吴裕雄 python 神经网络——TensorFlow训练神经网络：卷积层、池化层样例 - 吴裕雄

import numpy as np
import tensorflow as tf

M = np.array([
        [[1],[-1],[0]],
        [[-1],[2],[1]],
        [[0],[2],[-2]]
    ])

print("Matrix shape is: ",M.shape)

filter_weight = tf.get_variable(\'weights\', [2, 2, 1, 1], initializer = tf.constant_initializer([[1, -1],[0, 2]]))
biases = tf.get_variable(\'biases\', [1], initializer = tf.constant_initializer(1))

M = np.asarray(M, dtype=\'float32\')
M = M.reshape(1, 3, 3, 1)

x = tf.placeholder(\'float32\', [1, None, None, 1])

conv = tf.nn.conv2d(x, filter_weight, strides = [1, 2, 2, 1], padding = \'SAME\')
bias = tf.nn.bias_add(conv, biases)
pool = tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\'SAME\')
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    convoluted_M = sess.run(bias,feed_dict={x:M})
    pooled_M = sess.run(pool,feed_dict={x:M})
    print("convoluted_M: \n", convoluted_M)
    print("pooled_M: \n", pooled_M)