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# coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)


    

    




Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting MNIST_data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

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def w(shape):
    inital = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(inital)


    

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def b(shape):
    inital = tf.constant(0.1, tf.float32, shape)
    return tf.Variable(inital)


    

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


    

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


    

In [7]:

    

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


    

In [8]:

    

# 第一个卷积层 strides=1 图片大小不变，但高度方向变为32
wc1 = w([5, 5, 1, 32])
bc1 = b([32])
conv1 = tf.nn.relu(conv2d(x_image, wc1) + bc1)


    

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# 第一个maxpooling层  tf.nn.conv2d和tf.nn.max_pool input和output都是[28,28,n]的数据
pool1 = max_pool(conv1)


    

In [10]:

    

# 第二个conv pooling
wc2 = w([5, 5, 32, 64])
bc2 = b([64])
conv2 = tf.nn.relu(conv2d(pool1, wc2) + bc2)
pool2 = max_pool(conv2)


    

In [11]:

    

wf1 = w([3136, 1024])
bf1 = b([1024])
indata = tf.reshape(pool2,[-1, 3136])
fc1 = tf.nn.relu(tf.matmul(indata, wf1) + bf1)


    

In [12]:

    

# drop层 降低过拟合 此处暂定为0.5 也可以采用placeholder占位
fc1d = tf.nn.dropout(fc1, 0.5)


    

In [13]:

    

wf2 = w([1024, 10])
bf2 = b([10])
pre = tf.nn.softmax(tf.matmul(fc1d, wf2) + bf2)


    

In [14]:

    

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pre), reduction_indices=[1]))  
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# train_step = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cross_entropy)


    

In [15]:

    

# 定义准确率
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(pre, 1))  
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


    

In [16]:

    

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for i in range(5000):
    batch_xs, batch_ys = mnist.train.next_batch(50)
    sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch_xs, y: batch_ys})
        print("step %d, training accuracy %g" % (i, train_accuracy))
# result
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))


    

    




step 0, training accuracy 0.1
step 100, training accuracy 0.58
step 200, training accuracy 0.86
step 300, training accuracy 0.8
step 400, training accuracy 0.92
step 500, training accuracy 0.9
step 600, training accuracy 0.96
step 700, training accuracy 0.92
step 800, training accuracy 0.84
step 900, training accuracy 0.94
step 1000, training accuracy 0.88
step 1100, training accuracy 0.88
step 1200, training accuracy 0.94
step 1300, training accuracy 0.9
step 1400, training accuracy 0.92
step 1500, training accuracy 0.96
step 1600, training accuracy 0.92
step 1700, training accuracy 0.96
step 1800, training accuracy 1
step 1900, training accuracy 0.98
step 2000, training accuracy 0.92
step 2100, training accuracy 0.96
step 2200, training accuracy 0.96
step 2300, training accuracy 1
step 2400, training accuracy 0.96
step 2500, training accuracy 0.98
step 2600, training accuracy 0.94
step 2700, training accuracy 0.98
step 2800, training accuracy 1
step 2900, training accuracy 0.96
step 3000, training accuracy 0.96
step 3100, training accuracy 1
step 3200, training accuracy 0.98
step 3300, training accuracy 0.96
step 3400, training accuracy 1
step 3500, training accuracy 1
step 3600, training accuracy 1
step 3700, training accuracy 0.98
step 3800, training accuracy 1
step 3900, training accuracy 0.98
step 4000, training accuracy 0.98
step 4100, training accuracy 0.96
step 4200, training accuracy 0.98
step 4300, training accuracy 0.94
step 4400, training accuracy 0.98
step 4500, training accuracy 0.98
step 4600, training accuracy 1
step 4700, training accuracy 1
step 4800, training accuracy 0.96
step 4900, training accuracy 0.98
0.9799

In [20]:

    

saver = tf.train.Saver()
save_path = saver.save(sess, "C:/Users\hasee\model.ckpt")
print(save_path)


    

    




C:/Users\hasee\model.ckpt

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