Tensorflow练习

# coding: utf-8

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#print("hello")

#载入数据集
mnist = input_data.read_data_sets("F:\\TensorflowProject\\MNIST_data",one_hot=True)

#每个批次的大小，训练时一次100张放入神经网络中训练
batch_size = 100

#计算一共有多少个批次
n_batch = mnist.train.num_examples//batch_size

#定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])
#0-9十个数字
y = tf.placeholder(tf.float32,[None,10])
keep_prob = tf.placeholder(tf.float32)

#创建一个神经网络
# W = tf.Variable(tf.zeros([784,10]))
# b = tf.Variable(tf.zeros([10]))
W1 = tf.Variable(tf.truncated_normal([784,2000],stddev=0.1))
b1 = tf.Variable(tf.zeros([2000])+0.1)
L1 = tf.nn.tanh(tf.matmul(x,W1)+b1)
L1_drop = tf.nn.dropout(L1,keep_prob)

#隐藏层1
W2 = tf.Variable(tf.truncated_normal([2000,2000],stddev=0.1))
b2 = tf.Variable(tf.zeros([2000])+0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)
L2_drop = tf.nn.dropout(L2,keep_prob)

#隐藏层2
W3 = tf.Variable(tf.truncated_normal([2000,1000],stddev=0.1))
b3 = tf.Variable(tf.zeros([1000])+0.1)
L3 = tf.nn.tanh(tf.matmul(L2_drop,W3)+b3)
L3_drop = tf.nn.dropout(L3,keep_prob)

W4 = tf.Variable(tf.truncated_normal([1000,10],stddev=0.1))
b4 = tf.Variable(tf.zeros([10])+0.1)
prediction = tf.nn.softmax(tf.matmul(L3_drop,W4)+b4)

#二次代价函数
#loss = tf.reduce_mean(tf.square(y-prediction))
#交叉熵
#loss = tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()

#结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

#
with tf.Session() as sess:
　　sess.run(init)
　　for epoch in range(30):
　　　　for batch in range(n_batch):
　　　　　　batch_xs,batch_ys = mnist.train.next_batch(batch_size)
　　　　　　sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0})

　　　　#测试准确率
　　　　test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
　　　　train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1.0})
　　　　print("Iter: "+str(epoch)+" ,Testing Accuracy "+str(test_acc)+" Train : "+str(train_acc))

###########################运行效果

Extracting F:\TensorflowProject\MNIST_data\train-images-idx3-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\train-labels-idx1-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\t10k-images-idx3-ubyte.gz
Extracting F:\TensorflowProject\MNIST_data\t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From <ipython-input-6-c16fee9228bc>:44: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

Iter: 0  ,Testing Accuracy  0.9394    Train : 0.948436
Iter: 1  ,Testing Accuracy  0.9601    Train : 0.974145
Iter: 2  ,Testing Accuracy  0.9639    Train : 0.982691
Iter: 3  ,Testing Accuracy  0.965    Train : 0.9868
Iter: 4  ,Testing Accuracy  0.9691    Train : 0.988891
Iter: 5  ,Testing Accuracy  0.9698    Train : 0.9902
Iter: 6  ,Testing Accuracy  0.9692    Train : 0.9912
Iter: 7  ,Testing Accuracy  0.9706    Train : 0.991836
Iter: 8  ,Testing Accuracy  0.971    Train : 0.992291
Iter: 9  ,Testing Accuracy  0.9701    Train : 0.992818
Iter: 10  ,Testing Accuracy  0.9706    Train : 0.993073
Iter: 11  ,Testing Accuracy  0.9706    Train : 0.993236
Iter: 12  ,Testing Accuracy  0.9713    Train : 0.993491
Iter: 13  ,Testing Accuracy  0.9704    Train : 0.993782
Iter: 14  ,Testing Accuracy  0.9707    Train : 0.994036
Iter: 15  ,Testing Accuracy  0.9716    Train : 0.994236
Iter: 16  ,Testing Accuracy  0.9713    Train : 0.994509
Iter: 17  ,Testing Accuracy  0.9712    Train : 0.994691
Iter: 18  ,Testing Accuracy  0.9714    Train : 0.994891
Iter: 19  ,Testing Accuracy  0.9718    Train : 0.995055
Iter: 20  ,Testing Accuracy  0.9726    Train : 0.995236
Iter: 21  ,Testing Accuracy  0.972    Train : 0.995382
Iter: 22  ,Testing Accuracy  0.9725    Train : 0.995473
Iter: 23  ,Testing Accuracy  0.9728    Train : 0.995527
Iter: 24  ,Testing Accuracy  0.9725    Train : 0.995582
Iter: 25  ,Testing Accuracy  0.9725    Train : 0.995618
Iter: 26  ,Testing Accuracy  0.9723    Train : 0.995673
Iter: 27  ,Testing Accuracy  0.9726    Train : 0.9958
Iter: 28  ,Testing Accuracy  0.9721    Train : 0.995836
Iter: 29  ,Testing Accuracy  0.9721    Train : 0.995873