tensorflow 分类

tensorflow 分类

from __future__ import print_functionimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataminist = input_data.read_data_sets('MNIST_data', one_hot=True)def add_layer(inputs, in_size, out_size, activation_function=None,): # add one more layer and return the output of this layer Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,) Wx_plus_b = tf.matmul(inputs, Weights) + biases if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b,) return outputsdef compute_accuracy(v_xs, v_ys): global prediction y_pre = sess.run(prediction, feed_dict={xs: v_xs}) correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) result = sess.run(accuracy, feed_dict={xs: v_xs, ys:v_ys}) return resultxs = tf.placeholder(tf.float32, [None, 784])ys = tf.placeholder(tf.float32, [None, 10])prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))train_step = tf.train.ProximalGradientDescentOptimizer(0.5).minimize(cross_entropy)sess = tf.Session()init = tf.global_variables_initializer()sess.run(init)for i in range(1000): batch_xs, batch_ys = minist.train.next_batch(100) sess.run(train_step, feed_dict={xs: batch_xs, ys:batch_ys}) if i % 50 == 0: print(compute_accuracy(minist.test.images, minist.test.labels))

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