Tensorflow項目實戰一：MNIST手寫數字識別

　　此模型中，輸入是28*28*1的圖片，經過兩個卷積層（卷積+池化）層之后，尺寸變為7*7*64，將最后一個卷積層展成一個以為向量，然后接兩個全連接層，第一個全連接層加一個dropout，最后一個全連接層輸出10個分類的預測結果，然后計算損失，進行訓練。

　　代碼如下：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#定義一個獲取卷積核的函數
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

#定義一個獲取偏置值的函數
def bias_variable(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

#定義一個卷積函數
def conv2d(x,W):
    return tf.nn.conv2d(x,W,[1,1,1,1],padding="SAME")

#定義一個池化函數
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1], strides=[1,2,2,1],padding="VALID")


if __name__ == "__main__":
    mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
    x = tf.placeholder(shape=[None,28*28],dtype=tf.float32)
    lable = tf.placeholder(shape=[None,10],dtype=tf.float32)

    x_image = tf.reshape(x,[-1,28,28,1])

    #第一個卷積層
    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)
    h_pool1 = max_pool_2x2(h_conv1)
    #14*14*32

    #第二個卷積層
    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)
    h_pool2 = max_pool_2x2(h_conv2)
    #7*7*64

    #全連接層，輸出為1024維向量
    W_fc1 = weight_variable([7*7*64,1024])
    b_fc1 = weight_variable([1024])
    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)
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_dropout = tf.nn.dropout(h_fc1,keep_prob=keep_prob)

    #把1024維向量轉換成10維，對應10個類別
    W_fc2 = weight_variable([1024,10])
    b_fc2 = weight_variable([10])
    y_conv = tf.matmul(h_fc1,W_fc2)+b_fc2

    #直接使用tf.nn.softmax_cross_entropy_with_logits直接計算交叉熵
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=lable,logits=y_conv))
    #定義train_step
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    #定義測試的准確率
    correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(lable,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

    # 創建Session和變量初始化
    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())

    #訓練20000步
    for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i % 100==0:
            train_accuracy = sess.run(accuracy,feed_dict={
                x:batch[0],lable:batch[1],keep_prob: 1.0})
            print("step %d, training accuracy %g" % (i, train_accuracy))
        _ = sess.run(train_step, feed_dict={x: batch[0], lable: batch[1], keep_prob: 0.5})
    print("test accuracy %g" % sess.run(accuracy, feed_dict={
        x: mnist.test.images, lable: mnist.test.labels, keep_prob: 1.0}))