说明
本例子利用TensorFlow搭建一个全连接神经网络,实现对MNIST手写数字的识别。
先上代码
from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf # prepare data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) xs = tf.placeholder(tf.float32, [None, 784]) ys = tf.placeholder(tf.float32, [None, 10]) # the model of the fully-connected network weights = tf.Variable(tf.random_normal([784, 10])) biases = tf.Variable(tf.zeros([1, 10]) + 0.1) outputs = tf.matmul(xs, weights) + biases predictions = tf.nn.softmax(outputs) cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predictions), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) # compute the accuracy correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32)) with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={ xs: batch_xs, ys: batch_ys }) if i % 50 == 0: print(sess.run(accuracy, feed_dict={ xs: mnist.test.images, ys: mnist.test.labels }))
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