tensorflow CNN 卷积神经网络中的卷积层和池化层的代码和效果图

tensorflow CNN 卷积神经网络中的卷积层和池化层的代码和效果图

因为很多 demo 都比较复杂，专门抽出这两个函数，写的 demo。

#!/usr/bin/python# -*- coding: UTF-8 -*-import matplotlib.pyplot as pltimport tensorflow as tffrom PIL import Imageimport numpyimg = Image.open('szu.jpg')img_ndarray = numpy.asarray(img, dtype='float32')print(img_ndarray.shape)img_ndarray=img_ndarray[:,:,0]plt.figure()plt.subplot(221)plt.imshow(img_ndarray)w=[[-1.0,-1.0,-1.0],   [-1.0,9.0,-1.0],   [-1.0,-1.0,-1.0]]with tf.Session() as sess:    img_ndarray=tf.reshape(img_ndarray,[1,183,276,1])    w=tf.reshape(w,[3,3,1,1])    img_cov=tf.nn.conv2d(img_ndarray, w, strides=[1, 1, 1, 1], padding='SAME')    image_data=sess.run(img_cov)    print(image_data.shape)    plt.subplot(222)    plt.imshow(image_data[0,:,:,0])    img_pool=tf.nn.max_pool(img_ndarray, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],                   padding='SAME')    image_data = sess.run(img_pool)    plt.subplot(223)    plt.imshow(image_data[0, :, :, 0])    plt.subplot(224)    img_pool = tf.nn.max_pool(img_ndarray, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1],                              padding='SAME')    image_data = sess.run(img_pool)    plt.imshow(image_data[0, :, :, 0])    plt.show()

效果图片