tensorflow前馈神经网络

tensorflow前馈神经网络

# -*- coding: utf-8 -*-"""Created on Tue Jul 25 21:58:11 2017@author: maoyingxue"""import tensorflow as tfimport scipy.io as scioimport numpy as npdef load_data(name):    data=scio.loadmat(name+'.mat')    data_1 = data[name][2][0]    data_2 = data[name][2][1]    data_3 = data[name][2][2]    data = np.vstack((data_1, data_2, data_3)).astype(np.float32)    print(data.shape)    label_1=np.zeros([data_1.shape[0],3])    label_1[:,0]=1    #print(label_1)    label_2=np.zeros([data_2.shape[0],3])    label_2[:,1]=1    #print(label_2)    label_3=np.zeros([data_3.shape[0],3])    label_3[:,2]=1    #print(label_3)    label=np.vstack((label_1,label_2,label_3)).astype(np.float32)    print(label.shape)    for i in range(data.shape[0]):        m=max(data[i])        data[i]=data[i]/m      return data,labeltrain,train_label=load_data('train') test,test_label=load_data('test')     def add_layer(inputs,in_size,out_size,activation_function=None):    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, 128]) # 128ys = tf.placeholder(tf.float32, [None, 3])l1=add_layer(xs,128,200,activation_function=tf.nn.relu)prediction=add_layer(l1,200,3,activation_function=tf.nn.softmax)cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),                                              reduction_indices=[1]))       # losstrain_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)sess = tf.Session()sess.run(tf.global_variables_initializer())for i in range(6000):    sess.run(train_step,feed_dict={xs:train,ys:train_label})    if i%100==0:        print(compute_accuracy(test,test_label))