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

mnist = input_data.read_data_sets('data/', one_hot = True)

learning_rate = 0.1
epochs = 5
batch_size = 100
display_step = 1

# 构建TF的输入
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 设置模型参数
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# 构建模型
pred = tf.nn.softmax(tf.matmul(x, W) + b)

# 损失函数
loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)

    for epoch in range(epochs):
        avg_loss = 0.
        total_batch = int(mnist.train.num_examples / batch_size)

        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, loss], feed_dict={x:batch_xs, y:batch_ys})

            avg_loss += c / total_batch
        if (epoch + 1) % display_step == 0:
            print("Epoch:",'%04d' % (epoch+1))
            print("cost=", "{:.9f}".format(avg_cost))

    print("Optimization Finished!")

    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]}))
dcd7607c75848e4a275acfece8877ed74a76fd34
<mask token>

<mask token>
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(epochs):
        avg_loss = 0.0
        total_batch = int(mnist.train.num_examples / batch_size)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, loss], feed_dict={x: batch_xs, y:
                batch_ys})
            avg_loss += c / total_batch
        if (epoch + 1) % display_step == 0:
            print('Epoch:', '%04d' % (epoch + 1))
            print('cost=', '{:.9f}'.format(avg_cost))
    print('Optimization Finished!')
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print('Accuracy:', accuracy.eval({x: mnist.test.images[:3000], y: mnist
        .test.labels[:3000]}))

<mask token>
mnist = input_data.read_data_sets('data/', one_hot=True)
learning_rate = 0.1
epochs = 5
batch_size = 100
display_step = 1
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
pred = tf.nn.softmax(tf.matmul(x, W) + b)
loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(epochs):
        avg_loss = 0.0
        total_batch = int(mnist.train.num_examples / batch_size)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, loss], feed_dict={x: batch_xs, y:
                batch_ys})
            avg_loss += c / total_batch
        if (epoch + 1) % display_step == 0:
            print('Epoch:', '%04d' % (epoch + 1))
            print('cost=', '{:.9f}'.format(avg_cost))
    print('Optimization Finished!')
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print('Accuracy:', accuracy.eval({x: mnist.test.images[:3000], y: mnist
        .test.labels[:3000]}))

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('data/', one_hot=True)
learning_rate = 0.1
epochs = 5
batch_size = 100
display_step = 1
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
pred = tf.nn.softmax(tf.matmul(x, W) + b)
loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(epochs):
        avg_loss = 0.0
        total_batch = int(mnist.train.num_examples / batch_size)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, loss], feed_dict={x: batch_xs, y:
                batch_ys})
            avg_loss += c / total_batch
        if (epoch + 1) % display_step == 0:
            print('Epoch:', '%04d' % (epoch + 1))
            print('cost=', '{:.9f}'.format(avg_cost))
    print('Optimization Finished!')
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print('Accuracy:', accuracy.eval({x: mnist.test.images[:3000], y: mnist
        .test.labels[:3000]}))


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

mnist = input_data.read_data_sets('data/', one_hot = True)

learning_rate = 0.1
epochs = 5
batch_size = 100
display_step = 1

# 构建TF的输入
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 设置模型参数
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# 构建模型
pred = tf.nn.softmax(tf.matmul(x, W) + b)

# 损失函数
loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)

    for epoch in range(epochs):
        avg_loss = 0.
        total_batch = int(mnist.train.num_examples / batch_size)

        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, loss], feed_dict={x:batch_xs, y:batch_ys})

            avg_loss += c / total_batch
        if (epoch + 1) % display_step == 0:
            print("Epoch:",'%04d' % (epoch+1))
            print("cost=", "{:.9f}".format(avg_cost))

    print("Optimization Finished!")

    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]}))