Merge pull request caicloud#36 from ScorpioCPH/minor-fix-comments

Minor fix comments
joelxiangnanchen · May 26, 2017 · 3fa01a0 · 3fa01a0
2 parents a784ef0 + 925302d
commit 3fa01a0
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Showing 25 changed files with 50 additions and 50 deletions.
diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/1. 全模型.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/1. 全模型.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/2. 不使用正则化.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/2. 不使用正则化.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/3. 不使用指数衰减的学习率.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/3. 不使用指数衰减的学习率.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/4. 不使用激活函数.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/4. 不使用激活函数.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.matmul(input_tensor, weights1) + biases1\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1)\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/5. 不使用隐藏层.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/5. 不使用隐藏层.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return layer1\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return layer1"
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/6. 不使用滑动平均.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter05/2. TensorFlow训练神经网络/6. 不使用滑动平均.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/1. 全模型.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/1. 全模型.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/2. 不使用正则化.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/2. 不使用正则化.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/3. 不使用指数衰减的学习率.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/3. 不使用指数衰减的学习率.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/4. 不使用激活函数.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/4. 不使用激活函数.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.matmul(input_tensor, weights1) + biases1\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1)\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/5. 不使用隐藏层.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/5. 不使用隐藏层.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return layer1\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return layer1"
    ]

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/6. 不使用滑动平均.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter05/2. TensorFlow训练神经网络/6. 不使用滑动平均.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/...g_with_TensorFlow/1.0.0/Chapter05/.ipynb_checkpoints/2. TensorFlow训练神经网络-checkpoint.ipynb b/...g_with_TensorFlow/1.0.0/Chapter05/.ipynb_checkpoints/2. TensorFlow训练神经网络-checkpoint.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/...TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/1. 全模型-checkpoint.ipynb b/...TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/1. 全模型-checkpoint.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/...sorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/2. 不使用正则化-checkpoint.ipynb b/...sorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/2. 不使用正则化-checkpoint.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/...ow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/3. 不使用指数衰减的学习率-checkpoint.ipynb b/...ow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/3. 不使用指数衰减的学习率-checkpoint.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/...orFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/4. 不使用激活函数-checkpoint.ipynb b/...orFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/4. 不使用激活函数-checkpoint.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.matmul(input_tensor, weights1) + biases1\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1)\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/...sorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/5. 不使用隐藏层-checkpoint.ipynb b/...sorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/5. 不使用隐藏层-checkpoint.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return layer1\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return layer1"
    ]

diff --git a/...orFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/6. 不使用滑动平均-checkpoint.ipynb b/...orFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/.ipynb_checkpoints/6. 不使用滑动平均-checkpoint.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/1. 全模型.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/1. 全模型.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/2. 不使用正则化.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/2. 不使用正则化.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/3. 不使用指数衰减的学习率.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/3. 不使用指数衰减的学习率.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/4. 不使用激活函数.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/4. 不使用激活函数.ipynb
@@ -57,13 +57,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.matmul(input_tensor, weights1) + biases1\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1)\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/5. 不使用隐藏层.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/5. 不使用隐藏层.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return layer1\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return layer1"
    ]

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/6. 不使用滑动平均.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter05/2. TensorFlow训练神经网络/6. 不使用滑动平均.ipynb
@@ -56,13 +56,13 @@
    "outputs": [],
    "source": [
     "def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n",
-    "    # 使用滑动平均类\n",
+    "    # 不使用滑动平均类\n",
     "    if avg_class == None:\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n",
     "        return tf.matmul(layer1, weights2) + biases2\n",
     "\n",
     "    else:\n",
-    "        # 不使用滑动平均类\n",
+    "        # 使用滑动平均类\n",
     "        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))\n",
     "        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  "
    ]