def convolutional(input_data, filters_shape, trainable, name, downsample=False, activate=True, bn=True):
#卷积层名称
with tf.variable_scope(name):
#如果需要下采样
if downsample:
pad_h, pad_w = (filters_shape[0] - 2) // 2 + 1, (filters_shape[1] - 2) // 2 + 1
paddings = tf.constant([[0, 0], [pad_h, pad_h], [pad_w, pad_w], [0, 0]])
input_data = tf.pad(input_data, paddings, 'CONSTANT')
strides = (1, 2, 2, 1)
padding = 'VALID'
else:
strides = (1, 1, 1, 1)
padding = "SAME"
#定义一个变量
weight = tf.get_variable(name='weight', dtype=tf.float32, trainable=True,
shape=filters_shape, initializer=tf.random_normal_initializer(stddev=0.01))
conv = tf.nn.Conv2d(input=input_data, filter=weight, strides=strides, padding=padding)
#如果归一化
if bn:
conv = tf.layers.batch_normalization(conv, beta_initializer=tf.zeros_initializer(),
gamma_initializer=tf.ones_initializer(),
moving_mean_initializer=tf.zeros_initializer(),
moving_variance_initializer=tf.ones_initializer(), training=trainable)
#如果不归一化
else:
bias = tf.get_variable(name='bias', shape=filters_shape[-1], trainable=True,
dtype=tf.float32, initializer=tf.constant_initializer(0.0))
conv = tf.nn.bias_add(conv, bias)
if activate == True: conv = tf.nn.leaky_relu(conv, alpha=0.1)
return conv
input_data = common.convolutional(input_data, filters_shape=(3, 3, 3, 32), trainable=trainable, name='conv0')
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 [email protected] 举报，一经查实，本站将立刻删除。
