Tensorflow实现全链接神经网络

import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data ##神经网络参数初始化 def init_NN(): mnist = input_data.read_data_sets('data/', one_hot=True) ##定义神经网络参数 输入->输出 n_input = 784 ##输入层 n_hidden_1 = 256 ##隐藏层1 n_hidden_2 = 128 ##隐藏层2 n_classes = 10 ##输出层 x = tf.placeholder('float', [None, n_input]) y = tf.placeholder('float', [None, n_classes]) stddev = 0.1 ##标准差 ##初始化每层神经网络的权重 weigths = { 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)), 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)), 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev)), } ##初始化每层神经网络的偏移量 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])) } return x, y, weigths, biases, mnist ##前向传播 def multilayer_perceptron(_x, _weights, _biase): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_x, _weights['w1']), _biase['b1'])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['w2']), _biase['b2'])) return tf.add(tf.matmul(layer_2, _weights['out']), _biase['out']) ##反向传播 def back_propagation(_pred, _y): ##学习率 learnint_rate = 0.01 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, _y)) optm = tf.train.GradientDescentOptimizer(learning_rate=learnint_rate).minimize(cost) corr = tf.equal(tf.arg_max(_pred, 1), tf.arg_max(_y, 1)) accu = tf.reduce_mean(tf.cast(corr, 'float')) return cost, optm, accu ##开始训练 def start_train(_cost, _optm, _accu, _mnist): training_epochs = 20 batch_size = 100 display_step = 4 init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for epoch in range(1, training_epochs + 1): avg_cost = 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) feeds = {x: batch_xs, y: batch_ys} sess.run(_optm, feed_dict=feeds) avg_cost += sess.run(_cost, feeds) avg_cost = avg_cost / total_batch if epoch % display_step == 0: print("Epoch:d/d cos:%.9f" % (epoch, training_epochs, avg_cost)) train_acc = sess.run(_accu, feeds) print("train_acc:%.3f" % (train_acc)) test_feeds = {x: mnist.test.images, y: mnist.test.labels} test_acc = sess.run(_accu, test_feeds) print("test_acc:%.3f" % (test_acc)) print('FINISH') if __name__ == '__main__': x, y, weigths, biases, mnist = init_NN() ##前向传播公式 pred = multilayer_perceptron(x, weigths, biases) ##反向传播公式 cost, optm, accu = back_propagation(pred, y) ##训练数据 start_train(cost, optm, accu, mnist) ##输出结果 Epoch:004/020 cos:1.799211222 train_acc:0.570 test_acc:0.625 Epoch:008/020 cos:0.995448108 train_acc:0.830 test_acc:0.808 Epoch:012/020 cos:0.662830535 train_acc:0.790 test_acc:0.854 Epoch:016/020 cos:0.525997586 train_acc:0.870 test_acc:0.873 Epoch:020/020 cos:0.454229037 train_acc:0.890 test_acc:0.887 FINISH Process finished with exit code 0