40Kv4970BQUZT1czjqxcjvy8hPhFm7HUDKPl+FEe7TtddgcHPv2d6EmLcdkc1B/dhw5v3V9hTz3XGokrt7P9/34kIzoev1rhdHj7ARqNLX/xv9JQ1A1fzfkXkQNfzmHna//1cFKKyUC923rTd8ZrbpmFGavJiTuLlHDkp8XeGQCBFvpMfZl6I3sVe/7MY6fY8txETq3aic5spMmDg+j0/sNF1hqyZ+awesQbJG897O5ja3PQ8O6b6PHjC5VWiOKyO5gZPtJns5Lwrs0ZtumbYo236akvOPLzUs+aBkUQ1asNQ/7+vLTmVjr2zBwWdH6C3MQUd165ItCZjXSf+AxNHhxU2eZpVBG0bJ8y5uCXc71KtlWbg9i563Ba7W6ZhWdu44ZPnsAQYPHZqcmVayPjUFyx57amZLCw65MkLN2KanPgyMgh+oeFrBrxepHH2Pj45yRtPogrz1aQe3589l8c/HJuse0pK2ypWYU2TM+KOVXs8Tq+9wiBjWqiz39C0gdYMIcH0/u/E0plZ0WRl5TG9lcnsaDz46we/RZnNx7weP3QN3+Sm5B8oaBIdXf72vzMxKu+Wluj4rkuKnyllKXOrbZn5Ph+QZW4cq0elZshreq7qxgvuQHo/ExUK4HMwJGfFrtTzi7KYnFZ7SRtPEDqvuOEtml42eudVjuxc9d76am4cm0c/HourV+4o9g2lQXm8GAUk8FnRk9J3idTtQBG7plE/KJNpO45TmDDGtS/vU+Rn44qk9wzqcxv/yj29BxUu4Nzu2JIXL6NHt8/T+N8sbPYeet9vldCEZzbFUNUD99ZRoWRl5TG6dW70PubqXlL5wqrPtaoGlyzzt9ls7Pj1Z+J/nERzlwb4Z2b0m3iM0R0aX7li31Qs38HYueu92jiDuBfN9Kr+1LdkT0xv/QjOVZ7QWeo85rttYd0LfbcKTuO+PzQKzqdu4H3FZy/K89WaH65IzPX65hUVU6t2knCks2YwoJpdN8AAsuhG5ii19H+jfvY9eYUr5h/p3f/UeIx643sVaLQWmWy9/0Z2NOyUR35Iav8Hr5bnvmahmP6oRj0mMN8VzRLp4oppHhphfs//Y2db0xGGHQIIRBCMGDJB0R2L94NROMCLpfKqsWHWbP8KHabk87d6zLizrYEBFbNxcc1G/ZZe98HHP5+gbvgRkpStkWz7KYXC7J0ikvnDx/FEOyHYnT38hU6Bb2fmR4/vOD1VKEzGhi6eSL1R/dx5/5ajNS/80Zu3fB1ifTiQ9o2LLSHcGHpaBdjrBbgs5WjUBRqDvAMDapOF6uGv8aa29/i4Ffz2PPeL8xr9Y+CDJzLIaXk9N+72fvBTI5OXY6jkCblF9Pq+dvp+tVTBNSvjmIyENqxCQMWvldlUxNPrd7JwhueZHrQUP5s9whxCzeWybiJy7ddcPwXobpUMvJz9Fs+e5vXprXQKQQ2qkG1FvWKPFfy1sPsfHMKLqsdZ1Yejsxc7Bk5rLz1VVw2H3UVGkXi+8/W88eM3ZxJzCQ1JZc1S4/w9j+XYLN5/16rAmWy4SuEGAR8CeiASVLKDy95/UHgP8B5zztRSjnpcmOWZsM3Oz6Juc0e8FotC72Opg8Pocd3z5Vo3NzT5zjwxRySNh4guFkdWr94R7E+dCUlO/YMfzS+z6uAJ6xjE4Zv/75IY5xZu5eVQ17BZXcgnS4UkwG9v5nh277zUJw8NnM1Gx/7zKtKVR9g4e6zcwoNobjsDlYMfpmUrYfdTVUsRhS9nsF/fUZou0bF/ImrJokrtrN61JseIl46PxO9fp5Aw1KmUhbW+U0xGbjj+IyCxjt73p/Bnnd/QTHqkS4V/9oR3LL8o2KlGG54/DOOTFriVQxnCPLjxpmvU6cET6fXE9s3xbF47n4y0q20aludEWPaYre5ePPFxTjsnj2gTSY99zzSmb4DKi7vv8I6eQkhdMA3wAAgAdgmhFggpbxU1Wi2lHJ8aecrClkxiT5jydLpInVPTInH9asRRpePxpXWvGITM20lQq/zdP7CvepTXS4yjyZiDPa/bGeu6n3aMnzn9xz4Yg4Zh+OI7NmGluNHeKk+Hpuxyqc8gVAEZ9fvo9YA339Thyb+6Ra5yi+HP5/B89eYd7jt0JSrQkTuSmz7vx88HD+49022Tfih1M6/9Yt3sHZ3jMd7rxj1RPVq4/F7bffqWJo/PozkrYcxR1Rz95Uu5nvryMrzWQWNLFyaQsPNorn7mT97L3ab28mv/+s42zfHM3JMGxQfsiI2m5PD+85UqPMvKmUR878BiJFSHgcQQswCRgCVJmkX1LQ2qq+NMYOOsI5V75dwJY7+d6l38ZJ096H9NWo0qs2B6nQR0bUF/Wa/UaiMb3DTOvT49vJPPTof4aXz8+mMhbwGHJ28zEsHBdwqk1nHTxPUqOZl570ayIiO93k8NzEF1eEskYjfeeqN7EXbV8ey551pqKpEOl2YwoLo8p/HvM41hQZRe9ANJZ6r/ug+xC/Y6OXoVYcT/zqR7kyjtXsxhQfTZsIYmjw06Jq4eZcWm9Xh4fgBVFViszo4fOAsvt4hvUEhskbV7E9cFjH/WsDFn4qE/GOXMloIsVcI8YcQwmegWggxTgixXQixPTm55PK7/rUiqDe6j3d1nslI6xfvLPG4lcV5EalLkU4Ve2oWzhwrqs1B0sYDrBjySqnmavrwEPT+3sVQilFPZM/WhV94ufBhFa0lKS7+tcJ9HjeGBCDKoPdvVI9WoChIlwukxJqSydK+z5N+8GSpx76YuiN6ENWn7YXfs6Kg8zPR5qW7WDHo/4j7cwO2c5lkRsez5dmJ7HxzcpnOf7VyKiETnc7bZbpckjOnMgmqZvZa/et0SpVc9UPFbfguBOpLKdsCK4Gpvk6SUv4opewspewcEVG66tPek/+PVs/fjjEkML9nbRuGrP2iwjsqObJySdlxpMjSDpfizLMR2KgmPpcVlyCdLjKjE0jde+yK56buOcaJ3/4mbf8Jj+O1h3SlyT8Go7MY0VlM6AMtGIL8uHnBu5fdrG784ECvmy2AX80wt/3XAO3fesBLa0jvZ6bdq2PLZGW88YnP3WGl/JCMdDhxZOWx7f9+KPXYF6PodNy84F1unPEaje4bQIsnh3Prui/JTUxxN1m56GbtzLFy4NPfsWcWkup8HVEt1ILT4XshFhEZyCvvDaRx8wj0egWDUUdEVAD/fKs/oeH+FWxp0SiLsE8icPFKvjYXNnYBkFKeu+jbScDHZTDvZVEMejq9+48SpwyWFiklu/41lf0fz3ZX1Nqd1B7SlT7TXi6yzIDqcLKkz3Puld9Fi2eRv/rwpeAoDDpyT50jtK3vTVZHTh4rh75KyrboAu37yO6tuHn+v9H7mRFC0O3L8bQYP5LTq3ZirBZAneHd3Ro7l6Hl06OIX7iJc7ticGbnofc3I/Q6+s5645oJGTS+bwDO7Dx2vjkZR1YuOouJti/fTavnS98hzJGTR+ZRH5loUnJm7b5Sj+8xpKqChLrDe3gonSZtPFCQmnwxilFP5tFEwjuVrzZRVSck1I8WbapzcN8ZnI4Lnz2jSceQ21oRGubHa+8PJDPDisPuIjTcr0r/7ZeF898GNBFCNMDt9O8C7rn4BCFEDSnl6fxvhwOHymDeKoHL7iB58yEUo57wLs0KpBJipq7gwCe/47LaCzaeE5ZsYdOTX9J7yktFGjv2zw1kRMfhyrska0lRaPLIEGKmLPN6TbU5LruvsfXF70nefMhjDyFpw362vzqJbl9c2I8PblKb4Ca1i2QnuENqg//6jFOrd5K86SCWGmE0GNMXY1DVXPWUlOZPDKfZY0OxZ+ZiCLSUmTSGzmREMehxubz3qozBZfMe2jNz2PLMRI7P/gvV4SSyeyt6fP98QXOgoCa1SD8U5xWmU+1O/AoJeV1vPPnP3vz4xUb27kpEp1PQ6xXGPtyF5q0uZFsFBV8dGlKldv5SSqcQYjywHHeq53+llAeEEO8A26WUC4BnhBDDASeQCjxY2nnLk+Sthzn87XzyktKpO6IHje+/xWeKY/zizfzv3vfdHxYp0fub6T//XSK6NGfff2Z5yUGcl1To/u2zRVr9n1q1w6fujTDoCG5aG1NoENbk9AItG72/meZPDC9U+VNKybHpK7w2j11WOzGTl9Hti/GoDie7/z2dQ9/Mx5GVS2S3lnT9arxPFU0vuxSFWgM6F5oRdK0gFAVTtbLValf0OhrdP4Bj01Z6ZKnp/Ey0fHZ0qceXUrJi0Euc23m04O8laeMBFvd6htHRU7FEhtD25XtIXLHDM5XVbKT2kK5VstlKeXMqIYM/pu8i+mASgcEmbh3Vil43NeLZV/uSnWUjO8tGRFSAz32Aq4EysVpKuURK2VRK2UhK+V7+sTfzHT9SyleklK2klO2klP2klIfLYt7yIPqnRSy96QVipq8kcdlWtr74HYu6j/dy5NnxSfw15h0cGTk4MnNxZOWRdyaNFbf8H85ca6HNxYUQ2H1U1frCv3Z4QVHZxSg6HYENazBi5w+0eGokgY1qEt6lGT1+eIHOV0hF9dVvFCjoHLbuHx+z/9PfsadlIZ0uzq7fx5I+z5F1vPhaOxrFo+vnT1FrYBd0ZiOGYH90JgON7r2Z1i+UPqx0budR0vad8BS9kxLV5iD6x0UARHRtQd9fX8evVjiKyYBiMtDgrn70mV66JIKrkaQzWfxrwlJ2bo0nO8vG6YRMpv24lXmz9gAQEGiies2gq9bxwzUs71ASHNl5bHn+W4+URVeujcyYRI5OWUaLJ0cWHD82fYXPmLuqqsQt2Ej1Pm2Jnb/BK5/aGBKIJapomvyNHxzEvo9me2ryCIHOYqT2oBtQDHpu+PQJbvj0iSKNJ4Sgeu+2nFm71/PRXghq3NSB3FMpxP6x1usG4bLa2f/pb3T/pmTFcRpFQ28x0X/eO2THJ5F94gzBzeuUWf+GjOh4n+0tXVY7aXuPF3xfd3gP6gzrjjU53d1H4irQRSoPFv6+D7vN6fExsdtcLJ13kMEjW2GxFJ72fLVw9d62yoHkLYdQ9N73Q1eujZN/rAXcrfLiF28mIzrBZ+MQ6XRhS82i0wePYAiwXEgBFAKdn4luE58p8iZQQJ1Ibpr3L8wRwegDLOj9zAQ1rsXgvz4rcU5592+fxRDkVyAXoTMbMQb70/XLp8g4koDiQ9xLOl2c23m0RPNpFJ+AOpFU79O2xI4/J9vGoX1nOJN4obdBSOv6vhvbW4yEX6J3JYTAEhly3Tp+gKPRyag+CuF0OoWk01mVYFHZo638L8IY7O8l3FbwWrVA/h77HnHz1rurh/NsCEXxeX6Nfu0JblqHEbt+ZO+Hv5K0YT+BjWvR9uW7iezWslg21RrQmTGnfidt3wl0ZiPBzeqUKoOgWot6jD48hegfF3Nu11HCOjah2bih7g+7n9nnDU3odYS0u3LMX6NykVIyd+Zulv55CL1BweVUqdcwlOde7Udo20ZEdm9F0ob9BXsKQnHrUzV9eHAlW171iKoRxOkE78ZADqeLkNBro4+25vwvIqxTUyyR1cjKF4M7j87PhDEkgBOz//LI3kEIhF4p0KTX+5tpOLZ/gd5PYIMa9PzhhVLbpeh0RdpwLSqWqFDav3Gf13H/2hHUGd6d+IWbPLKIdCYDrV+sHNlnDcjOsjF76k62b4pFIOjauz533NcBP3/Pp7StG2JZvuAwDocLR34++vGYc3zx/l/4B5o4EtkJ/ZDW1Dq4ixono6k1oBNdP39S6/Llg2GjW3Nw72mPal6DUUf7zrUJqnZtOH+tk9clZBxNYPmACdjSshBCQbU76PD2Axz4cg55p70LtYROR1Tv1ujMRpo+civ1RvWq0rm9V8Jld1yQws6xlloKW6N0OJ0qrz69gJTkHFznFxl6heq1gvj350M9Kkrf/ucSTsSc8zmOEBfWM0aTjptvbc6Y+zuWu/1XM9s3xzH9h61kZ7v3ALv1bsADj92A0VS118xaG8dSIFWVpM2HsKdmEtmjFabQIKYHDfXZnUvoFMamL7hiEdTViFRVn42qr0VyEpM58PkfJG3YT3CzurR68Y4r9kmoCLZtjGXSVxuxWj1lgU1mPU9N6EO7TheUVP752DySz2YXaVyDUcdXk2/3enrQ8ERVJVmZViwWQ5V3+ufR2jiWAqEoRPVoRZ2h3QseiWv27+gzW6Jay3oV6viz45PISSi57lFxuF4cf+axU/zZ5pF8ZdLDHJuxikXdx5O4svJ7SMedSPNy/AAOu4uE2DSPY2071kSnK9pTp16vcOaUd0xbwxNFEQRXs1w1jr84XB+f7jKgyyePYwz2L8iSEQYden93M5eKIHXvMea2eoi5zR5gTtP7mdfmYdIOnCz0fKmq5J1NxelD3bQq48jJY9PTX/FL8DCmWgaxauQbZMeeLdc5t7/8I/bMnIIceOlSceXa2Pj4F1T2k3FUzUBMZm/HYzTpiKzuqRY5/I42+AeY0OmvfANwOlxVVnNGo2K4Jp1/6p5jnJy7jsxjZVeYFNSoJrcdnEzrf46hRv+OtHhiBCP3/FTs7J2S4MjKZWnfF8g4FFew4Zx+MJalNz6HI8c7FBXzy0pm1biD3xuMZWbYCDY9/ZXPLlFVjfNVqEcmLcGRlYtqc5CwaDMLb3gSe0bRwhkl4cxfu33q2+cmJmNLrdzVcZce9TCZ9B7CfkKAn7+Rhk3C+GPGbj54fQXTftiC1erkva+GUb9RGJfbdjIYdbTrXJtqIddeqLIo2O0uzpzKJC/Pd8Hj9cI19SxjS89m5eCXSdt/wt3oxO6kzrDu3DjjtRK1T7wUS1Qonf79UBlYWjxO/Pa3t/OWEpfdSeycdTS+/5aCw4nLt7Hx8c89CtWO/ncZ0qVeUcu/ojm7YT8735hM+oGTBDWtTcN7+pO6+5hHuqlUVZw5ecRMXUHLZ24rFzuM1QKwpfrI3RaiyCJ85YXJpOeO+zsw+dvNqC73DUpv0HH7vR144/nF2GxOnA6VI4eSWLfmOM++ciPxJ9IKVdE2GBS696nPfY+WvB/A1YqUksXzDrDgN7dQnupS6dWvEfeOuwG9/ppcB1+Wa8r5bxj3Ked2HfUoYY9ftJl9H8+i3atjK9Gy0pGbmOKzw5Irz0ZuYorHsd3/nu7VVMWVZyNmynK6fPwYhoCqsdo7tWYXq4a/VmCrNTmdlG2H8bVkdebaSNlxpNxsafncaLa//JPH+6aYDNQf3adEhU7WPAcnj6XiF2CkTr1qpcr+Sj6bzfQfthY4fgCHw8V/J27C5ZIFYSnVJbG7nEz9fkuh8wVVM/PZT7dhMJSNGN3Vxoa/j3s1Y9nwv+MYTDrGPtzlitcnxqez6e8TOF0qnbvVpXHz0snOVzbXjPN3Wu3EL9joqV2C2/Ed/m7BVe38w29ojj7A4pVtpLMYCe/awuNY1skzPscQOsVdsl/Bzj/3TCp7P5hJ4vJtWKJCaP3indQd3oNt//ze6yal2p0++xboLCZC2pZf5k2LJ0eQeSSBI5OWoJgMqDYH1W9sR4/vny/2WKuXRjNryg50OgXVJQmL8OfFN28iPLJkQnDrVsfgujQkJd0poL44l5yDrpBVbN36Idet4wdY+Pt+D8cPbsmGv1ccZcz9HdHnvzcnYs6xZtkRsrNsdO5Wl6696rFm2RF+m74Ll1NFVSWrl0bTq19D7n2kC0ejU7DmOmjSIgL/gKunKvqacf6q3YH01ZeUq78vaa1bOhPSuj6pe44VFF/pLEZC2zemRr/2HudGdGlG3IJNXrK8QqdUuCxvXlIa89s/ij0tG9XhJPNIAud2HqXd6/eSXthmtQRh0CPPh7mEQGc20PShQeVmp1AUun31NO3fuI/0g7H4140sUdOfI4eSmDVlR76DcTuZ06cy+eRfq/lg4vASPQGkp+UV5PcXBSEEg0e2ZOmfh7DbLiyEjCYdt93TrtjzX0tkpHvvj4H7qclqdRJg0BXcvB12F1LCgd2nWb7wEIlx6R43XLvNxbrVx9i2MQ6r1YnT4T6/WoiFh57qRvvORZdDryyumUCXMcif4GY+ukMqCrUGXfmRriojFIVBqz+l3Wv3Eti4FkFNatP+9fsYuOI/Xg6lwzsPob+k25TOz0SHfz902R685cGBz//Anp7jsV/hzLGy+53pmCOCfV5jrBZAw7v6oRj1oAiq923H0E0TK6QK1RxRjeo3titxt7eViw5jt3uuLKUqST2XS9yJtEKuujyt29f0me2jKAJfmbj1GoUy6q523PVgR0LC/NDrFeo3CuPFN/vTqOnVHaYoLQ2bhPk8HhBkwj/ASG6OnV8nu2/e59dONpuTxLh0PLop5eNwqGRl2gpuFOC+WX/90f/YtjG2nH6KsuOaWfkD9Jr0T5YNmIBqd6DanejMRvQBFjp/+Ghlm1Zq9BYT7V4de8XwVWibhgxZ9yU7Xp1EyrZo/GqE0e71e2lwZ9+KMfQiTq3c4alImo9qd1B3VC9iJi/3kMrW+5lp89JdtH3pbrp++RQpW6MxhQcRVIymMpVJZrrVl49AUQTZWd7N7YtCx651qPlnMImx6QU3FpNJT4eutdm6PpZLJ4w7nkZqSi79Bzej/+BmJZrzWmXMA51475Xl2O1Oj2rnsY90QQjBkUNJ6PUKjktu4Ozv1I4AACAASURBVE6nivBR41MYTofK7Kk76dKjXlmaX+ZcU84/omsLRu37mUPf/En6wVgie7ai2bihmMN8rzKvVcLaN+aWJR9Wthn414nwqQYqXSqpe47R9vWx7PvgV1SXCyEELZ8fTZsJY9j/2e/sfP2/KCYD0unCr3Y4A5d9TEC9KB+zVB063FCb40dTvFb/TqdKg8a+V51XQq9XePW9gfy9/Agb157AZNRz0+CmZGfZ2Lk53vtJQ0q2bDjJkJGtSvxzXKvUaxjKGx8NYt6sPZyMSSWyegAjxrSlRZvqAFgshsLrOopZ75F8NhtVlV4N3asSmryDRrlxdv0+lvR93mcOvc5s5PZjv2AKC8KalI45Ihidycjpv3axathr7kbi+QhFIahZHUbt/7lYcfOE2DTWrzmG1eqkU7e6tG5fo1x1l6x5Dt58YTGp53ILVo9Gk47R97Rn0IiyrQdZNv8gv0/f5bXxKwSMGNOWUXdd3/H9kqC6VJ57ZC4ZaZ57A0aTjmG3t2Hh7/sQikBKiZqfaeVy+fafQcFmvp5aOWKIRZV3uKZW/hoVT3bsWba+8C0Jy7ehMxlo8uAgOr77D/QWE1G92mCJDCHvjLcgnmLQY01Kx69GGP61L8SiD341z8PxgzvXPyfuLGn7jhfamP5SVi2JZvaUHTidLlQVNv7vBG071OTJCX3KbTVmthj412e38veyI2zfEk9QsJlbhjYvWFmWJW071eKPGbu9jp9XntQoPopOYcJb/fn47VXYbU4EAqdTZdTd7RgyshU3DWrKrq0JOJ0u2nWuzfZNcfl/Y543YINRYfidbSrppyg6mvPXKDG29GwW3vAk1nOZoLolEQ59t4Bzu2MYvPpTAOqN7k30j4uQDu/wRJCPDXprciHtL/U634VYPsjKtDJr8o4CWWMAm9XJ3l2n2LfzFO0617rM1aXDYjEweFQrBo8q37BLzdrBDLi1GauWRLs3HAGTUU/Pfg28QkzZWTbWLDvCwb2niaweyC1Dm1O7Xgin4jP4a8URMtKttOtUi6496xWkO16v1Kkfwpc/j+bwgbPk5jpo3jKKgCB3AkVAoIne/S8sPm4Z2pyGTcKYMWkbscfTUFWJxU/PyDFtuXlI1d9v0Zy/Rok5OnmZW17iooY2qtVO8pZD7kYxHZrQ7pV7ODHrL+yZOQU3AL2fiS4fj0Pvo2tY3ZE9ObcrxqOJOIDqcBLepWgfqAN7TqPTCxyX7DXbrE62bjxZrs6/IhnzQCc63FCHjX8fR1Ul3fs0oHlrz32R9LQ83nx+Ebk5dhwOlegDSWxae4IBtzZn5aLDOPPz1ndvS2DFwkO89v7Aa1LErDgoOoWWbYuW8dW4WQRv/WcIqiqx5jkwWwxVOs5/MddMqqdGxZOy7bBXoRa4Y/Rp+04A/9/eeYdHVaZ9+H6nppNCSKH3XkWkCyIgIKKirqtrWXXVVT/r6rK2ta0r6qprW8W2rO7asIGoVOm995JKSIMkpCdTzrzfHzOEDDNDemZC3vu6cjGZOXPOM4fJc97zlN8DIYltuXL3B/S5+wrCuiVgjonAYdfY8eQnbPvLB2hnVQP1uWsmoR1j0Qe7Lgyu8ZcjXr271uqpzkYmzz9AoRPnnWPr1bcdt/5xJLfdO4q+A+M9cho/fLmH0hILNpvzAu1wSKwWjcXf7sdq1apGFVoq7WQdL2L18qRm/wznAzqdICTU1GIcP6iVv6IBRA/sxrHv15+ZbHYaKYnodSbuHJLYlkF/uYHkT5dhLSoDKbEUFHPgze8oOpTBpO+eq9rWGB7CFdve4/C8H8lYtJHg+Cj63ncVcWMG1NquAUMT8VZzaTToGHeJZ87AatX4+bv9rF2Z7FxBX9yVmbMHEHQeDOnevT3TZ1LybKwWjc3r0pg8I3AH95SVWvl1yRF2b88kpm0oU2b2oVvP5m1ePF9Qzl9Rb3rdMZ29r3yJZrFVlcLpTAba9OlI7FmyEwff+QF7hcWtZE6rsJC5ZCvFSZlE9DgTijGGBTPg4WsZ8HD9qiXMZgMPPj6RN/72K7gmWDk0B7OuH+ThKKSUvPLMclKT8qsqdJb8cJA92zN59tXp6PQt++Y4OKRuF7DgZrzgOTQHG9aksnpZEg5NMvaSboyb1MOnyFppiYWnHvqxqrFKCNi++Ri3/nEkYyb4f/BOS0M5/3NgLS4ja9l2kJLEKcMxRSj98+oExUYyY90/WX/na5zYdACdXk/n2eMZ9c4DHuGHvK2HvA6H15mMFB5Id3P+jUHfgfG8+e9r2L09C0ulnf5D4gkOMeHQHG4O/fD+E6SnFLg19thsGieyS9i1PZNhI7x0jbcgpl7Rl0/nbXHTtNHpQK/XYbM73G6QzGYDl1zWq9ls+9dr69i9LROLS4biWFoBWzekc++j4zm0Lxe9Xke/QfFVobpffjhAcWFlVXWNlM67lU/f3xLQyeqiwgp2bM7A4ZAMGd6BmNjA8COtwvlrFivbH/+IIx8uxlZWSbuR/Rj59v3nHIqe9u1a1tz8d3R65xfKYdcYN//PdL3m4uYyu0UQ2a8LM9a55gXoRNX5OpvoQd3IWbXLQ3jPYbO7hYgaE3OQkRFjOrNtYzrPPfYLRYUVGI16Lp3em9k3DkGv15FyNA/7WZVIAJWVdlKO5LV45z/uku5kpJ5i5ZIjGI16NM1B+06R3HjbcN56eTUW15Qwze5g0vReDLmwecpEU5Py2bXtuNtFyWrROHzgBPffuqBKgE4iuX/OBPoPTmDn1uNeBe0kcPxYIV2616+RrilZvyqFT97dVDVD+fOPt3PdzUOZMrNvzW9uYlqF8199w984/suWKlG0Exv289P4B7lyz4eEd/Gswa7ILWDNTS+iVVip7hbW3vwScaP7E5KoYoxnozOe+6vU974rOfTeIjfnrw8y0m5MfyL7dKp6TtMcCCHOmTiTUnJoXy4pR/OIjA5h+KhOzoEnXji4N4f3X19f1Qlr0ewsW+zU4PndHRcSExvqcoruFyWTWR8wK7SGIITgxjsuZMbsAaSnFBAdE0LHLlEAvP7hbA7uzaGk2ELvfu2adbLX4f25bjLVp7G7EtPVnfw/X1zFGx/PJizcu2KmpjkCUk2z8FQFn7y7yUMu4qtPdzJwWCIJ7f2rPNCyA5q1oCQth+M/n3H8p3FYbBx44xuv70n9eg1etYWB1K9WN7aJrYKwTnFMX/06bUf0ASHQmY10/93kqmRvekoBzz32M7df81/+8Jv/8dHbG6n0MmnJatX4+5NLef1vv7Lgs13Mf28zD9/xDZkZ3vsDvvt8t4cEgtWisXrpUSyVNoaO6IjRrPcYI2Aw6Bk5rkujfPZAIDIqmMEXtK9y/OAM/QwYksio8V2bfaRjeESQT+lpDwRs35TB1Cv6YjK731nqdIKOXaKIjaufZHZTsmNzhlcv4tAcbF7nf+G38975Fx/OqJq7Wx2HzU7+Tk/dGQB7WYXXsYcOm73Fy0P7k5ihPZm56R1usSzh5vKfGTPvEYyhwRTklfHiE0tIPpKHlM7V38bVKc6E7VksWXiAlKP5WCrtOBwSS6Wd0lIr77661usxc3O8N4YJnaCosBKTSc+Tf7+Mzt2iMRh1GIw62ndqw+N/m0JwiGcfgqJxuGBkx1qXRTo0Zw39sBEdufzqARiNeoJDjJjMejp0iuSBOYEZitU0hzedP5c8RO1lupuK8z7sE9G7o/dEo9FAzDDvya2OM0ay67lP0ezuK0ad2UiHGRc1iZ2tibNHaq74+XDV7f5pbDYHyUfzOH6skA6dIqueX7sy2eM2Ggm5WSUU5JV5rGA7d42m8FSmR+WnAKJiQgCIT4zg2X/MoKiwAumQREaHNOwDKmokKNjIn5+bzD9fXEV5uRUhnKt4m1Wr6kmoQsDAYYmAU7fo0hm9SU3Kp01ksNudTKAx9MIOfPnvHR7PGwx6LhjZycs7mpfzfuUf3iWeDtMvQn9WN6k+yEj/B2d7fU/UgK70/sMMDKFBTqUsITCEBtHz1svOmST2J5rVRnFSJraScn+bUmeOpZ7ymsjT63XkZJ01QN1XybrwLrw4+8YhmEzuFxuTWc/M6wZ6TLVqExmsHH8z0rVHDK99eDVPvDiVOc9P4e351zJsZCe3+QUmszNBH594Zp5DaJiZAUMSA9rxA7RtF8bsGwdjNOnR6QRCgMmk59LL+9C5W7S/zWsdqp6axcqOpz7h8LwfsZdV0m5Uf0a+9X9ED/YtEialJGf1bpL/uwKkg+43Xkr8hCHnVIXUrDZ2v/AZhz9YjFZhof3UC7nwlbsI69S0UsT7//kNO5/+N9LhQGoOut04iVHvPNDsw1vqy7ef7+Knb/d7rPiMJj0vvH458e3P/OH/8OUeFi3Y56bbA5DYIYK/vz3L6/6Tj5zki3/vID2lgDaRQcy8diDjLunepAqfivohpWT3tkw2rE7FYNAxblL3JhHGayin/WZtvkNZGUVsWpeKQ5MMH9XJZ1WSlJKMtFMUFVbStUeMzwR3TdRW1bNVOP/mYvkVT5C1YmeVLo3Q6zBFhTP78HzMUeFNcszUr1ax7vZX3HIR+mAzPW6Zwuh3H2ySYzY2RYUVzLn3ByrKbVWrd6NJT//BCTz0xES3ba0WO39/cilZGUVUVtoxmw3oDTr+8sJkOnX1/2qqOXE4JClH8igvt9KjdywhoY2fo5BSUlRYSVCQ4bzoeG4olRU2Pv9kOxtWpWCzafQZEMfNd11EYoeGVe6cKijn1WdXcDKnFJ1eYLc5mHnNAGb9ZlCd96WcfzNTeDCdhcP/6CFIpg82M/SZWxj46G+a5LjfD/kDp/akeDyvDzJxQ/73GIIDrwTOGzlZxfz3o60c2JOD2WxgwtSeXHX9YK8Dxx0Oyd6dWaQcySOqbQgXjelcp+Ts5nVpLFqwj6LCCvr0j2P2jUPcwgotgezMIl55ZgVlpRaEEGh2B9fdMqxRpRl2b8vkk39torS4EimdU8Vuu29Us3YBBxovPr6E5KN5Z3JUAkJCjMx9ZxYRkU7tqWNppzi0L4eINkEMHdHRZxlydZ7500+kpxRUaS2Bs+nuj4+MZWgde02Unn8zU7AnBZ1Bz9ntQlqFhZObDzbZccuz8n2+Zi0qazHOPz4xgkeemlSrbXU6weAL2jP4grp3BX/+yTZW/HS4KsS0deMx9u7I4rnXZ9Auvmnuzhobh0PyyjMryM8rc8uBfPWfHXTpHk3PPu0afIz0lALefmW1WxPWji0ZlM+18ugzlzZ4/y2R9JQCUpPz3YsTJNisDn5depSZ1wxk3hvr2b7pGFJK9AYd89/bwpznJ58zxn8ip4TjxwrdHD845wcvWXSwzs6/tjRKwlcIcZkQ4rAQIkkIMcfL62YhxJeu1zcLIbo0xnEDiYjuiUiHZ9JSZzYS2a/pZnnGjuyLR5E6YAwPJrhdpJd3tE7Ky6z87fEl/PLDQbfcgnRILBY7ixbs86N1dSPlSB5lJRaP5LfVqrHipyONcoyfvtvvOcvW5uDwgROczC1tlGO0NLIzi7yWp9psGukpBWxck8qOzcewuiqWKivslJdZeePFX32Ph8QpVqf3oSFVUly/2c+1ocErfyGEHngHmAwcB7YKIRZKKQ9U2+x24JSUsocQ4npgLtA0cRA/EXNBLyL7dqZgT7J7F6vJQO+7Lm+y417wt9vJ+XWXUzTNtXLQh5gZ8do9CF3LKOayVNo4tN85PLt3v3b11miRUnJ4/wlO5JTQqWuUW2Ltgzc3kHz4pNf3ORySfbuyeO6xn8nMKKJtu1Bm3zCEYRcFjrSDpjlYvvgQvy49SnmpFZvdU5IC6Rxk0xjkZpd4rZ4yGHTk55UFZFNVfSgtsVB4qoJ2cWE1yn0ndoz0WJ2DMz/VpXsMq5YexWLx/H8pK7VyLPWUz9V/h86RXpvBDEYdFzThd7Axwj4jgCQpZQqAEOILYBZQ3fnPAp5xPV4AvC2EEDJQEw71QAjBlKUvs+Hu1zj2/XqkQxI9qBuj5z1MaPvYmndQT6IHdmPm5nfY8cx8Tm46SHjXeAY/+TvaT64x5BcQbFmfxgdvbnCufCTo9IIH/jKB3v3rViFVXFTJS08uJf9kGVI6NWG694rl4acuQdMc7KlB2vhUfjkFec4y2ePphfzrtbX1Uou0WOx8+e/trPs1BZtVo+/AOG6+8yK3iqX68Pbc1ezbne0Whjkbk1nP8FGNUz/eu187jqWdQjurBNduc9ChY8u/o7RaNT5+eyNbN6ZjMOiQDrjiNwO5/Grf0uGdukTRvVdbkg7lVVWbCeGcHzFhSk92bT3u9X1COC/evjAa9dx01wj+/S+nFISUzgtKmzZBTL2i6TSAGsP5twcyqv1+HDi7E6pqGymlXQhRBMQAedU3EkLcCdwJ0KmT/5sg6oo5MoyJXzyNZrXhsNlrPXykoUT268IlX/21WY7VmJzMLeGDf25wyS+ccWqvvbCSf358TZ2qSz55ZxM5WcVuDj7p8Em+/3I3k2f0OWdJnvDSI2C1aHw1fwejL+5ap5LQ159fSdLhk1WhpQN7cnj2sZ+Z++4sItoE1Xo/1UlPKWDfrmwPmYrqmMx64hMjGDOxdjOOa2LqrH6sWZFMhWatOjcms55LLutVNdawJfPpvM1s23QMu81RFcP/4cs9tI0NZeS4rj7f99CTl/DV/B2sXZmM3abRd2A8N905gog2QYyZ2I2M9FMeF2iDQU+XGur6x0zoRkL7CJb9eIiCvHIGXZDIxKm9mqSCq8quJttzPZBSzgPmgbPax8/m1Bu9ydhiauz9yfpVqWhebqORzuTi6Itrt+q22TSvQ0tsVo21y5O59ndDCY8wU5Dv2QBnNOpwOKTXu4LiokqsVq1W1RrgrPJIPprnnlOQTjtWLT3KFdfWb6h3ytE8n6+1iw+nXXwYw0d1YsyEbh4NbfUlOiaE516bzoLPdnFgTw6hYSYum9WXiyf3bJT9+xOLxc6G1akeXeVWi8aiBfvO6fzNZgM33TmCm+4c4fHaxZf2YMv6dFKTnPIjRqMOoRPc86dxtZoL0a1nW+56aGzdP1A9aQznnwlUD0x1cD3nbZvjQggD0AbwXaaiaBWUl1o8wgoAmkNSUX5GkkNKSdLhk2SkFdIuPox+gxLcEm8Oh/SZULPZNIQQ/P7ekbw1dzV2m3Nmrd4gMJsNPPfa5bzy7HJyszw1gMxBBq+lpr7Iyij0mRBMS67/1z0qOgSd3ktS36hnwuQezJhd+ylndSE2Lpw/PjKuSfbtTyrKrD7v5opOVdR7vwajnj8/N5m9O7PYvzubNpHBjJnYjcio5okA1JXGcP5bgZ5CiK44nfz1wA1nbbMQuAXYCFwDrDyf4v2K+jF4eAdWLUuq0pSvzoAhzgHalkobrzyzgmOpp3BIiV4niIwO4YkXp1TVVZvNBjp3iyY1yd3B6nSCoS59+kHD2vP03GksWXiQ7Kxi+vSPY8rMPrSJDOaq6wfz8Tsb3W7XTWY9M67uX6eZrIkd2nhPCBr1DWrnHzA0kaBgI5ZKu1t4Sq8XjJ3UOGGe1kREZDDBwUaPaiYhnDORG0JDypCbmwaXg0gp7cB9wBLgIPCVlHK/EOI5IcQVrs0+AmKEEEnAw4BHOaii9dFvUDz9BsW7hVVOT5OKS3AmSL/9327SkguwWOzYrBqVlXZO5pbw8bub3PZ1+32jCA4xYnSFPUxmAxGRQfzmlmFV23TsEsUd94/mqZcu49qbhtLGdfEYNb4rN9w2nLBwMwaDjqBgI5fPHsDldVxRd+oaTbeebTEYz/xZCQFGk46JU84dLqkot7JmeRKLFuzl0P5ctzsZg0HHEy9OpXO3aIxGPSaTnti4MB599tKqz9BYnMwt5evPdvLea2tZszwJq8XzwtzS0ekEN9w+3E0eWqcTmIMMXPO7oX60rHlRHb4Kv+LQHGzblMHG1SkYjHrGX9qDAUMSqm7L773pK0pLPGud9XrB+1/81i0sU1JcyZrlSWRlFNGtV1vGTOhWp6SxwyGpKLcSHGys9+xeS6Wz/X/9qhTsNgd9+sdx010jztn+n5qUz9ynl+HQJDabHaPJQM8+sTz05CUe82xPFZRjtzlo2y600bWJ9u/O5o0Xf0XTJJrdgTnIQHRMCE+/PK1JE4/NQUWFjSU/HGDL+nRMQQYund6b6JgQFn2zj5M5pfTsE8us3wyqVad3eZmVvJNlxLYLDUjZbyXvoDgvuPuGL9zi/6fR6QTvf359jbXZ/kRKWaODLimq5LF7vqe8zP0zmsx6rr/1AiZN692UJlbhcEgevP0bj5i3wahj+lX9mX3DEK/vy80uZumPh8jOLKZ3v3ZMuqx3o1UDORyyTmE3X1itGn99eDEnc0urSjTNZj0jx3XltvtG1d4ezcF/P9rG6mVJ6A06NM3BxCk9+e1twxvFzsZCyTsozguGjejIxrWpbiP/hHBWRgSy44eaFR/zT5bx1EM/ejh+cE0bW5bUbM4/N7uYSi8XWbvNwZZ16V6d/6F9ufzj+RXY7Q4cmuTI/lyWLT7Ec/+Y0aDJYIf35/LpB1vISCskKNjIpTN6c/VvB/vsgq2JzWvTyM8rc1OCtVg0NqxOZcbsAcQlnFvWw253sGrJERZ+vZfiIqfO0el9rVp2lIjIIGZeU79KLn/SMlpAFa2W624ZRmRUcJXGu8msJyTUxO3/V/sVW6Dy1X92UF5mPccWzXdXbjIZvCarATd9/dNIKfno7Q1YLVrVhdlmc1BWYuWb/+2qtx3H0k7x6nMryEhzjuWsrLCxdOFB5r+3ud773L87y2tRgU4vSPLR9X0aKSWvv7CSL/+zg6LCSq/9IL/80HTaXU1JYC+dFK2eyKhgXnpnFpvXppFyNI/EDm0YM7FbQA7srit7dmR5lVAAZ5J43KTmGxwUExtK+05tSE89hax2ETCZ9VwyzXPiXUlRZVVHdHUcDqcef22orLCRllJAeISZ9q6u4R8X7PWowrFaNTasSuG6m4bVK6QU3TbUGaY5q6xYCGoswzxy4ARHD508Z2d12Tkv4IGLcv6KgMdsNjD+0h6MGNOZdSuTmffGemJiQ5k0vXeV02iJmIMMPlf+3Xq2rbFCqLG577GLeenJpZSWWpDS6chHjO7MeC8XIZPZ4PO+JCik5iT70h8P8fV/dlTFzuMSInj4qUvISCv0rilk1HPyRGm9nP+EKT1ZtvgQWrXFvxAQEmqi74Bzy4gcOXjCc2zoWXTuGtgTxXyhnL+iRVBWauWZPy2m8FQFVouGTidYuyKZPz4yLqAE2OrCpGm9WPjVXjfZBp1O0LNvLH95YUqTTBo7nn6K9NRTtIsPo0fvWLdjxMaF8cr7V3FoXw6n8ivo0adtVcnt2QQFGxk8rD17dmS6jeA0mfVMnt4bu93Bwb05lJVa6DMg3m2FfXBvDl9/usP5uV2fPfNYIa89v5LO3aLJzip2u/sAZ+6hXXz9xOTaxYdz/5wJvP/GOmeYyiGJT4zg/jkX11jV1SYqGKNJ7zVsdPrz3njHhfWyy98o569oESxZeICC/PKqlnyHQ2K1anz41gbeGn5tvZOB/mT6Vf05lnqKnVuOYzDo0BwOOnaO4oG/TGx0x2+3abw5dzUH9+Sg0wkk0C4ujD8/P5nwiDOaQzqdoN+ghFrt8477R/GP51aSkX4KvV6H3aYxYnRneveP48HbFlQlRTW7g8tnD+DK6wcDsGTRQY8wisMhyc0u5uobBrN9c4Zbf4HJrGfcpO4NCvUNHJrImx9fQ9bxIkxmQ61nN1w4ujP/+8iz6lAIZ5Pi1b8dHBDzeOuDcv6KFsG2jcc8tFjAqZaYmVFEpwAf5u0NvV7HvY+OJze7hIz0U8S2C2syR7Lom30c2JPjFsLIOl7ER29v5MHHJ57jnb4JDTPz9MvTOJZ2ivwTpXTqGk1UdDAP3vGthw794u/207t/HH0HxlNc6F12Wq/XERpqYs7zk/nsw62kJ+cTHGpi6hV9ufyq/vWysTo6vY4Onev2PQkONvLn5ybz9surKSlyfqawcBP3PnYx3Xu1bbBN/kQ5f0WLwFczjaZJv40VTE3KJz21gLj4cHr3j6t3rXdcQniN5YYNZdWSox6xa02T7NmehdVib1DZbKcuUVUX3yMHTmCp9F66uvKXI/QdGM/g4e1JS873ENPT7A46d4smKNjIX1+eVm97GpuuPWJ49f2ryD5ejESS2KFNk4Tkmhvl/BUtgsmX9+ZYWoFbuECnE7Tv2KbZB4tYLXZee2ElyUecaps6IYiKCeHxv53RGwo0qte4uyPPqTVfVywWu0/HWF7uTG5npBd6OH6hg2tvHhqwQ+KFECR2bNiQ9kCj5QVKFa2Si8Z2YeKUXhiNTu0dc5CB2Pgw7p8zodlt+eGrPSQdysNq0bBanHpDJ3JK+Ojtjc1uS20ZMryD1zuTxI5tGlWioGefWK9KrWazgZFju5KWnM/ubZ5DTwwGPZ26tMzYeUtFrfwVLQIhnGJc067qR/KRPCIjg+neu61fbr/XrEj2WElrmmTvTufAlcbS1G9Mrrt5KPt3Z1NebsVq0TAadegNOu74v9GNepygYCM33zWC/7y/Bbtdw+FwlrR26hrFqPFdWLr4EJrds5bTZtXYvyebPjWUXioaD+X8FS2KqOgQho90n/JWVmph68ZjVJbbGDA0kQ6dalf7n3W8iIN7cwgLNzP0wg61jnvbfYRQJBKH5gAax/nb7Q62bzrGjs0ZhEWYmTC5Jx3rmdiOjA7hpXeuYO3KFJIOnySxfQQTpvQkMjqkUWytzrhJPejcPYbVS49QUmzlgpEduWBkJwwGHWFhZgxGnUeoyWjSEx7eNI17UkpWLT3Kwq/2UlRYQUKHNtxw23D6D65dVdP5ihJ2U7Ro9u3K4p9/X4VAoGkOdDrBuEnduenOET7vCqSUzH9vM+t+TQGcCqE6nY7Hnr2Urj1ivL6nOvP+uZ6Na9z1hhDOxOdvf38B0TGhDZ7Za7dpzH16SGuo/wAAHbFJREFUOempBVgq7eh0ztDIzXeNaNbO38amvMzKQ3d8Q2WFe928yaznH+9f1SQ5k5+/3893n+/BUr181KTnkacnnZd3GrUVdlMxf0WLxWrVeGvuGqwWDYvFjt3uwGrVWLcyhX27sn2+b9vGY2xYlYrNqjlnBFTYKS+z8vrffvWpb1Od624eRpvIM3pDBqMOvV6QlVHEmy+t5qmHfuSFOb94laKuLRvXpJGeUlDVXORwOD/vf+ZtobLCs5qmpRASauKRpycRHmEmKNhAULCRkFATDz4+sUkcv6Y5+OGrvW6OH5zncsFnOxv9eC0JFfZRtFgO7cvx+rzFYmftimQGDk30+vrqZUkezgCcWvypSXl07xV7zuNGRgUz950r2LA6lZSjeVgq7ezcchyrVavqdk1Jyuf919fxyNOT6vipnGxel+bVRr1ex5GDJxg0zHNSlKXSxv7dOUgp6T84IWArZ3r1bcebn1xD8tE8pAO69WrrMbfgXDgckv27s0k6dJLI6GBGjOlCaJj3pHVpicVrfwg4w36tGeX8FS0Wh0OCj3zvqYIyykotXrtCfZc9Cp+O4mzMQUYmTu3FxKm9eOqhH90kGsBZs35gbw6lxZZ66dEE+9DHkVJSWWFjwWc7OZFbSt8BcYy+uCsH9+Xy7qtrXaEuicMhufOBMVw4unOdj90c6PQ6evap+8hEq1Xj5b8u41jqKSyVdkxmPV/O38Gfn5vsNWQXGmZGrxfYvNwsNXVvRaCjnL+ixWG3aWxam8amtWlYfWiupCUX8MDvv2HWdQOZea271vroi7uScjTPQ2JACOrVtVlW6l2cTacTlJdbPZz/gT3ZfDl/J9mZRcTEhjL7xiEeSeyJU3uya9txDxv1eh0fvrkRTXNgtzvYteU4ixbso7io0qOJ6/031tO9dyzRMY2f1PUXyxYfIj25oOpi6zw/Gu+8soZX3rvSI89jMOiYdlV/Fn+7z+1cGk26Oo/pPN9QMX9Fi8Ju03jxiaXMf38ze3dmVcXoz65ht1o0bDaNhQv2snu7u8TwmInd6dE79kzM3qDDZNJz98NjMRjrXqkzcGgiOr3nLUhQkJG2se5DTfbvzub1F34lLTkfS6WdrIwi3n99Het/TXbbrt+gBGZc1R+DUVcVGw8NN2Ew6KryG+AMcZ3KL/daW4+UbFmfVufPE8isX5nscZcFUFRYwYmcEq/vmXXdQK7+7WDCXNVEQoCU8O4/1vLea+u8htdaA2rlr2hRbFqbxvH0Qo8VsUSi0wv3ChycF4Gliw4y+IIzMXKDQcejf53Enp1Z7NuZRXibIMZO7E5MbP2mT115/SC2bz5GRbkNu82B0AmMBh2/v2ekh2rkl/N3eDgvq0Xjy//sZPSEbm4r1yuvH8yEKT05uC+XkFATcQnhPPXgjx7H95WkttsdWCrOL8fms69Dgq8YoBCCy2b1Q2/Q8fnH23A4qArvbdt4DE1zcO+j45vG4ABGOX9Fi2LrhmNeV2omox4pwap5rgq9Vd3o9DqGDO/AkOEdGmxTVHQIL755BcsWH+LAnhzaxYVx2ax+XkXasn0kGUtcYZuzew0io0MYNb4r4FzdOnyVZjtD/W4YTXoGXeCZGA5UysuslJVaiWkb4lNqeeS4Lnz7xW6Pi3x02xCfks9SSua94SzPPfv02WwaO7dk1Ds305JRzl/RoggNN1Xdtrvh7TnAaNQ3i95/RJsgZt8whNk3+N5GSklImAlrQYXHa0HBBow1dAa3iQyma/cYko/kea72JW7nxWw2cNG4LrXqW/A3lkobH761kR1bMtDpBCaTgetuGUplhZ39u7KJjQtl0vQ+hEeYWbnkiIfWvznIwL2PXezzrmD39ky2b87wOTVNb9BRWFihnL9CEchcclkvtm5I9wj7BAWbuOaGwXz64VZsVg0pnSvfqOhgJs/o4ydr3fni39spKfKUMzaZ9Vw+e0CtpCrueXQ8f39yKcWFFVWDSU4jpTP3MXREByZO7cWAIS2jg/Vfr61j386sqlCM1aLx8dubMBh02O3Oxr01y5MZMDSRYi9zdIOCDOfs6t64OtXnMBZwnrfa6vufTyjnr2hR9OgdyzU3DuHrT3diMOqRUmI2G3j0r5Po2CWK9p2jWPbjIU4VlDNkeHsmTOnZqMJl9SX/ZBkrfjrsoWYJMGxER6bXUq8+OiaEue/MYuOaVD56a4PH60IniI0L89njUB2b1c6iBftIPppPp66RXHHNwGY/V4UF5ezbmYXNS4nt6aT26cE9O7d4X71XlNs4mVvic+rYuQb9GAw6rvrt4IDUY2pqlPOvJWWlFpYvPszu7ZlExYRw2ay+9apTVjScqVf0Y+wl3Tl84AQhISZ69Y2tihF379WW7g+P9bOFnhzal+u00YuT0+tFnQTqdDpBaJgJk9lARbl7Abtmd3As9VSN+8jJKubx+xdVVQnt25nFL98f5OlXptG1e/OFik4VVGAw6r06/7PxFbax2R2Yg3w3tI2Z2I1tGz1zRULAH+4fzUhXTqW1oZx/LSgttvDUwz86k3I2BwjYsyOTm+64kPGTm3fItsJJaJiZYSNazuze07kKDwSEtwny8sK5SezQxm127mkMBl2t4vyvPLvcozzU4ZC8/NQy/vW/6+tsT31JaB/R4HkCAtxmBJ9Nv0HxTJjak5W/HEFKiV6nQyK5f86EWt0hna8o518Lfl54gOLCyjN/bNIZl/zvR9sYeXG3VnnLqKgbA4Ykeg8/SOpVjtkuPpxBQxPZszPrTHOXcOY5Lq0hx2G1auTllnl9rbzcRnZmEQntm2dwSVCwkSuuHcjCr/d65HGq4zXJ78JcgxqrEIIbbhvOhCk92bsji6BgA8NHdWrQTODzAeX8a8HubZleV1kIQeaxwhZRUaFoXAryyliy6CBJh06S2DGSaVf2I7GDb4dpMOgYMCSBzevSPV5btyqF624ZRkho3eLtdz00ho/e3sSubcex2xz0GdCOG++4sMaO3pqUfHdtzWw25w8w85qBtIsPZ/E3+ygqqqTvgHiEkGzdcAy9wZnXiYkNJaJNEEcOnHBLcuv1gtETahe2SezQ5pz/R60N5fxrQXgb7ysETXP4FJRSnL/kZBbzzKM/YbVqaHYHKUfz2bQ2lUeeOrdEcGaG9xp/g0FHdmZRjYJy1UlNyucfz63AZtPQ6QR6vWDk2C6071jzLAOz2dkx7Esd1B8y7xeN7cJFY7tU/W6ptDFlZl/yT5YRFRNKt54xnCqo4IU//0JZmQWbVcNo1BMbH8a1Nw1tdnvPB5TzrwWXzexH0qGTHvNjO3aJapUlYq2dL/69ncoKW1UYwuGQWC0an/xrE3PfmeXzffGJERw/VujRjGW3aUS3rX13sc2m8cozyz00hT79YCtde7at1cCXm+++kHmve1YLGQyCQcP8Fwe3WuzMf28zm9alIYQgOMTITXeOoHuvtkTHhPDye1eya9txTmSX0LFLFP0HJ3gdT9nc5J8s4+SJUhI7tCGiHjkcf6Ccfy0YPLw9V14/mO8/343e4JxClNihDQ/8ZYK/TVP4gYP7cr3Gn0/klFBRYSPYh5Ty9Kv6s2dHprvAmFHPgKEJRNVhotb+XdleS0btdo3Vy5P43R0X1riPMRd3Z+/2LDavS68KoxiNgvGTe9Ghc/2mhTUG8/65gV1bM6pq/m1WjQ/eWE9kZDC9+rXDYNB5iOD5E4vFzr9eXcu+XdkYjDrsNo3xk3vwuztGBMRF6Vwo519LZlzVn0um9iQtuYA2kcEkdlSxw9ZKSIj3kIlOJzCeQxiue6+23PPIOOa/t5nSEgsSuHB0J27940V1Or6zvNPT+Tsc+BQ388ZdD41l9IRubFiVihDOksjGGG1YUW5l09o0sjOL6do9huGjO53zvJymuKiSndUc/2msVo1FC/bWezZCU/LpvC3s25WNzaZVSYWvXZFMfEIEU2b29bN150Y5/zoQHGKi78B4f5uh8DOXXt6H77/YfdYKXsdF47rWOJRk6IiODLmwA0WFlQSHGGusVPFGn4Fx3gsQcKqG5mYX+2x4qo4QgkHD2nsdDFNfsjOLeGHOL1itGlaLhjnIwLef7+KvL0+vUT7hVH45BoPe60yFEzmljWZjY2G3aWxck+p5sbJo/LLwYMA7/wZJOgshooUQy4QQR13/er1fFEJoQohdrp+FDTmmQuFvpl3Rl1Hju2I06gkOMWI06ek7MJ6b76w53AJOpxsZFVwvxw9OIbmpPhyLZnew+Nv99dpvbZBSsmF1Cs/P+YWnHvqRxd/uc2ue+uDNDZSVWqsujJZKO/l55Xz92Y4a9x2fGI7DS82/Tifo1bf2yfDmwmbTPHSGTlNe5n3GQyDR0JX/HGCFlPIlIcQc1+9/9rJdhZRySAOPpVAEBDq9jtvuHcXVNwwhK6OQ2LgwYuOaN/E/7KKOLFt8yFPaWkLK0bwmO+4n72xi09ozIyYz0k7x8/cHePKly4iKDiY1Kd8jH6LZHWzdcIzf3zPqnPs2Bxm5/JoB/PjNmcErQoDJbPAYyBMIBAUbiYkN9bgrEQL69A/8wfANHeYyC5jvejwfuLKB+1MoWgyRUcH0G5RQK8dvsdhZvyqFn77bz9FDJxpcTtm2XZjXVacQkFiLcs/6kJNVzIY1qW4rfSmhpNjCkw8sIiOt0GcnlrdhN9644tqB/P6ekXToFEl4hJkLRnbimVenBWRVnRCC398zEpNZj3Ald/V6QVCwkd/cOszP1tVMQ1f+cVLKbNfjHMDX5S5ICLENsAMvSSm/b+BxFYoWQ0baKf7+5FLsdgd2m4bBqKdX33Y8+MTEOg0ur05kVDDDLurIji3H3cY3Gk36JhtPePTgSZ8VLDabg1efW+FDVlvHmAndanUMIQSjL+7G6Itrt72/6TcogafnTuPn7/eTdbyYHn1imTarX70HAzUnNTp/IcRywFuW84nqv0gppRDC13Kms5QyUwjRDVgphNgrpUw+eyMhxJ3AnQCdOgVOOZdCUV+klLw1d7VbTb6m2Tm8P5eVPx9uUFLwDw+M4YtPtrNmeRJ2u4N2CeHcevdFdKpFnX99iIgM8q5P5OJskTlwrvg7do3m6t8ObhKbAoGOXaK488HAExOsiRqdv5TyUl+vCSFyhRAJUspsIUQCcMLHPjJd/6YIIVYBQwEP5y+lnAfMAxg+fHjztxkqFI3MiZwSTuWXezx/unxx8uV96qToWR2jUc9Nd47gxtuHY7c7PKaAnUZKiabJet9lnGbAkATMQQYq66BFpNfreHruZfX+jIqmo6Ex/4XALa7HtwA/nL2BECJKCGF2PW4LjAEONPC4CkWLQDrwNVqW4mILP33X8MocnV7n1fFrmoMFn+3k7hu+4PZr/8uc+35g/+5sL3uoHXq9jr+8MIXQOmgQaXaHcvwBSkOd/0vAZCHEUeBS1+8IIYYLIT50bdMX2CaE2A38ijPmr5y/olUQlxjuu91fwk/fNd2fwqfztrBk0UHnSl1C9vFi3vjbr6Qm5dd7nwnt2/DGJ7PpPzgeg0FXVeoaFu7lgiA4p9aRwr8If4g41Ybhw4fLbdu2+dsMhR85mVvKprWpWCvtDL6wA917tW2Rq8iUo3k8++jPPl//5NvfNboUQFmplQd+/7XnkBQBQy/swIOPT2zwMU7mlpB1vJiE9hHY7Q6ee+xn7DYNm82B0ajDYNTz9MvTlJJmMyOE2C6lHF7TdqrDVxGQbFidwsfvbEI6JJrm4JdFBxk5tgu33TeqxV0AuvVsS/sOEWQeL/Z4LT4xvEk0YAryytAbvEzIkpDlQ120rsTGhbuVuc59dxa//nKEtJQCunSLZuJlvWgT6XvIisK/KOevCDjKSq18/M4mtxJGq0Vj8/p0LhrXhQFDWt70pd/dOYLXX/gVa7XPZDLrueG22nUF15W2cWFeJ2QJAZ26RTfJMdtEBnPl9edvVc/5RkNj/gpFo7NvVxZ6L01Blko7m9akNb9BjUC/QQk8+uyl9B0YT0SbIHr3a8cjT01i8PDG09WpTnCwkUun98ZkdhdUM5r0zArAbtmGcDK3hEUL9vL1ZztJOnTSL/MIWiJq5a8IOJzjDj2dvxCgb2C5oj/p1bcdc56f3GzHu+7mYbSJCuaX7w9QWmKhS/cYbrj9glrp/dfEydxSNM1BXEK4X8Nw61el8Mm7m3A4JA7NwdJFB7loTBdu/7+WFx5sbpTzVwQcA4Yk4HB4hiyMJn2tO0UVTkG0abP6MW1Wv0bbZ05mMW/OXc2JnBKEgLBwM398ZBy9+rZrtGPUlrJSK5+86xke3LIhnZHjW2Z4sDlpucsoxXlLULCRex8dj8msx2TWYzDqMJr0TJ7Rh179mt/JKJzYbRovPrGErIxCbC7J5oK8cl59dgVFhRXNbs/+3dmuu0R3WnJ4sDlRK39FQDJkeAde/2A22zYfw1ppZ9Cw9sS3r1mjXtF07NmRhcVi99DvcWiSdSuTmXF102gK+cJXlZQQtReSa80o568IWMIizEyY3NPfZjQaxYUVFJ6qIC4xot5a/v7kVEG51/GRNptGfp6nhEVTM2BIgtfkrgSOpxdyeH8uvVuAtLK/UGEfhaIJ0DRHlWOyWOy8NXc1D/3hW/72+BLuu/krfvxmX7PYUVlh48jBE+Rme/YY1JUevWO9CruZgwx+0a8PCjZy75/GYzTp3KvDJCQfyePVZ1eweV1as9vVUmh5yw+FIoA5cuAE/3l/M8ePFWIyGZgwtSeFBRXs3paJ3eaoGvn3w1d7iI0L46KxXZrMll8WHuCbz3ahN+jQ7A46doniwccnEFHPxqvO3aIZODSRvTuzqoatGI064hLCGXZRx8Y0vdbExocRFGyktNji8ZrVqvHZB1u5cHTngB+m7g/Uyl+haCSOHyvklWeXk5FeiJTOFf/KX46wZX1a1XDv01gtGoubcPW/d2cW3/x3F1arRkW5DatVIy05nzdfWt2g/d776Hiuv9VZLprQIYKZ1wzkyb9PbbBiaH2QUvLPF1dRUmzxNUOGigobhQXNH5JqCaiVv0LRSPz07T4POYXqZYhnU1hY2WS2/Pz9fo8Rj5omSUsp4GRuKbFxYfXar16vY9K03kya1rsxzGwQ2ZnFFOSXOYP8PpBSElIHFdLWhFr5KxSNREZaoc+B3mcjdKJJ4+RFPi4ser2O0hLPEElLxGbV0J2jkcto1DNiTGeCgo3NaFXLQTl/haKR6NIjxmtsWa8XGE1n/tR0OkGQ2cDsG5tOB2fwBe0xGD3/vKWUtO/UNDN+m5uOnSMxGPVeX9PpBIMvbM+tfxzZzFa1HJTzVygaiRlX98docndGJrOe8ZN68KenL2XgsETiEyMYe0l3nn9jBnEJTde3MO3KfoSFm90uACaznt/+/gJMJu8Os6Wh0+u4++GxzkZAV87BZNaT0D6Cl9+7kv977OIWWVLbXCg9f4WiEUlPKeC/H20j+fBJQkJNTJnZhxlX9UfnpRO1qSkttvDLooPs3ZFJVEwIl83q55eSzKYm70Qpa5YncSq/nP5DEhg+qrNfEtCBQm31/JXzVygUivOI2jr/1nt5VChaEZZKG4f25ZKeUqAkjxWAKvVUKM57Vi87ymcfbkWv1+FwSCKjgnnk6UnEJYTX/GbFeYta+SsU5zEpR/P47IOtWC3OZi9LpZ0TOSW88sxydQfQylHOX6E4j1nx02GP7mIpobiokuQjeX6yShEIKOevUJzHFBVWepU+0OnEedPspagfyvkrFOcxQ0d08JjjC2C3OejRO9YPFikCBeX8FYrzmLGXdCe2XZhbY5fJrGfWbwYRFm72o2UKf6OqfRSK8xiz2cBfX5nGqmVJbNt4jNAwE5Nn9KH/4AR/m6bwM8r5KxTnOeYgI1Nn9mXqzL7+NkURQKiwj0KhULRClPNXKBSKVogK+ygUimbBYrGz4qfDbF6XhjnIwKRpvRkxpjPiHJr8iqZDOX+FQtHk2Gwaz//5F3Kyiqumm6UlFXD4wAluvnOEn61rnaiwj0KhaHK2rEvnRE6J21hLi8XOmmVJnMwt8aNlrRfl/BUKRZOzd2cmlkq7x/M6veDIwZN+sEihnL9CoWhyomJC0Os9Y/tCQESbID9YpFDOX6FQNDkTpvREf9Y0MyEgKNhI/0HxfrKqddMg5y+EuFYIsV8I4RBC+JwcI4S4TAhxWAiRJISY05BjKhSKlkdcQgT3/GkcIaEmgoINmMx64hIj+MvzU/wy4lLR8GqffcDVwPu+NhBC6IF3gMnAcWCrEGKhlPJAA4+tUChaEENHdOSt+deSkXYKs9lAQocIVebpRxrk/KWUB4Ga/gNHAElSyhTXtl8AswDl/BWKVobBoKNrjxh/m6GgeWL+7YGMar8fdz2nUCgUCj9R48pfCLEc8JaReUJK+UNjGiOEuBO4E6BTp06NuWuFQqFQVKNG5y+lvLSBx8gEOlb7vYPrOW/HmgfMAxg+fLgaMKpQKBRNRHOEfbYCPYUQXYUQJuB6YGEzHFehUCgUPmhoqedVQojjwChgsRBiiev5RCHETwBSSjtwH7AEOAh8JaXc3zCzFQqFQtEQGlrt8x3wnZfns4Dp1X7/CfipIcdSKBQKReMhpAzM0LoQ4iSQXu2ptkCen8ypC8rOxkXZ2bgoOxuXQLSzs5QytqaNAtb5n40QYpuU0mcXcaCg7GxclJ2Ni7KzcWkpdnpD9VUrFApFK0Q5f4VCoWiFtCTnP8/fBtQSZWfjouxsXJSdjUtLsdODFhPzVygUCkXj0ZJW/gqFQqFoJJTzVygUilZIwDr/OgyKSRNC7BVC7BJCbGtOG13HbxEDbYQQ0UKIZUKIo65/o3xsp7nO5S4hRLPJcNR0foQQZiHEl67XNwshujSXbWfZUZOdtwohTlY7h3f4wcaPhRAnhBD7fLwuhBBvuj7DHiHEsOa20WVHTXZOEEIUVTuXTze3jS47OgohfhVCHHD9rT/gZZuAOKd1QkoZkD9AX6A3sAoYfo7t0oC2gWwnoAeSgW6ACdgN9GtmO18G5rgezwHm+tiu1A/nsMbzA9wDvOd6fD3wZYDaeSvwdnPbdpYN44FhwD4fr08HfgYEMBLYHKB2TgB+9Oe5dNmRAAxzPQ4Hjnj5fw+Ic1qXn4Bd+UspD0opD/vbjpqopZ1VA22klFbg9ECb5mQWMN/1eD5wZTMf/1zU5vxUt38BMEk0/xioQPh/rBEp5Rqg4BybzAL+I51sAiKFEAnNY90ZamFnQCClzJZS7nA9LsGpUXb2TJKAOKd1IWCdfx2QwFIhxHbXPIBAJBAG2sRJKbNdj3OAOB/bBQkhtgkhNgkhmusCUZvzU7WNdIoFFgHNPRKqtv+Ps123/guEEB29vO5vAuH7WFtGCSF2CyF+FkL097cxrnDjUGDzWS+1pHMKNHyGb4NopEExY6WUmUKIdsAyIcQh14qi0WjOgTYN4Vx2Vv9FSimFEL5qfDu7zmc3YKUQYq+UMrmxbT2PWQR8LqW0CCHuwnm3comfbWqp7MD5fSwVQkwHvgd6+ssYIUQY8A3woJSy2F92NBZ+df6y4YNikFJmuv49IYT4DueteaM6/0aws9YDbRrCuewUQuQKIRKklNmu29ETPvZx+nymCCFW4VzlNLXzr835Ob3NcSGEAWgD5DexXWdTo51Syuo2fYgz1xJoNMv3saFUd7BSyp+EEO8KIdpKKZtdSE0IYcTp+P8rpfzWyyYt4pxWp0WHfYQQoUKI8NOPgSmA18oBPxMIA20WAre4Ht8CeNyxCCGihBBm1+O2wBjgQDPYVpvzU93+a4CV0pVpa0ZqtPOsOO8VOOPDgcZC4GZXhcpIoKhaSDBgEELEn87rCCFG4PRXzX3Bx2XDR8BBKeVrPjZrEefUDX9nnH39AFfhjJtZgFxgiev5ROAn1+NuOCsudgP7cYZhAs5OeaYa4AjOVbQ/7IwBVgBHgeVAtOv54cCHrsejgb2u87kXuL0Z7fM4P8BzwBWux0HA10ASsAXo5qfvZU12/t31XdwN/Ar08YONnwPZgM313bwduBu42/W6AN5xfYa9nKOazs923lftXG4CRvvJzrE4c4t7gF2un+mBeE7r8qPkHRQKhaIV0qLDPgqFQqGoH8r5KxQKRStEOX+FQqFohSjnr1AoFK0Q5fwVCoWiFaKcv0KhULRClPNXKBSKVsj/A5TziHMqXS7iAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot data\n",
+    "y_pred = np.zeros(nn.z2.shape[0])\n",
+    "y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1\n",
+    "\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=nn.y, cmap=plt.cm.Spectral)\n",
+    "plt.title(\"ground truth\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n",
+    "plt.title(\"predicted\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 如何使用类的方法封装多层神经网络?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "shapes (200,2) and (4,2) not aligned: 2 (dim 1) != 4 (dim 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-56-87b1e2b98e79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0mnn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNN_Model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackpropagation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-56-87b1e2b98e79>\u001b[0m in \u001b[0;36mbackpropagation\u001b[0;34m(self, X, y, n_epoch, epsilon)\u001b[0m\n\u001b[1;32m     75\u001b[0m                     \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m                     \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m                 \u001b[0md0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: shapes (200,2) and (4,2) not aligned: 2 (dim 1) != 4 (dim 0)"
+     ]
+    }
+   ],
+   "source": [
+    "% matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# defin sigmod & its derivate function\n",
+    "def sigmod(X):\n",
+    "    return 1.0/(1+np.exp(-X))\n",
+    "\n",
+    "\n",
+    "# generate the NN model\n",
+    "class NN_Model:\n",
+    "    def __init__(self, nodes=None):\n",
+    "        self.epsilon = 0.01                 # learning rate\n",
+    "        self.n_epoch = 1000                 # iterative number\n",
+    "        \n",
+    "        if not nodes:\n",
+    "            self.nodes = [2, 4, 2]          # default nodes size (from input -> output)\n",
+    "        else:\n",
+    "            self.nodes = nodes\n",
+    "    \n",
+    "    def init_weight(self):\n",
+    "        W = []\n",
+    "        B = []\n",
+    "        \n",
+    "        n_layer = len(self.nodes)\n",
+    "        for i in range(n_layer-1):\n",
+    "            w = np.random.randn(self.nodes[i], self.nodes[i+1]) / np.sqrt(self.nodes[i])\n",
+    "            b = np.random.randn(1, self.nodes[i+1])\n",
+    "            \n",
+    "            W.append(w)\n",
+    "            B.append(b)\n",
+    "            \n",
+    "        self.W = W\n",
+    "        self.B = B\n",
+    "    \n",
+    "    def forward(self, X):\n",
+    "        Z = []\n",
+    "        x0 = X\n",
+    "        for i in range(len(self.nodes)-1):\n",
+    "            z = sigmod(np.dot(x0, self.W[i]) + self.B[i])\n",
+    "            x0 = z\n",
+    "            \n",
+    "            Z.append(z)\n",
+    "        \n",
+    "        self.Z = Z\n",
+    "        \n",
+    "    # back-propagation\n",
+    "    def backpropagation(self, X, y, n_epoch=None, epsilon=None):\n",
+    "        if not n_epoch: n_epoch = self.n_epoch\n",
+    "        if not epsilon: epsilon = self.epsilon\n",
+    "        \n",
+    "        self.X = X\n",
+    "        self.Y = y\n",
+    "        \n",
+    "        for i in range(n_epoch):\n",
+    "            # forward to calculate each node's output\n",
+    "            self.forward(X)\n",
+    "            \n",
+    "            # calc weights update\n",
+    "            W = self.W\n",
+    "            B = self.B\n",
+    "            Z = self.Z\n",
+    "            \n",
+    "            D = []\n",
+    "            d0 = y\n",
+    "            n_layer = len(self.nodes)\n",
+    "            for j in range(n_layer-1, 0, -1):\n",
+    "                jj = j - 1\n",
+    "                z = self.Z[jj]\n",
+    "                \n",
+    "                if j == n_layer - 1:\n",
+    "                    d = z*(1-z)*(d0 - z)\n",
+    "                else:\n",
+    "                    d = z*(1-z)*np.dot(d0, W[jj].T)\n",
+    "                    \n",
+    "                d0 = d\n",
+    "                D.insert(0, d)\n",
+    "            \n",
+    "            # update weights\n",
+    "            for j in range(n_layer-1, 0, -1):\n",
+    "                jj = j - 1\n",
+    "                \n",
+    "                if jj != 0:\n",
+    "                    W[jj] += epsilon * np.dot(Z[jj-1].T, D[jj])\n",
+    "                else:\n",
+    "                    W[jj] += epsilon * np.dot(X.T, D[jj])\n",
+    "                    \n",
+    "                B[jj] += epsilon * np.sum(D[jj], axis=0)\n",
+    "        \n",
+    "    def evaulate(self):\n",
+    "        z = self.Z[-1]\n",
+    "        \n",
+    "        # print loss, accuracy\n",
+    "        L = np.sum((z - self.Y)**2)\n",
+    "            \n",
+    "        y_pred = np.argmax(z)\n",
+    "        y_true = np.argmax(self.Y)\n",
+    "        acc = accuracy_score(y_true, y_pred)\n",
+    "        \n",
+    "        print(\"L = %f, acc = %f\" % (L, acc))\n",
+    "        \n",
+    "\n",
+    "\n",
+    "\n",
+    "# generate sample data\n",
+    "np.random.seed(0)\n",
+    "X, y = datasets.make_moons(200, noise=0.20)\n",
+    "\n",
+    "# generate nn output target\n",
+    "t = np.zeros((X.shape[0], 2))\n",
+    "t[np.where(y==0), 0] = 1\n",
+    "t[np.where(y==1), 1] = 1\n",
+    "\n",
+    "# plot data\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "nn = NN_Model([2, 4, 2])\n",
+    "nn.init_weight()\n",
+    "nn.backpropagation(X, t)\n",
+    "\n",
+    "nn.evaluate()\n"
    ]
   },
   {
@@ -592,8 +2673,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.5.2"
-  },
-  "main_language": "python"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/nn/mlp_bp.py b/nn/mlp_bp.py
index bd4a6e4..36a806c 100644
--- a/nn/mlp_bp.py
+++ b/nn/mlp_bp.py
@@ -256,6 +256,11 @@ import matplotlib.pyplot as plt
 np.random.seed(0)
 X, y = datasets.make_moons(200, noise=0.20)
 
+# generate nn output target
+t = np.zeros((X.shape[0], 2))
+t[np.where(y==0), 0] = 1
+t[np.where(y==1), 1] = 1
+
 # plot data
 plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
 plt.show()
@@ -269,7 +274,10 @@ class NN_Model:
 nn = NN_Model()
 nn.n_input_dim = X.shape[1]      # input size
 nn.n_output_dim = 2              # output node size
-nn.n_hide_dim = 3                # hidden node size
+nn.n_hide_dim = 4                # hidden node size
+
+nn.X = X
+nn.y = y
 
 # initial weight array
 nn.W1 = np.random.randn(nn.n_input_dim, nn.n_hide_dim) / np.sqrt(nn.n_input_dim)
@@ -291,13 +299,195 @@ def forward(n, X):
     n.z2 = sigmod(n.z1.dot(n.W2) + n.b2)
     return n
 
+
 # use random weight to perdict
 forward(nn, X)
-y = nn.z2[:, 0]>nn.z2[:,1]
 y_pred = np.zeros(nn.z2.shape[0])
 y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1
-print(y_pred)
-print(nn.z2)
+
+# plot data
+plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)
+plt.show()
+
+# +
+from sklearn.metrics import accuracy_score
+
+y_true = np.array(nn.y).astype(float)
+
+# back-propagation
+def backpropagation(n, X, y):
+    for i in range(n.n_epoch):
+        # forward to calculate each node's output
+        forward(n, X)
+        
+        # print loss, accuracy
+        L = np.sum((n.z2 - y)**2)
+        
+        y_pred = np.zeros(nn.z2.shape[0])
+        y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1
+        acc = accuracy_score(y_true, y_pred)
+        
+        print("epoch [%4d] L = %f, acc = %f" % (i, L, acc))
+        
+        # calc weights update
+        d2 = n.z2*(1-n.z2)*(y - n.z2)
+        d1 = n.z1*(1-n.z1)*(np.dot(d2, n.W2.T))
+        
+        # update weights
+        n.W2 += n.epsilon * np.dot(n.z1.T, d2)
+        n.b2 += n.epsilon * np.sum(d2, axis=0)
+        n.W1 += n.epsilon * np.dot(X.T, d1)
+        n.b1 += n.epsilon * np.sum(d1, axis=0)
+
+nn.n_epoch = 2000
+backpropagation(nn, X, t)
+
+
+# +
+# plot data
+y_pred = np.zeros(nn.z2.shape[0])
+y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1
+
+plt.scatter(X[:, 0], X[:, 1], c=nn.y, cmap=plt.cm.Spectral)
+plt.title("ground truth")
+plt.show()
+
+plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)
+plt.title("predicted")
+plt.show()
+
+# -
+
+# ## 如何使用类的方法封装多层神经网络?
+
+# +
+% matplotlib inline
+
+import numpy as np
+from sklearn import datasets, linear_model
+import matplotlib.pyplot as plt
+
+
+# defin sigmod & its derivate function
+def sigmod(X):
+    return 1.0/(1+np.exp(-X))
+
+
+# generate the NN model
+class NN_Model:
+    def __init__(self, nodes=None):
+        self.epsilon = 0.01                 # learning rate
+        self.n_epoch = 1000                 # iterative number
+        
+        if not nodes:
+            self.nodes = [2, 4, 2]          # default nodes size (from input -> output)
+        else:
+            self.nodes = nodes
+    
+    def init_weight(self):
+        W = []
+        B = []
+        
+        n_layer = len(self.nodes)
+        for i in range(n_layer-1):
+            w = np.random.randn(self.nodes[i], self.nodes[i+1]) / np.sqrt(self.nodes[i])
+            b = np.random.randn(1, self.nodes[i+1])
+            
+            W.append(w)
+            B.append(b)
+            
+        self.W = W
+        self.B = B
+    
+    def forward(self, X):
+        Z = []
+        x0 = X
+        for i in range(len(self.nodes)-1):
+            z = sigmod(np.dot(x0, self.W[i]) + self.B[i])
+            x0 = z
+            
+            Z.append(z)
+        
+        self.Z = Z
+        
+    # back-propagation
+    def backpropagation(self, X, y, n_epoch=None, epsilon=None):
+        if not n_epoch: n_epoch = self.n_epoch
+        if not epsilon: epsilon = self.epsilon
+        
+        self.X = X
+        self.Y = y
+        
+        for i in range(n_epoch):
+            # forward to calculate each node's output
+            self.forward(X)
+            
+            # calc weights update
+            W = self.W
+            B = self.B
+            Z = self.Z
+            
+            D = []
+            d0 = y
+            n_layer = len(self.nodes)
+            for j in range(n_layer-1, 0, -1):
+                jj = j - 1
+                z = self.Z[jj]
+                
+                if j == n_layer - 1:
+                    d = z*(1-z)*(d0 - z)
+                else:
+                    d = z*(1-z)*np.dot(d0, W[jj].T)
+                    
+                d0 = d
+                D.insert(0, d)
+            
+            # update weights
+            for j in range(n_layer-1, 0, -1):
+                jj = j - 1
+                
+                if jj != 0:
+                    W[jj] += epsilon * np.dot(Z[jj-1].T, D[jj])
+                else:
+                    W[jj] += epsilon * np.dot(X.T, D[jj])
+                    
+                B[jj] += epsilon * np.sum(D[jj], axis=0)
+        
+    def evaulate(self):
+        z = self.Z[-1]
+        
+        # print loss, accuracy
+        L = np.sum((z - self.Y)**2)
+            
+        y_pred = np.argmax(z)
+        y_true = np.argmax(self.Y)
+        acc = accuracy_score(y_true, y_pred)
+        
+        print("L = %f, acc = %f" % (L, acc))
+        
+
+
+
+# generate sample data
+np.random.seed(0)
+X, y = datasets.make_moons(200, noise=0.20)
+
+# generate nn output target
+t = np.zeros((X.shape[0], 2))
+t[np.where(y==0), 0] = 1
+t[np.where(y==1), 1] = 1
+
+# plot data
+plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
+plt.show()
+
+
+nn = NN_Model([2, 4, 2])
+nn.init_weight()
+nn.backpropagation(X, t)
+
+nn.evaluate()
+
 # -
 
 # ## References
diff --git a/references/References.md b/references/References.md
new file mode 100644
index 0000000..ba172f7
--- /dev/null
+++ b/references/References.md
@@ -0,0 +1,46 @@
+## Notebooks:
+
+* machineLearning/10_digits_classification.ipynb
+* MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
+* MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb
+
+
+## Exercise
+* http://sofasofa.io/competitions.php?type=practice
+* https://www.kaggle.com/competitions
+
+
+* Titanic: notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb
+
+
+## Method
+
+* Programming Multiclass Logistic Regression
+notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
+
+* Equation for MLP
+notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb
+
+* Optimization methods
+notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb
+
+
+* https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md
+
+* evaluation metrics
+http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb
+
+
+* model selection and assessment
+http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb
+
+
+## NN
+* 神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317
+* 零基础入门深度学习(3) - 神经网络和反向传播算法 https://www.zybuluo.com/hanbingtao/note/476663
+* 如何直观地解释 backpropagation 算法？ https://www.zhihu.com/question/27239198
+* 一文弄懂神经网络中的反向传播法——BackPropagation https://www.cnblogs.com/charlotte77/p/5629865.html
+
+* https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf
+* https://enlight.nyc/projects/neural-network/
+* https://www.python-course.eu/neural_networks_with_python_numpy.php
\ No newline at end of file
diff --git a/references/nn/.nn_2.py.kate-swp b/references/nn/.nn_2.py.kate-swp
deleted file mode 100644
index f37ba8f..0000000
Binary files a/references/nn/.nn_2.py.kate-swp and /dev/null differ
diff --git a/references/nn/nn_3.py b/references/nn/nn_3.py
new file mode 100644
index 0000000..df46785
--- /dev/null
+++ b/references/nn/nn_3.py
@@ -0,0 +1,46 @@
+%matplotlib nbagg
+
+from sklearn.metrics import accuracy_score
+
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+fig = plt.figure()
+imgs = []
+
+y_true = np.array(nn.y).astype(float)
+
+# back-propagation
+def backpropagation(n, X, y):
+    for i in range(n.n_epoch):
+        # forward to calculate each node's output
+        forward(n, X)
+        
+        # print loss, accuracy
+        L = np.sum((n.z2 - y)**2)
+        
+        y_pred = np.zeros(nn.z2.shape[0])
+        y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1
+        acc = accuracy_score(y_true, y_pred)
+        
+        print("epoch [%4d] L = %f, acc = %f" % (i, L, acc))
+        
+        # calc weights update
+        d2 = n.z2*(1-n.z2)*(y - n.z2)
+        d1 = n.z1*(1-n.z1)*(np.dot(d2, n.W2.T))
+        
+        # update weights
+        n.W2 += n.epsilon * np.dot(n.z1.T, d2)
+        n.b2 += n.epsilon * np.sum(d2, axis=0)
+        n.W1 += n.epsilon * np.dot(X.T, d1)
+        n.b1 += n.epsilon * np.sum(d1, axis=0)
+        
+        # plot animation
+        #img = plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)
+        #imgs.append(img)
+
+nn.n_epoch = 2000
+backpropagation(nn, X, t)
+
+#ani = animation.ArtistAnimation(fig, imgs)
+#plt.show()
diff --git a/references/nn/nn_4.py b/references/nn/nn_4.py
new file mode 100644
index 0000000..0cb433b
--- /dev/null
+++ b/references/nn/nn_4.py
@@ -0,0 +1,125 @@
+#% matplotlib inline
+
+import numpy as np
+from sklearn import datasets, linear_model
+import matplotlib.pyplot as plt
+
+
+# define sigmod & its derivate function
+def sigmod(X):
+    return 1.0/(1+np.exp(-X))
+
+
+# generate the NN model
+class NN_Model:
+    def __init__(self, nodes=None):
+        self.epsilon = 0.01                 # learning rate
+        self.n_epoch = 1000                 # iterative number
+        
+        if not nodes:
+            self.nodes = [2, 4, 2]          # default nodes size (from input -> output)
+        else:
+            self.nodes = nodes
+    
+    def init_weight(self):
+        W = []
+        B = []
+        
+        n_layer = len(self.nodes)
+        for i in range(n_layer-1):
+            w = np.random.randn(self.nodes[i], self.nodes[i+1]) / np.sqrt(self.nodes[i])
+            b = np.random.randn(1, self.nodes[i+1])
+            
+            W.append(w)
+            B.append(b)
+            
+        self.W = W
+        self.B = B
+    
+    def forward(self, X):
+        Z = []
+        x0 = X
+        for i in range(len(self.nodes)-1):
+            z = sigmod(np.dot(x0, self.W[i]) + self.B[i])
+            x0 = z
+            
+            Z.append(z)
+        
+        self.Z = Z
+        
+    # back-propagation
+    def backpropagation(self, X, y, n_epoch=None, epsilon=None):
+        if not n_epoch: n_epoch = self.n_epoch
+        if not epsilon: epsilon = self.epsilon
+        
+        self.X = X
+        self.Y = y
+        
+        for i in range(n_epoch):
+            # forward to calculate each node's output
+            self.forward(X)
+            
+            # calc weights update
+            W = self.W
+            B = self.B
+            Z = self.Z
+            
+            D = []
+            d0 = y
+            n_layer = len(self.nodes)
+            for j in range(n_layer-1, 0, -1):
+                jj = j - 1
+                z = self.Z[jj]
+                
+                if j == n_layer - 1:
+                    d = z*(1-z)*(d0 - z)
+                else:
+                    d = z*(1-z)*np.dot(d0, W[jj].T)
+                    
+                d0 = d
+                D.insert(0, d)
+            
+            # update weights
+            for j in range(n_layer-1, 0, -1):
+                jj = j - 1
+                
+                if jj != 0:
+                    W[jj] += epsilon * np.dot(Z[jj-1].T, D[jj])
+                else:
+                    W[jj] += epsilon * np.dot(X.T, D[jj])
+                    
+                B[jj] += epsilon * np.sum(D[jj], axis=0)
+        
+    def evaulate(self):
+        z = self.Z[-1]
+        
+        # print loss, accuracy
+        L = np.sum((z - self.Y)**2)
+            
+        y_pred = np.argmax(z)
+        y_true = np.argmax(self.Y)
+        acc = accuracy_score(y_true, y_pred)
+        
+        print("L = %f, acc = %f" % (L, acc))
+        
+
+
+# generate sample data
+np.random.seed(0)
+X, y = datasets.make_moons(200, noise=0.20)
+
+# generate nn output target
+t = np.zeros((X.shape[0], 2))
+t[np.where(y==0), 0] = 1
+t[np.where(y==1), 1] = 1
+
+# plot data
+plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
+plt.show()
+
+
+nn = NN_Model([2, 4, 2])
+nn.init_weight()
+nn.backpropagation(X, t)
+
+nn.evaluate()
diff --git a/InstallPython.md b/tips/InstallPython.md
similarity index 100%
rename from InstallPython.md
rename to tips/InstallPython.md
diff --git a/metric/confusion matrix.ipynb b/tips/confusion matrix.ipynb
similarity index 100%
rename from metric/confusion matrix.ipynb
rename to tips/confusion matrix.ipynb
diff --git a/metric/confusion matrix.py b/tips/confusion matrix.py
similarity index 100%
rename from metric/confusion matrix.py
rename to tips/confusion matrix.py
diff --git a/tips/datasets.ipynb b/tips/datasets.ipynb
new file mode 100644
index 0000000..b6c4f72
--- /dev/null
+++ b/tips/datasets.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Moons\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "% matplotlib inline\n",
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# generate sample data\n",
+    "np.random.seed(0)\n",
+    "X, y = datasets.make_moons(200, noise=0.20)\n",
+    "\n",
+    "# plot data\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## XOR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "X = rng.randn(200, 2)\n",
+    "Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)\n",
+    "\n",
+    "# plot data\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Spectral)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Digital "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 64 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt \n",
+    "from sklearn.datasets import load_digits\n",
+    "\n",
+    "# load data\n",
+    "digits = load_digits()\n",
+    "\n",
+    "# copied from notebook 02_sklearn_data.ipynb\n",
+    "fig = plt.figure(figsize=(6, 6))  # figure size in inches\n",
+    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
+    "\n",
+    "# plot the digits: each image is 8x8 pixels\n",
+    "for i in range(64):\n",
+    "    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
+    "    ax.imshow(digits.images[i], cmap=plt.cm.binary)\n",
+    "    \n",
+    "    # label the image with the target value\n",
+    "    ax.text(0, 7, str(digits.target[i]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  },
+  "main_language": "python"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tips/datasets.py b/tips/datasets.py
new file mode 100644
index 0000000..59ca2f5
--- /dev/null
+++ b/tips/datasets.py
@@ -0,0 +1,76 @@
+# ---
+# jupyter:
+#   jupytext_format_version: '1.2'
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+#   language_info:
+#     codemirror_mode:
+#       name: ipython
+#       version: 3
+#     file_extension: .py
+#     mimetype: text/x-python
+#     name: python
+#     nbconvert_exporter: python
+#     pygments_lexer: ipython3
+#     version: 3.5.2
+# ---
+
+# ## Datasets
+
+# ## Moons
+#
+
+# +
+% matplotlib inline
+import numpy as np
+from sklearn import datasets
+import matplotlib.pyplot as plt
+
+# generate sample data
+np.random.seed(0)
+X, y = datasets.make_moons(200, noise=0.20)
+
+# plot data
+plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
+plt.show()
+# -
+
+# ## XOR
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.gaussian_process import GaussianProcessClassifier
+
+rng = np.random.RandomState(0)
+X = rng.randn(200, 2)
+Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
+
+# plot data
+plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Spectral)
+plt.show()
+# -
+
+# ## Digital 
+
+# +
+import matplotlib.pyplot as plt 
+from sklearn.datasets import load_digits
+
+# load data
+digits = load_digits()
+
+# copied from notebook 02_sklearn_data.ipynb
+fig = plt.figure(figsize=(6, 6))  # figure size in inches
+fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
+
+# plot the digits: each image is 8x8 pixels
+for i in range(64):
+    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
+    ax.imshow(digits.images[i], cmap=plt.cm.binary)
+    
+    # label the image with the target value
+    ax.text(0, 7, str(digits.target[i]))
diff --git a/metric/images/confusion_matrix1.png b/tips/images/confusion_matrix1.png
similarity index 100%
rename from metric/images/confusion_matrix1.png
rename to tips/images/confusion_matrix1.png
diff --git a/metric/images/confusion_matrix2.png b/tips/images/confusion_matrix2.png
similarity index 100%
rename from metric/images/confusion_matrix2.png
rename to tips/images/confusion_matrix2.png
diff --git a/0_python/tips/README.md b/tips/python/README.md
similarity index 100%
rename from 0_python/tips/README.md
rename to tips/python/README.md
diff --git a/0_python/tips/pip.md b/tips/python/pip.md
similarity index 100%
rename from 0_python/tips/pip.md
rename to tips/python/pip.md
diff --git a/0_python/tips/virtualenv.md b/tips/python/virtualenv.md
similarity index 100%
rename from 0_python/tips/virtualenv.md
rename to tips/python/virtualenv.md
diff --git a/0_python/tips/virtualenv_wrapper.md b/tips/python/virtualenv_wrapper.md
similarity index 100%
rename from 0_python/tips/virtualenv_wrapper.md
rename to tips/python/virtualenv_wrapper.md