重新整理文件夹结构，并添加CNN训练代码。

tools/TrainCNN/forward.py

@@ -0,0 +1,98 @@
+import tensorflow as tf
+
+
+def get_weight(shape, regularizer=None):
+    w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
+    if regularizer is None:
+        tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
+    return w
+
+
+def get_bias(shape):
+    b = tf.Variable(tf.zeros(shape))
+    return b
+
+
+def conv2d(x, w):
+    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding="VALID")
+
+
+def avg_pool_2x2(x):
+    return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
+
+
+def max_pool_2x2(x):
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
+
+
+# 原图像行数
+SRC_ROWS = 36
+
+# 原图像列数
+SRC_COLS = 48
+
+# 原图像通道数
+SRC_CHANNELS = 1
+
+# 第一层卷积核大小
+CONV1_KERNAL_SIZE = 5
+
+# 第一层卷积输出通道数
+CONV1_OUTPUT_CHANNELS = 8
+
+# 第二层卷积核大小
+CONV2_KERNAL_SIZE = 3
+
+# 第二层卷积输出通道数
+CONV2_OUTPUT_CHANNELS = 16
+
+# 第一层全连接宽度
+FC1_OUTPUT_NODES = 32
+
+# 第二层全连接宽度（输出标签类型数）
+FC2_OUTPUT_NODES = 8
+
+# 输出标签类型数
+OUTPUT_NODES = FC2_OUTPUT_NODES
+
+
+def forward(x, regularizer=None):
+    vars = []
+    nodes = []
+
+    conv1_w = get_weight(
+        [CONV1_KERNAL_SIZE, CONV1_KERNAL_SIZE, 1, CONV1_OUTPUT_CHANNELS]
+    )
+    conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
+    conv1   = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
+    pool1   = avg_pool_2x2(conv1)
+    vars.extend([conv1_w, conv1_b])
+    nodes.extend([conv1, pool1])
+
+    conv2_w = get_weight(
+        [CONV2_KERNAL_SIZE, CONV2_KERNAL_SIZE, CONV1_OUTPUT_CHANNELS, CONV2_OUTPUT_CHANNELS]
+    )
+    conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
+    conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
+    pool2 = avg_pool_2x2(conv2)
+    vars.extend([conv2_w, conv2_b])
+    nodes.extend([conv2, pool2])
+
+    pool_shape = pool2.get_shape().as_list()
+    node = pool_shape[1] * pool_shape[2] * pool_shape[3]
+    reshaped = tf.reshape(pool2, [-1, node])
+
+    fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
+    fc1_b = get_bias([FC1_OUTPUT_NODES])
+    fc1   = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
+    vars.extend([fc1_w, fc1_b])
+    nodes.extend([fc1])
+
+    fc2_w = get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer)
+    fc2_b = get_bias([FC2_OUTPUT_NODES])
+    fc2   = tf.nn.softmax(tf.matmul(fc1, fc2_w) + fc2_b)
+    vars.extend([fc2_w, fc2_b])
+    nodes.extend([fc2])
+
+    return nodes, vars
+