Improve some description

5_nn/1-Perceptron.ipynb

@@ -70,7 +70,7 @@
     "\n",
     "这样，假设超平面S的总的误分类点集合为M，那么所有误分类点到S的距离之和为\n",
     "$$\n",
-    "-\\frac{1}{||w||} \\sum_{x_i \\in M} y_i (w \\cdot x_i + b)\n",
+    "-\\frac{1}{||w||} \\sum_{x_i \\in M} |w \\cdot x_i + b|\n",
     "$$\n",
     "不考虑1/||w||，就得到了感知机学习的损失函数。\n",
     "\n",
@@ -156,10 +156,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "update weight and bias:  1.0 3.0 0.5\n",
-      "update weight and bias:  -2.5 1.5 0.0\n",
-      "w =  [-2.5, 1.5]\n",
-      "b =  0.0\n",
+      "update weight and bias:  0.5 1.5 0.5\n",
+      "update weight and bias:  -3.0 0.0 0.0\n",
+      "update weight and bias:  -2.0 3.0 0.5\n",
+      "w =  [-2.0, 3.0]\n",
+      "b =  0.5\n",
       "ground_truth:  [1, 1, 1, 1, -1, -1, -1, -1]\n",
       "predicted:     [1, 1, 1, 1, -1, -1, -1, -1]\n"
      ]
@@ -175,16 +176,17 @@
     "    else:      return -1\n",
     "    \n",
     "def perceptron_train(train_data, eta=0.5, n_iter=100):\n",
-    "    weight = [0, 0]  # 权重\n",
-    "    bias = 0  # 偏置量\n",
+    "    weight = [0, 0]      # 权重\n",
+    "    bias = 0             # 偏置量\n",
     "    learning_rate = eta  # 学习速率\n",
     "\n",
     "    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",
+    "        # need improve to cover all samples\n",
     "        train = random.choice(train_data)\n",
     "        x1, x2, y = train\n",
+    "        \n",
     "        predict = sign(weight[0] * x1 + weight[1] * x2 + bias)  # 输出\n",
     "        #print(\"train data: x: (%2d, %2d) y: %2d  ==> predict: %2d\" % (x1, x2, y, predict))\n",
     "        \n",

5_nn/3-softmax_ce.ipynb

@@ -268,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,