莫烦CNN教程

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CNN mnist

"""
import tensorflow as tf 
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)     #下载并加载mnist数据
x = tf.placeholder(tf.float32, [None, 784])                        #输入的数据占位符
y_actual = tf.placeholder(tf.float32, shape=[None, 10])            #输入的标签占位符

#定义一个函数，用于初始化所有的权值 W
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

#定义一个函数，用于初始化所有的偏置项 b
def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

#定义一个函数，用于构建卷积层
def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#定义一个函数，用于构建池化层
def max_pool(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

#构建网络
x_image = tf.reshape(x, [-1,28,28,1])         #转换输入数据shape,以便于用于网络中
W_conv1 = weight_variable([5, 5, 1, 32])      
b_conv1 = bias_variable([32])       
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)     #第一个卷积层
h_pool1 = max_pool(h_conv1)                                  #第一个池化层

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)      #第二个卷积层
h_pool2 = max_pool(h_conv2)                                   #第二个池化层

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])              #reshape成向量
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)    #第一个全连接层

keep_prob = tf.placeholder("float") 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)                  #dropout层

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_predict=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)   #softmax层

cross_entropy = -tf.reduce_sum(y_actual*tf.log(y_predict))     #交叉熵
train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)    #梯度下降法
correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1))    
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))                 #精确度计算
sess=tf.InteractiveSession()                          
sess.run(tf.initialize_all_variables())
for i in range(20000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:                  #训练100次，验证一次
    train_acc = accuracy.eval(feed_dict={x:batch[0], y_actual: batch[1], keep_prob: 1.0})
    print('step',i,'training accuracy',train_acc)
    train_step.run(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 0.5})

test_acc=accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0})
print("test accuracy",test_acc)

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莫烦cnn mnist

from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
    return result

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 conv2d(x, W):
    # stride [1, x_movement, y_movement, 1]
    # Must have strides[0] = strides[3] = 1
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    # stride [1, x_movement, y_movement, 1]
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])
# print(x_image.shape)  # [n_samples, 28,28,1]

## conv1 layer ##
W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)                                         # output size 14x14x32

## conv2 layer ##
W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)                                         # output size 7x7x64

## fc1 layer ##
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]
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)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

## fc2 layer ##
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                              reduction_indices=[1]))       # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()
# important step
sess.run(tf.initialize_all_variables())

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
    if i % 50 == 0:
        print(compute_accuracy(
            mnist.test.images, mnist.test.labels))

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保存文件

import tensorflow as tf

# save to file

W = tf.Variable([[1,2,3],[4,5,6]],dtype=tf.float32,name="weight")
b = tf.Variable([[1,2,3]],dtype=tf.float32,name="biases")

init = tf.initialize_all_variables()
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init)
    save_path = saver.save(sess,"savefile/save_net.ckpt")
    print("save to path: ", save_path)

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提取数据

import tensorflow as tf
import numpy as np

# restore  提取数据[1,2,3],[4,5,6]  

W = tf.Variable(np.arange(6).reshape(2,3),dtype=tf.float32,name="weights")
b = tf.Variable(np.arange(3).reshape(1,3),dtype=tf.float32,name="biases")

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess,"savefile/save_net.ckpt")
    print("weights: ", sess.run(W))
    print("biases: ", sess.run(b))

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