8_最简单的神经网络_mnist 模糊数字图片识别 0-9

import tensorflow as tf #导入tensorflow库
from tensorflow.examples.tutorials.mnist import input_data
import pylab 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  # 下载并加载数据
 
# print('训练集:', mnist.train.images) # 训练集
print('训练集shape:', mnist.train.images.shape)  # 输入数据打印shape: (55000, 784)

im = mnist.train.images[1]
im = im.reshape(-1, 28)
pylab.imshow(im)
pylab.show()

print('测试集shape:', mnist.test.images.shape) # 输入数据打印shape: (10000, 784) 测试集
print('输入数据打印shape:', mnist.validation.images.shape)
print("训练数据总数:" , mnist.train.num_examples) # 550 00
print("测试数据总数:" , mnist.test.num_examples)   # 1w

Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
训练集shape: (55000, 784)
测试集shape: (10000, 784)
输入数据打印shape: (5000, 784)
训练数据总数: 55000
测试数据总数: 10000

import tensorflow as tf #导入tensorflow库
from tensorflow.examples.tutorials.mnist import input_data
import pylab 

# 1 加载数据
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print("训练数据总数:" , mnist.train.num_examples)
print(mnist.test.nu)

# 2 函数用于清除默认图形堆栈并重置全局默认图形
tf.reset_default_graph() 

# 3 tf Graph Input 输入输出占位
x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 数字=> 10 classes

# 4 Set model weights 权重占位,
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.zeros([10]))

# 5 前向输出
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax分类 # 激活函数,输出为总输出的概率, 总和为1,

# 6 生成的pred与样本标签y进行一次交叉熵运算, 然后在取平均值, 注意这里的交叉熵运行在上一节已经提到过
# 将这个结果作为一次正向传播的误差, 通过梯度下降的优化方法找到能够使这个误差最小化的b,w偏移量
# 更细b,w参数, 使其调整为合适的参数
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

# 7 使用梯度下降优化器
learning_rate = 0.01 # 学习率, 对于梯度下降法的输入参数
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# 8 训练参数设置
training_epochs = 25
batch_size      = 100
display_step    = 1
saver           = tf.train.Saver()
model_path      = "log/521model.ckpt"

# 9 启动session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())# Initializing OP

    # 启动循环开始训练
    for epoch in range(training_epochs):  # 循环25次 25*550次循环,每次循环喂100 
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size) # total_batch=55000/100=550 一次训练

        # 注意下面的for是训练一遍数据的
        # 遍历全部数据集
        for _1 in range(total_batch):  #  1 - 550
            batch_xs, batch_ys = mnist.train.next_batch(batch_size) # 拿出来100个喂给训练兽
            # Run optimization op (backprop) and cost op (to get loss value)
            _2, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
                                                          y: batch_ys})
            # Compute average loss 计算平均loss值,
            avg_cost += (c / total_batch)
        # 显示训练中的详细信息
        if (epoch+1) % display_step == 0:
            print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

    print( " Finished!")

    # 测试 model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print ("Accuracy:", accuracy.eval（{x: mnist.test.images, y: mnist.test.labels}))

    # Save model weights to disk
    save_path = saver.save(sess, model_path)
    print("Model saved in file: %s" % save_path)

Epoch: 0001 cost= 8.740313732
...
Epoch: 0025 cost= 0.849729373
 Finished!
Accuracy: 0.8294
Model saved in file: log/521model.ckpt

'''
#读取模型
print("Starting 2nd session...")
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver.restore(sess, model_path)
    
     # 测试 model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print ("Accuracy:", accuracy.eval（{x: mnist.test.images, y: mnist.test.labels}))
    
    output = tf.argmax(pred, 1)
    batch_xs, batch_ys = mnist.train.next_batch(2)
    outputval,predv = sess.run([output,pred], feed_dict={x: batch_xs})
    print(outputval,predv,batch_ys)

    im = batch_xs[0]
    im = im.reshape(-1,28)
    pylab.imshow(im)
    pylab.show()
    
    im = batch_xs[1]
    im = im.reshape(-1,28)
    pylab.imshow(im)
    pylab.show()
    
'''