mnist简单的神经网络

#载入数据集

mnist = input_data.read_data_sets(r"E:\anaconda\tensorflow\tensor_mnist-master\MNIST_data",one_hot=True)

#每个批次的大小

batch_size = 100

#计算一共有多少个批次

n_batch=mnist.train.num_examples//batch_size

x = tf.placeholder(tf.float32,[None,784])

y = tf.placeholder(tf.float32,[None,10])

#创建一个神经网络

W = tf.Variable(tf.zeros([784,10]))

b = tf.Variable(tf.zeros([10]))

prediction = tf.nn.softmax(tf.matmul(x,W)+b)

loss = tf.reduce_mean(tf.square(y-prediction))

train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#一个bool类型的列表

correct_rediction = tf.equal(tf.arg_max(y,1),tf.arg_max(prediction,1))#arg_max会返回一个张量中最大值所在位置

#cast将bool转化为float型，true为1，false为0

accuracy = tf.reduce_mean(tf.cast(correct_rediction,tf.float32))

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

for epoch in range(21):

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})

acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})

print ('Iter'+str(epoch)+'.Testing Accuracy'+str(acc))

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稍微复杂一点的代码：

#载入数据集

mnist = input_data.read_data_sets(r"E:\anaconda\tensorflow\tensor_mnist-master\MNIST_data",one_hot=True)

#每个批次的大小

batch_size = 100

#计算一共有多少个批次

n_batch=mnist.train.num_examples//batch_size

x = tf.placeholder(tf.float32,[None,784])

y = tf.placeholder(tf.float32,[None,10])

keep_prob = tf.placeholder(tf.float32)

Ir = tf.Variable(0.001,dtype=tf.float32) #学习率

#创建一个神经网络

W1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1))

b1 = tf.Variable(tf.zeros([500])+0.1)

L1 = tf.nn.tanh(tf.matmul(x,W1)+b1)

L1_drop = tf.nn.dropout(L1,keep_prob)

W2 = tf.Variable(tf.truncated_normal([500,300],stddev=0.1))

b2 = tf.Variable(tf.zeros([300])+0.1)

L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)

L2_drop = tf.nn.dropout(L2,keep_prob)

W3 = tf.Variable(tf.truncated_normal([300,10],stddev=0.1))

#tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。

b3 = tf.Variable(tf.zeros([10])+0.1)

prediction = tf.nn.softmax(tf.matmul(L2_drop,W3)+b3)

#交叉熵代价函数

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

train_step = tf.train.AdamOptimizer(Ir).minimize(loss)

#一个bool类型的列表

correct_rediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#arg_max会返回一个张量中最大值所在位置

#cast将bool转化为float型，true为1，false为0

accuracy = tf.reduce_mean(tf.cast(correct_rediction,tf.float32))

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

for epoch in range(51):

sess.run(tf.assign(Ir,0.001*(0.95**epoch)))

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})

learning_rate = sess.run(Ir)

acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})

print ('Iter'+str(epoch)+'.Testing Accuracy'+str(acc)+'.learning rate'+str(learning_rate))