本文最后更新于：14 天前
LeNet网络：
def LeNet(inputs):
mu = 0
sigma = 0.1
print(inputs.shape)
# TODO: 第一层卷积：输入=32x32x3, 输出=28x28x6
conv1_w = tf.Variable(tf.truncated_normal(shape=[5, 5, 3, 6], mean=mu, stddev=sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(inputs, conv1_w, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
print(conv1.shape)
# 激活函数
conv1_out = tf.nn.relu(conv1)
# 池化层， 输入=28x28x6, 输出=14x14x6
pool_1 = tf.nn.max_pool(conv1_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
print(pool_1.shape)
# TODO: 第二层卷积： 输入=14x14x6， 输出=10x10x16
conv2_w = tf.Variable(tf.truncated_normal(shape=[5, 5, 6, 16], mean=mu, stddev=sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(pool_1, conv2_w, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
print(conv2.shape)
# 激活函数
conv2_out = tf.nn.relu(conv2)
# 池化层， 输入=10x10x16, 输出=5x5x16
pool_2 = tf.nn.max_pool(conv2_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
print(pool_2.shape)
# Flatten 输入=5x5x16， 输出=400
pool_2_flat = tf.reshape(pool_2, [-1, 400])
# TODO: 第三层全连接层， 输入=400， 输出=120
fc1_w = tf.Variable(tf.truncated_normal(shape=[400, 120], mean=mu, stddev=sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(pool_2_flat, fc1_w) + fc1_b
# 激活函数
fc1_out = tf.nn.relu(fc1)
print(fc1_out.shape)
# TODO: 第四层全连接层： 输入=120， 输出=84
fc2_w = tf.Variable(tf.truncated_normal(shape=[120, 84], mean=mu, stddev=sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1_out, fc2_w) + fc2_b
# 激活函数
fc2_out = tf.nn.relu(fc2)
print(fc2_out.shape)
# TODO: 第五层全连接层： 输入=84， 输出=10
fc3_w = tf.Variable(tf.truncated_normal(shape=[84, 10], mean=mu, stddev=sigma))
fc3_b = tf.Variable(tf.zeros(10))
fc3_out = tf.matmul(fc2_out, fc3_w) + fc3_b
print(fc3_out.shape)
return fc3_outAlexNet网络：
# 卷积操作
def conv2d(name, l_input, w, b):
return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'), b), name=name)
# 最大下采样操作
def max_pool(name, l_input, k):
return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)
# 归一化操作
def norm(name, l_input, lsize=4):
return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)
# 定义整个网络
def alex_net(_X, _weights, _biases, _dropout):
# 向量转为矩阵
# _X = tf.reshape(_X, shape=[-1, 28, 28, 3])
print(_X.shape)
# TODO: 第一层卷积：
conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
# 下采样层
pool1 = max_pool('pool1', conv1, k=2)
# 归一化层
norm1 = norm('norm1', pool1, lsize=4)
print(norm1.shape)
# TODO: 第二层卷积：
conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
# 下采样
pool2 = max_pool('pool2', conv2, k=2)
# 归一化
norm2 = norm('norm2', pool2, lsize=4)
print(norm2.shape)
# TODO: 第三层卷积：
conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
# 归一化
norm3 = norm('norm3', conv3, lsize=4)
print(norm3.shape)
# TODO: 第四层卷积
# 卷积
conv4 = conv2d('conv4', norm3, _weights['wc4'], _biases['bc4'])
# 归一化
norm4 = norm('norm4', conv4, lsize=4)
print(norm4.shape)
# TODO: 第五层卷积
# 卷积
conv5 = conv2d('conv5', norm4, _weights['wc5'], _biases['bc5'])
# 下采样
pool5 = max_pool('pool5', conv5, k=2)
# 归一化
norm5 = norm('norm5', pool5, lsize=4)
print(norm5.shape)
# TODO: 第六层全连接层
# 先把特征图转为向量
dense1 = tf.reshape(norm5, [-1, _weights['wd1'].get_shape().as_list()[0]])
dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')
dense1 = tf.nn.dropout(dense1, _dropout)
print(dense1.shape)
# TODO: 第七层全连接层：
dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation
dense2 = tf.nn.dropout(dense2, _dropout)
print(dense2.shape)
# TODO: 第八层全连接层：
# 网络输出层
out = tf.matmul(dense2, _weights['out']) + _biases['out']
print(out.shape)
return outVGG16Net网络：
def VGG16(inputs):
print(inputs.shape)
# (32x32x3) --> (32x32x64)
with tf.name_scope('conv_1'):
conv_1_out = tf.layers.conv2d(inputs, 64, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_1_out.shape)
# (32x32x64) --> (32x32x64)
with tf.name_scope('conv_2'):
conv_2_out = tf.layers.conv2d(conv_1_out, 64, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_2_out.shape)
# (32x32x64) --> (16x16x64)
with tf.name_scope('pool_1'):
pool_1_out = tf.layers.max_pooling2d(conv_2_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_1_out.shape)
# (16x16x64) --> (16x16x128)
with tf.name_scope('conv_3'):
conv_3_out = tf.layers.conv2d(pool_1_out, 128, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_3_out.shape)
# (16x16x128) --> (16x16x128)
with tf.name_scope('conv_4'):
conv_4_out = tf.layers.conv2d(conv_3_out, 128, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_4_out.shape)
# (16x16x128) --> (8x8x128)
with tf.name_scope('pool_2'):
pool_2_out = tf.layers.max_pooling2d(conv_4_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_2_out.shape)
# (8x8x128) --> (8x8x256)
with tf.name_scope('conv_5'):
conv_5_out = tf.layers.conv2d(pool_2_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_5_out.shape)
# (8x8x256) --> (8x8x256)
with tf.name_scope('conv_6'):
conv_6_out = tf.layers.conv2d(conv_5_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_6_out.shape)
# (8x8x256) --> (8x8x256)
with tf.name_scope('conv_7'):
conv_7_out = tf.layers.conv2d(conv_6_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_7_out.shape)
# (8x8x256) --> (4x4x256)
with tf.name_scope('pool_3'):
pool_3_out = tf.layers.max_pooling2d(conv_7_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_3_out.shape)
# (4x4x256) --> (4x4x512)
with tf.name_scope('conv_8'):
conv_8_out = tf.layers.conv2d(pool_3_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_8_out.shape)
# (4x4x512) --> (4x4x512)
with tf.name_scope('conv_9'):
conv_9_out = tf.layers.conv2d(conv_8_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_9_out.shape)
# (4x4x512) --> (4x4x512)
with tf.name_scope('conv_10'):
conv_10_out = tf.layers.conv2d(conv_9_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_10_out.shape)
# (4x4x512) --> (2x2x512)
with tf.name_scope('pool_4'):
pool_4_out = tf.layers.max_pooling2d(conv_10_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_4_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_11'):
conv_11_out = tf.layers.conv2d(pool_4_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_11_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_12'):
conv_12_out = tf.layers.conv2d(conv_11_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_12_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_13'):
conv_13_out = tf.layers.conv2d(conv_12_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_13_out.shape)
# (2x2x512) --> (1x1x512)
with tf.name_scope('pool_5'):
pool_5_out = tf.layers.max_pooling2d(conv_13_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_5_out.shape)
# (1x1x512) --> 512
with tf.name_scope('fc_1'):
pool_5_outz_flat = tf.layers.flatten(pool_5_out)
fc_1_out = tf.layers.dense(pool_5_outz_flat, 512, activation=tf.nn.relu)
fc_1_drop = tf.nn.dropout(fc_1_out, keep_prob=config.keep_prob)
print(fc_1_drop.shape)
# 512 --> 512
with tf.name_scope('fc_2'):
fc_2_out = tf.layers.dense(fc_1_drop, 512, activation=tf.nn.relu)
fc_2_drop = tf.nn.dropout(fc_2_out, keep_prob=config.keep_prob)
print(fc_2_drop.shape)
# 512 --> 10
with tf.name_scope('fc_3'):
fc_3_out = tf.layers.dense(fc_2_drop, 10, activation=None)
print(fc_3_out.shape)
return fc_3_outVGG19Net:
def VGG19(inputs):
print(inputs.shape)
# (32x32x3) --> (32x32x64)
with tf.name_scope('conv_1'):
conv_1_out = tf.layers.conv2d(inputs, 64, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_1_out.shape)
# (32x32x64) --> (32x32x64)
with tf.name_scope('conv_2'):
conv_2_out = tf.layers.conv2d(conv_1_out, 64, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_2_out.shape)
# (32x32x64) --> (16x16x64)
with tf.name_scope('pool_1'):
pool_1_out = tf.layers.max_pooling2d(conv_2_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_1_out.shape)
# (16x16x64) --> (16x16x128)
with tf.name_scope('conv_3'):
conv_3_out = tf.layers.conv2d(pool_1_out, 128, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_3_out.shape)
# (16x16x128) --> (16x16x128)
with tf.name_scope('conv_4'):
conv_4_out = tf.layers.conv2d(conv_3_out, 128, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_4_out.shape)
# (16x16x128) --> (8x8x128)
with tf.name_scope('pool_2'):
pool_2_out = tf.layers.max_pooling2d(conv_4_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_2_out.shape)
# (8x8x128) --> (8x8x256)
with tf.name_scope('conv_5'):
conv_5_out = tf.layers.conv2d(pool_2_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_5_out.shape)
# (8x8x256) --> (8x8x256)
with tf.name_scope('conv_6'):
conv_6_out = tf.layers.conv2d(conv_5_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_6_out.shape)
# (8x8x256) --> (8x8x256)
with tf.name_scope('conv_7'):
conv_7_out = tf.layers.conv2d(conv_6_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_7_out.shape)
# (8x8x256) --> (8x8x256)
with tf.name_scope('conv_8'):
conv_8_out = tf.layers.conv2d(conv_7_out, 256, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_8_out.shape)
# (8x8x256) --> (4x4x256)
with tf.name_scope('pool_3'):
pool_3_out = tf.layers.max_pooling2d(conv_8_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_3_out.shape)
# (4x4x256) --> (4x4x512)
with tf.name_scope('conv_9'):
conv_9_out = tf.layers.conv2d(pool_3_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_9_out.shape)
# (4x4x512) --> (4x4x512)
with tf.name_scope('conv_10'):
conv_10_out = tf.layers.conv2d(conv_9_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_10_out.shape)
# (4x4x512) --> (4x4x512)
with tf.name_scope('conv_11'):
conv_11_out = tf.layers.conv2d(conv_10_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_11_out.shape)
# (4x4x512) --> (4x4x512)
with tf.name_scope('conv_12'):
conv_12_out = tf.layers.conv2d(conv_11_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_12_out.shape)
# (4x4x512) --> (2x2x512)
with tf.name_scope('pool_4'):
pool_4_out = tf.layers.max_pooling2d(conv_12_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_4_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_13'):
conv_13_out = tf.layers.conv2d(pool_4_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_13_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_14'):
conv_14_out = tf.layers.conv2d(conv_13_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_14_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_15'):
conv_15_out = tf.layers.conv2d(conv_14_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_15_out.shape)
# (2x2x512) --> (2x2x512)
with tf.name_scope('conv_16'):
conv_16_out = tf.layers.conv2d(conv_15_out, 512, [3, 3],
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(mean=0., stddev=0.1))
print(conv_16_out.shape)
# (2x2x512) --> (1x1x512)
with tf.name_scope('pool_5'):
pool_5_out = tf.layers.max_pooling2d(conv_16_out, pool_size=[2, 2], strides=[2, 2], padding='same')
print(pool_5_out.shape)
# (1x1x512) --> 512
with tf.name_scope('fc_1'):
pool_5_outz_flat = tf.layers.flatten(pool_5_out)
fc_1_out = tf.layers.dense(pool_5_outz_flat, 512, activation=tf.nn.relu)
fc_1_drop = tf.nn.dropout(fc_1_out, keep_prob=config.keep_prob)
print(fc_1_drop.shape)
# 512 --> 512
with tf.name_scope('fc_2'):
fc_2_out = tf.layers.dense(fc_1_drop, 512, activation=tf.nn.relu)
fc_2_drop = tf.nn.dropout(fc_2_out, keep_prob=config.keep_prob)
print(fc_2_drop.shape)
# 512 --> 10
with tf.name_scope('fc_3'):
fc_3_out = tf.layers.dense(fc_2_drop, 10, activation=None)
print(fc_3_out.shape)
return fc_3_out本博客所有文章除特别声明外，均采用 CC BY-SA 3.0协议 。转载请注明出处！