使用CNN实现手写体识别（minist库）

入门级CNN代码

有空在下一篇写写CNN原理

# -*- coding: utf-8 -*-"""Created on Wed Oct 11 16:51:27 2017@author: tina"""import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datadef weight_varible(shape):    initial = tf.truncated_normal(shape, stddev=0.1)    return tf.Variable(initial)def bias_variable(shape):    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)def conv2d(x, W):    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')def max_pool_2x2(x):    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)print("Download Done!")sess = tf.InteractiveSession()# parasW_conv1 = weight_varible([5, 5, 1, 32])b_conv1 = bias_variable([32])# conv layer-1x = tf.placeholder(tf.float32, [None, 784])x_image = tf.reshape(x, [-1, 28, 28, 1])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)# conv layer-2W_conv2 = weight_varible([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)# full connectionW_fc1 = weight_varible([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)# dropoutkeep_prob = tf.placeholder(tf.float32)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# output layer: softmaxW_fc2 = weight_varible([1024, 10])b_fc2 = bias_variable([10])y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)y_ = tf.placeholder(tf.float32, [None, 10])# model trainingcross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))sess.run(tf.initialize_all_variables())for i in range(20000):    batch = mnist.train.next_batch(50)    if i % 100 == 0:        train_accuacy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})        print("step %d, training accuracy %g"%(i, train_accuacy))    train_step.run(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})# accuacy on testprint("test accuracy %g"%(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})))

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