117 lines
3.3 KiB
Python
117 lines
3.3 KiB
Python
import tensorflow as tf
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def get_weight(shape, regularizer=None):
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w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
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if regularizer is not None:
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tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
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return w
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def get_bias(shape):
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b = tf.Variable(tf.zeros(shape))
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return b
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def conv2d(x, w):
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return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding="VALID")
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def avg_pool_2x2(x):
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return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
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def max_pool_2x2(x):
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return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
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# 第一层卷积核大小
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CONV1_KERNAL_SIZE = 5
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# 第一层卷积输出通道数
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CONV1_OUTPUT_CHANNELS = 4
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# 第二层卷积核大小
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CONV2_KERNAL_SIZE = 3
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# 第二层卷积输出通道数
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CONV2_OUTPUT_CHANNELS = 6
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# 第三层卷积核大小
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CONV3_KERNAL_SIZE = 3
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# 第三层卷积输出通道数
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CONV3_OUTPUT_CHANNELS = 8
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# 第一层全连接宽度
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FC1_OUTPUT_NODES = 50
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# 第二层全连接宽度（输出标签类型数）
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FC2_OUTPUT_NODES = 15
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# 输出标签类型数
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OUTPUT_NODES = FC2_OUTPUT_NODES
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def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
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vars = []
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vars_name = []
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nodes = []
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conv1_w = get_weight(
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[CONV1_KERNAL_SIZE, CONV1_KERNAL_SIZE, int(x.shape[3]), CONV1_OUTPUT_CHANNELS]
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)
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conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
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conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
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pool1 = avg_pool_2x2(conv1)
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print("conv1: ", conv1.shape)
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print("pool1: ", pool1.shape)
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vars.extend([conv1_w, conv1_b])
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vars_name.extend(["conv1_w", "conv1_b"])
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nodes.extend([conv1, pool1])
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conv2_w = get_weight(
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[CONV2_KERNAL_SIZE, CONV2_KERNAL_SIZE, CONV1_OUTPUT_CHANNELS, CONV2_OUTPUT_CHANNELS]
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)
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conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
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conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
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pool2 = avg_pool_2x2(conv2)
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print("conv2: ", conv2.shape)
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print("pool2: ", pool2.shape)
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vars.extend([conv2_w, conv2_b])
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vars_name.extend(["conv2_w", "conv2_b"])
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nodes.extend([conv2, pool2])
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conv3_w = get_weight(
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[CONV3_KERNAL_SIZE, CONV3_KERNAL_SIZE, CONV2_OUTPUT_CHANNELS, CONV3_OUTPUT_CHANNELS]
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)
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conv3_b = get_bias([CONV3_OUTPUT_CHANNELS])
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conv3 = tf.nn.relu(tf.nn.bias_add(conv2d(pool2, conv3_w), conv3_b))
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print("conv3: ", conv3.shape)
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vars.extend([conv3_w, conv3_b])
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vars_name.extend(["conv3_w", "conv3_b"])
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nodes.extend([conv3])
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conv_shape = conv3.get_shape().as_list()
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node = conv_shape[1] * conv_shape[2] * conv_shape[3]
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reshaped = tf.reshape(conv3, [-1, node])
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reshaped = tf.nn.dropout(reshaped, keep_rate)
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print("reshaped: ", reshaped.shape)
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fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
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fc1_b = get_bias([FC1_OUTPUT_NODES])
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fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
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vars.extend([fc1_w, fc1_b])
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vars_name.extend(["fc1_w", "fc1_b"])
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nodes.extend([fc1])
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fc2_w = get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer)
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fc2_b = get_bias([FC2_OUTPUT_NODES])
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fc2 = tf.matmul(fc1, fc2_w) + fc2_b
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vars.extend([fc2_w, fc2_b])
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vars_name.extend(["fc2_w", "fc2_b"])
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nodes.extend([fc2])
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return nodes, vars, vars_name
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