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

转载于 2019-05-16 16:39:00 发布 · 131 阅读

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原文链接：http://www.cnblogs.com/tszr/p/10876480.html

文章标签：

#人工智能 #python

本文介绍如何使用TensorFlow库进行卷积神经网络的基本操作，包括定义权重和偏置变量、实现卷积层和平均池化层，并通过具体代码示例展示其在矩阵上的应用。

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)