前言
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。
效果
代码提示
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！
代码
# 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