tensorflow学习笔记-卷积,反卷积,空洞卷积

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卷积

卷积函数为:

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None,data_format=None, name=None)

input为一个4-D的输入,fileter为滤波器(卷积核),4-D,通常为[height, width, input_dim, output_dim],height, width分别表示卷积核的高,宽.input_dim, output_dim分别表式输入维度,输出维度.

import tensorflow as tfx1 = tf.constant(1.0, shape=[1, 5, 5, 3])
x2 = tf.constant(1.0, shape=[1, 6, 6, 3])
kernel = tf.constant(1.0, shape=[3, 3, 3, 1])
y1 = tf.nn.conv2d(x1, kernel, strides=[1, 2, 2, 1], padding="SAME")
y2 = tf.nn.conv2d(x2, kernel, strides=[1, 2, 2, 1], padding="SAME")sess = tf.Session()
tf.global_variables_initializer().run(session=sess)
x1_cov,  x2_cov = sess.run([y1, y2])print(x1_cov.shape)
print(x2_cov.shape)

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