tensorflow学习笔记（六）：cnn过程可视化

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport osimport matplotlib.pyplot as pltos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'mnist = input_data.read_data_sets('MNIST_data',one_hot=True)# print mnist.train.images.shape,mnist.train.labels.shape# (55000, 784) (55000, 10)# 784 = 28*28# print mnist.test.images.shape,mnist.test.labels.shape\# (10000, 784) (10000, 10)# print mnist.validation.images.shape,mnist.validation.labels.shape# (5000, 784) (5000, 10)def Weight_value(shape):    init = tf.random_normal(shape, stddev=0.1)    return tf.Variable(init, name="weight")def bias_value(shape):    init = tf.constant(0.1, shape=shape)    return tf.Variable(init)def conv2d(x, W):    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")def pool_2x2(x):    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")xs = tf.placeholder(tf.float32, [None, 784])ys = tf.placeholder(tf.float32, [None, 10])x_image = tf.reshape(xs, [-1, 28, 28, 1])# layer1 conv1  [-1, 28, 28, 32]W_conv1 = Weight_value([5, 5, 1, 32])b_conv1 = bias_value([32])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)+b_conv1)# layer2 pool1 [-1, 14, 14, 32]h_pool1 = pool_2x2(h_conv1)# layer3 conv2 [-1, 14, 14, 64]W_conv2 = Weight_value([5, 5, 32, 64])b_conv2 = bias_value([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)# layer4 pool2 [-1,7,7,64]h_pool2 = pool_2x2(h_conv2)# layer5 fc1 [-1,1024]h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])W_fc1 = Weight_value([7*7*64, 1024])b_fc1 = bias_value([1024])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1)#layer6 dropoutkeep_prob = tf.placeholder(tf.float32)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# layer7 fc2 [-1,10]W_fc2 = Weight_value([1024, 10])b_fc2 = bias_value([10])y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2)+b_fc2)# cross_entropycross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(y_conv), reduction_indices=[1]))# optimizertrain_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)# accuracycorrect_prediction = tf.equal(tf.argmax(ys, 1), tf.argmax(y_conv, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# initinit = tf.global_variables_initializer()# sesswith tf.Session() as sess:    sess.run(init)    img1 = mnist.train.images[1]    img1.shape = [1, 784]    result = sess.run(h_conv1, feed_dict={xs : img1})    for i in range(32):        show_img = result[:, :, :, i]        # print type(show_img)        show_img.shape = [28, 28]        plt.subplot(4, 8, i + 1)        plt.imshow(show_img, cmap='gray')        plt.axis('off')    plt.show()    # for i in range(1001):    #     x_batch, y_batch = mnist.train.next_batch(50)    #     sess.run(train_step, feed_dict={xs:x_batch, ys:y_batch, keep_prob:0.5})    #     if i%100 == 0:    #         x_test, y_test = mnist.test.next_batch(50)    #         print i, ' step train ', sess.run(accuracy, feed_dict={xs: x_batch, ys: y_batch, keep_prob: 1})    #         print i, ' step test', sess.run(accuracy, feed_dict={xs:x_test, ys:y_test, keep_prob: 1})

可以看出32个卷积核对图片特征的提取