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practicegithub2016 · May 30, 2017 · d14da44 · d14da44
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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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -128,7 +128,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -121,7 +121,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -127,7 +127,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -126,7 +126,7 @@
     "    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter07/6. 输入数据处理框架.ipynb b/Deep_Learning_with_TensorFlow/0.12.0/Chapter07/6. 输入数据处理框架.ipynb
@@ -158,7 +158,7 @@
     "# 优化损失函数\n",
     "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n",
     "    \n",
-    "# 初始化回话并开始训练过程。\n",
+    "# 初始化会话，并开始训练过程。\n",
     "with tf.Session() as sess:\n",
     "    tf.global_variables_initializer().run()\n",
     "    coord = tf.train.Coordinator()\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -128,7 +128,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -121,7 +121,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -127,7 +127,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -126,7 +126,7 @@
     "    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter07/6. 输入数据处理框架.ipynb b/Deep_Learning_with_TensorFlow/0.12.1/Chapter07/6. 输入数据处理框架.ipynb
@@ -158,7 +158,7 @@
     "# 优化损失函数\n",
     "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n",
     "    \n",
-    "# 初始化回话并开始训练过程。\n",
+    "# 初始化会话，并开始训练过程。\n",
     "with tf.Session() as sess:\n",
     "    tf.global_variables_initializer().run()\n",
     "    coord = tf.train.Coordinator()\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -128,7 +128,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -121,7 +121,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -127,7 +127,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -126,7 +126,7 @@
     "    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -128,7 +128,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -121,7 +121,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -130,7 +130,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -127,7 +127,7 @@
     "    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

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
@@ -126,7 +126,7 @@
     "    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
     "    \n",
-    "    # 初始化回话并开始训练过程。\n",
+    "    # 初始化会话，并开始训练过程。\n",
     "    with tf.Session() as sess:\n",
     "        tf.global_variables_initializer().run()\n",
     "        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}\n",

diff --git a/..._Learning_with_TensorFlow/1.0.0/Chapter07/.ipynb_checkpoints/6. 输入数据处理框架-checkpoint.ipynb b/..._Learning_with_TensorFlow/1.0.0/Chapter07/.ipynb_checkpoints/6. 输入数据处理框架-checkpoint.ipynb
@@ -158,7 +158,7 @@
     "# 优化损失函数\n",
     "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n",
     "    \n",
-    "# 初始化回话并开始训练过程。\n",
+    "# 初始化会话，并开始训练过程。\n",
     "with tf.Session() as sess:\n",
     "    tf.global_variables_initializer().run()\n",
     "    coord = tf.train.Coordinator()\n",

diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter07/6. 输入数据处理框架.ipynb b/Deep_Learning_with_TensorFlow/1.0.0/Chapter07/6. 输入数据处理框架.ipynb
@@ -158,7 +158,7 @@
     "# 优化损失函数\n",
     "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n",
     "    \n",
-    "# 初始化回话并开始训练过程。\n",
+    "# 初始化会话，并开始训练过程。\n",
     "with tf.Session() as sess:\n",
     "    tf.global_variables_initializer().run()\n",
     "    coord = tf.train.Coordinator()\n",