tensorflow实现简单的卷积神经网络

# MNIST训练

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

mnist = input_data.read_data_sets('MNIST_data/',one_hot=True)

def weight_variable(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 conv(x,w):
    return tf.nn.conv2d(x,w,strides=[1,1,1,1], padding='SAME')

def max_pool(x):
    return tf.nn.max_pool(x,ksize = [1, 2, 2, 1],strides=[1,2,2,1],padding='SAME')

x = tf.placeholder(tf.float32,shape=[None,784])
y_ = tf.placeholder(tf.float32,shape=[None,10])
x_image = tf.reshape(x,[-1,28,28,1])

#卷积层1-池化层1
w_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv(x_image,w_conv1)+b_conv1)
h_pool1 = max_pool(h_conv1)

#卷积层2-池化层2
w_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv(h_pool1,w_conv2)+b_conv2)
h_pool2 = max_pool(h_conv2)

#全连接层
w_fc1 = weight_variable([7 * 7 *64,1024])
b_fc1 = bias_variable([1024])
h_pool_flat = tf.reshape(h_pool2,[-1,7 * 7 *64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool_flat,w_fc1)+b_fc1)

#dropout层
keep_drop = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_drop)

#softmax层
w_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
y = tf.matmul(h_fc1_drop,w_fc2)+b_fc2

#loss
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y,labels=y_))
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)
#计算模型预测的准确率
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
sess.run(init)
losses = []
acc = []
for i in range(2000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_drop:1.0})
        print('step %d,training accuracy %g' %(i,train_accuracy))
        acc.append(train_accuracy)
        loss_tmp = sess.run(loss,feed_dict={x:batch[0],y_:batch[1],keep_drop:1.0})
        losses.append(loss_tmp)
    sess.run(train_step,feed_dict={x: batch[0], y_: batch[1], keep_drop: 0.5})
print("test accuracy",accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_drop:1.0}))

参考文章：

1.https://www.cnblogs.com/willnote/p/6874699.html

作者：舟华520

出处：https://www.cnblogs.com/xfzh193/

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