TensorFlow中nn.conv2d与nn.avg_pool的理解

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使用TensorFlow求卷积和池化：

import tensorflow as tfimport numpy as npM = 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)print("Reshaped M: \n", M)x = tf.placeholder('float32', [1, None, None, 1])# 在3x3的M的右边和下面补零，卷积窗口每次移动两个单位。得到的conv为2x2的矩阵，再转成高维矩阵--[1, 2, 2, 1]conv = tf.nn.conv2d(x, filter_weight, strides = [1, 2, 2, 1], padding = 'SAME') # 将卷积求得的值加上偏置就为所求值bias = tf.nn.bias_add(conv, biases)# 在3x3的M的右边和下面不补零，卷积窗口从左上角每次移动两个单位，遇到数据不足时，只取当前数据。# 得到的pool为2x2的矩阵，再转成高维矩阵--[1, 2, 2, 1]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.reshape(2, 2))    print ("pooled_M: \n", pooled_M.reshape(2, 2))

输出结果如下：

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