本文主要介绍使用TensorFlow对多层感知器（MLP）进行编程，其他内容参考：TensorFlow 学习目录。
目录
一、多层感知器MLP
多层感知器不同于CNN，CNN提取的是局部特征，而且CNN训练的并不是feature，而是feature的特征过滤器，也就是Filter（Kernel），多层感知器隐藏层中的每一个神经元是上一层的全部神经元的权重配比，并不具有局部性，而是更加注重整体，所以相比于CNN，MLP的参数量是非常庞大的。在多层感知器这种结构下，H-ELM、ML-ELM对其进行训练的效果是比Back Propagation算法进行不断地迭代训练好很多的，而且更快，只要调一次参数，而BP算法要调到收敛，并需要注意过拟合。
import tensorflow as tf import numpy as np import get_Dataset x_train, y_train, x_test, y_test = get_Dataset.get_Dataset(name='mnist') inputs = tf.placeholder(dtype=tf.float32, shape=[None, 784], name='inputs') labels = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='labels') pre_labels = tf.placeholder(dtype=tf.int64, shape=[None], name='pre_labels') def fully_connected(x, size): len_x = int(x.get_shape()[1]) w = tf.Variable(tf.random_normal(shape=[len_x, size], stddev=0.1), dtype=tf.float32) b = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[size])) z = tf.nn.relu(tf.nn.xw_plus_b(x, w, b)) return z f1 = fully_connected(inputs, 520) f2 = fully_connected(f1, 200) logits = fully_connected(f2, 10) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)) optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss) correct_pred = tf.equal(tf.argmax(logits, 1), pre_labels) acc = tf.reduce_mean(tf.cast(correct_pred, dtype=tf.float32)) epochs = 50 batch_size = 40 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for epoch in range(epochs): for batch in range(int(x_train.shape[0]/batch_size)): batchx = x_train[batch*batch_size: (batch+1)*batch_size] batchy = y_train[batch*batch_size: (batch+1)*batch_size] feed_dict={ inputs: batchx, labels: batchy } _ = sess.run(optimizer, feed_dict=feed_dict) Acc = sess.run(acc, feed_dict={inputs: x_test, pre_labels: y_test}) print ("Epoch ", epoch+1, "ACC = ", Acc) 输出：
Epoch 1 ACC = 0.9402 Epoch 2 ACC = 0.9577 Epoch 3 ACC = 0.9649 Epoch 4 ACC = 0.9682 Epoch 5 ACC = 0.9709 Epoch 6 ACC = 0.972 Epoch 7 ACC = 0.9738 Epoch 8 ACC = 0.9756 Epoch 9 ACC = 0.9757 Epoch 10 ACC = 0.9762 Epoch 11 ACC = 0.9766 Epoch 12 ACC = 0.9759 Epoch 13 ACC = 0.9768 Epoch 14 ACC = 0.9765 Epoch 15 ACC = 0.9772 Epoch 16 ACC = 0.9773 Epoch 17 ACC = 0.9782 Epoch 18 ACC = 0.9773 Epoch 19 ACC = 0.9798 Epoch 20 ACC = 0.9781 Epoch 21 ACC = 0.9795 Epoch 22 ACC = 0.9745 Epoch 23 ACC = 0.9813 Epoch 24 ACC = 0.9806 Epoch 25 ACC = 0.9804 Epoch 26 ACC = 0.9804 Epoch 27 ACC = 0.9786 Epoch 28 ACC = 0.9811 Epoch 29 ACC = 0.9811 Epoch 30 ACC = 0.9808 Epoch 31 ACC = 0.981 Epoch 32 ACC = 0.9809 Epoch 33 ACC = 0.9807 Epoch 34 ACC = 0.9807 Epoch 35 ACC = 0.982 Epoch 36 ACC = 0.9819 Epoch 37 ACC = 0.9816 Epoch 38 ACC = 0.9817 Epoch 39 ACC = 0.9809 Epoch 40 ACC = 0.9749 Epoch 41 ACC = 0.981 Epoch 42 ACC = 0.981 Epoch 43 ACC = 0.9811 Epoch 44 ACC = 0.9815 Epoch 45 ACC = 0.9813 Epoch 46 ACC = 0.9808 Epoch 47 ACC = 0.9811 Epoch 48 ACC = 0.9817 Epoch 49 ACC = 0.9819 Epoch 50 ACC = 0.982
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