TensorFlow官方教程代码(1)

MNIST手写数字识别

# TensorFlow官方教程代码之MNIST手写体数字识别

# 读入数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print(mnist.train.images.shape, mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.test.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)

# 查看数据
import matplotlib.pyplot as plt
num = mnist.train.images[0]
num.shape = [28, 28]
fg = plt.figure()
plt.imshow(num, cmap=plt.cm.jet)
plt.show()

# TensorFlow网络结构
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [None, 784])  # 输入
W = tf.Variable(tf.zeros([784, 10]))  # 权重
b = tf.Variable(tf.zeros([10]))  # 偏置
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_mean(tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
tf.global_variables_initializer().run()
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    train_step.run({x: batch_xs, y_: batch_ys})

# 模型评价
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))

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