Small improvements of perceptron and bp

1_nn/Perceptron.ipynb

@@ -141,11 +141,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "lines_to_end_of_cell_marker": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "update weight and bias:  1.0 3.0 0.5\n",
+      "update weight and bias:  -0.5 2.5 0.0\n",
+      "update weight and bias:  -2.5 2.0 -0.5\n",
+      "w =  [-2.5, 2.0]\n",
+      "b =  -0.5\n",
+      "[ 1  1  1  1 -1 -1 -1 -1]\n",
+      "[1, 1, 1, 1, -1, -1, -1, -1]\n"
+     ]
+    }
+   ],
    "source": [
     "import random\n",
     "import numpy as np\n",
@@ -163,6 +177,7 @@
     "    train_num = n_iter  # 迭代次数\n",
     "\n",
     "    for i in range(train_num):\n",
+    "        #FIXME: the random chose sample is to slow\n",
     "        train = random.choice(train_data)\n",
     "        x1, x2, y = train\n",
     "        predict = sign(weight[0] * x1 + weight[1] * x2 + bias)  # 输出\n",

1_nn/Perceptron.py

@@ -154,6 +154,7 @@ def perceptron_train(train_data, eta=0.5, n_iter=100):
     train_num = n_iter  # 迭代次数
 
     for i in range(train_num):
+        #FIXME: the random chose sample is to slow
         train = random.choice(train_data)
         x1, x2, y = train
         predict = sign(weight[0] * x1 + weight[1] * x2 + bias)  # 输出