实现孪生卷积神经网络时，验证是否共享了参数，进行了特征图可视化。由于卷积层特征图通道都很大，需要进行重新reshape成Tensorboard可以显示的通道数（１或３或４），这里全部变成了单通道的特征图。没有使用tf.reshape()是因为发现有问题，所以还是自己重新利用tf.concat进行reshape。
主要是最后两个函数，一个添加Tensorboard，一个对特征图进行reshape,很简单的思路。如果嫌特征图太小了，可以选择每次选一半的特征图进行reshape。见代码注释！
# coding=utf-8 """孪生卷积神经网络""" import tensorflow as tf import numpy as np import math class SiameseNet(object): def __init__(self): print("正在构建孪生网络...") self.opts = {'trainWeightDecay': 0.0, 'stddev': 0.01} def build_siamese_cnn_network(self, input): first_image = tf.expand_dims(input[:, :, :, 0], 3) second_image = tf.expand_dims(input[:, :, :, 1], 3) with tf.variable_scope('siamese_CNN') as scope: first_output = self._build_branch(first_image, True) scope.reuse_variables() second_output = self._build_branch(second_image, False) return first_output, second_output def _build_branch(self, image, branch): print("构建孪生网络分支...") with tf.variable_scope('conv_block1_1'): print("构建conv1,relu1...") name = tf.get_variable_scope().name outputs = self._conv(image, 64, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) with tf.variable_scope('conv_block1_2'): print("构建conv2,relu2,max-pooling1...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 64, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) outputs = self._maxPool(outputs, 2, 2) conv_block1 = outputs with tf.variable_scope('conv_block2_1'): print("构建conv3,relu3...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 64, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) with tf.variable_scope('conv_block2_2'): print("构建,conv4,relu4,max-pooling2...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 64, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) outputs = self._maxPool(outputs, 2, 2) conv_block2 = outputs with tf.variable_scope('conv_block3_1'): print("构建conv5,relu5...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 128, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) with tf.variable_scope('conv_block3_2'): print("构建conv6,relu6,max-pooling3...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 128, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) outputs = self._maxPool(outputs, 2, 2) conv_block3 = outputs with tf.variable_scope('conv_block4_1'): print("构建conv7,relu7...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 128, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) with tf.variable_scope('conv_block4_2'): print("构建conv8,relu8,max-pooling4...") name = tf.get_variable_scope().name outputs = self._conv(outputs, 128, 3, 1, self.opts['trainWeightDecay'], self.opts['stddev']) outputs = tf.nn.relu(outputs) if branch: self.conv_block1_1 = conv_block1 self.conv_block1_2 = conv_block2 self.conv_block1_3 = conv_block3 self.conv_block1_4 = outputs else: self.conv_block2_1 = conv_block1 self.conv_block2_2 = conv_block2 self.conv_block2_3 = conv_block3 self.conv_block2_4 = outputs return outputs def _conv(self, inputs, filters, size, stride, wd, stddev, name=None): channels = int(inputs.get_shape()[-1]) with tf.variable_scope('conv'): weights = self.getVariable('weights', shape=[size, size, channels, filters], initializer=tf.random_normal_initializer(), weightDecay=wd, dType=tf.float32, trainable=True) biases = self.getVariable('biases', shape=[filters, ], initializer=tf.constant_initializer(dtype=tf.float32), weightDecay=0.0, dType=tf.float32, trainable=True) p = np.floor((size - 1) / 2).astype(np.int32) p_x = tf.pad(inputs, [[0, 0], [p, p], [p, p], [0, 0]]) conv = tf.nn.conv2d(p_x, weights, strides=[1, stride, stride, 1], padding='VALID') if name is not None: conv = tf.add(conv, biases, name=name) else: conv = tf.add(conv, biases) print('Layer Type = Conv, Size = %d * %d, Stride = %d, Filters = %d, Input channels = %d' % ( size, size, stride, filters, channels)) return conv def _maxPool(self, inputs, kSize, _stride): with tf.variable_scope('poll'): p = np.floor((kSize - 1) / 2).astype(np.int32) p_x = tf.pad(inputs, [[0, 0], [p, p], [p, p], [0, 0]]) output = tf.nn.max_pool(p_x, ksize=[1, kSize, kSize, 1], strides=[1, _stride, _stride, 1], padding='VALID') return output def getVariable(self, name, shape, initializer, weightDecay=0.0, dType=tf.float32, trainable=True): if weightDecay > 0: regularizer = tf.contrib.layers.l2_regularizer(weightDecay) else: regularizer = None return tf.get_variable(name, shape=shape, initializer=initializer, dtype=dType, regularizer=regularizer, trainable=trainable) def build_summaries(self): with tf.name_scope('CNN_outputs'): tf.summary.image('conv_block1_1_1', self._concact_features(self.conv_block1_1[:, :, :, 0:16]), 1) # 取部分特征图显示 tf.summary.image('conv_block1_1_2', self._concact_features(self.conv_block1_1[:, :, :, 16:32]), 1) tf.summary.image('conv_block1_1_3', self._concact_features(self.conv_block1_1[:, :, :, 32:48]), 1) tf.summary.image('conv_block1_1_4', self._concact_features(self.conv_block1_1[:, :, :, 48:64]), 1) tf.summary.image('conv_block1_2', self._concact_features(self.conv_block1_2), 1) tf.summary.image('conv_block1_3', self._concact_features(self.conv_block1_3), 1) tf.summary.image('conv_block1_4', self._concact_features(self.conv_block1_4), 1) tf.summary.image('conv_block2_1', self._concact_features(self.conv_block2_1), 1) tf.summary.image('conv_block2_2', self._concact_features(self.conv_block2_2), 1) tf.summary.image('conv_block2_3', self._concact_features(self.conv_block2_3), 1) tf.summary.image('conv_block2_4', self._concact_features(self.conv_block2_4), 1) def _concact_features(self, conv_output): """ 对特征图进行reshape拼接 :param conv_output:输入多通道的特征图 :return: """ num_or_size_splits = conv_output.get_shape().as_list()[-1] each_convs = tf.split(conv_output, num_or_size_splits=num_or_size_splits, axis=3) concact_size = int(math.sqrt(num_or_size_splits) / 1) all_concact = None for i in range(concact_size): row_concact = each_convs[i * concact_size] for j in range(concact_size - 1): row_concact = tf.concat([row_concact, each_convs[i * concact_size + j + 1]], 1) if i == 0: all_concact = row_concact else: all_concact = tf.concat([all_concact, row_concact], 2) return all_concact 文章来源: Tensorboard特征图可视化