tensorflow 多層感知機 分類mnist
阿新 • • 發佈:2019-01-04
from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/root/data/", one_hot=True) import tensorflow as tf learning_rate = 0.001 training_epochs = 25 batch_size = 100 display_step = 1 n_hidden_1 = 256 n_hidden_2 = 256 n_input = 784 n_classes = 10 x = tf.placeholder("float",[None, n_input]) y = tf.placeholder("float",[None, n_classes]) def multilayer_perception(x, weights, biases): layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) layer_1 = tf.nn.relu(layer_1) layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) layer_2 = tf.nn.relu(layer_2) out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer weights = { 'h1':tf.Variable(tf.random_normal([n_input, n_hidden_1])), 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1])), 'b2': tf.Variable(tf.random_normal([n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_classes])) } pred = multilayer_perception(x, weights, biases) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) avg_cost += c / total_batch if epoch % display_step == 0: print "Epoch:", '%04d' % (epoch+1), "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, "float")) print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})