tensorflow 实现神经网络带正则化

import matplotlib.pyplot as pltimport numpy as npimport tensorflow as tffrom sklearn import datasetsfrom tensorflow.python.framework import ops#data processingiris_data = datasets.load_iris()x_vals = iris_data.data      #shape(examples,features)y_vals = iris_data.target    #shape(examples,)x = np.transpose(x_vals)     #shape(features,examples)y = tf.one_hot(y_vals,depth=3,axis=0) #one hot codingwith tf.Session() as sess:    y = sess.run(y)train_indices = np.random.choice(len(x_vals),round(len(x_vals)*0.8),replace=False)test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))x_train = x[:,train_indices]x_test = x[:,test_indices]y_train = y[:,train_indices]y_test = y[:,test_indices]def initializer_parameters():    w1=tf.get_variable("w1",[8,4],initializer=tf.contrib.layers.xavier_initializer(dtype=tf.float32))    b1=tf.get_variable("b1",[8,1],initializer=tf.zeros_initializer(dtype=tf.float32))    w2=tf.get_variable("w2",[3,8],initializer=tf.contrib.layers.xavier_initializer(dtype=tf.float32))    b2=tf.get_variable("b2",[3,1],initializer=tf.zeros_initializer(dtype=tf.float32))    parameters={"w1":w1,                "b1":b1,                "w2":w2,                "b2":b2}    return parameters    def layers(w,b,x,actfunction=None):    z = tf.add(tf.matmul(w,x),b)    # z = tf.nn.dropout(z,dropout) #dropout regularization    if actfunction is None:        a = z    else:        a = actfunction(z)    return a    def model(x_train,y_train,x_test,y_test,learning_rate=0.005):    ops.reset_default_graph()        X_place=tf.placeholder(tf.float32,[4,None])    Y_place=tf.placeholder(tf.float32,[3,None])    #dropout=tf.placeholder(tf.float32)   #dropout regularlzation        parameters=initializer_parameters()    w1=parameters["w1"]    b1=parameters["b1"]    w2=parameters["w2"]    b2=parameters["b2"]        a1 = layers(w1,b1,X_place,actfunction=tf.nn.relu)    z2 = layers(w2,b2,a1)        #L2_loss=0.01*(tf.nn.l2_loss(w1)+tf.nn.l2_loss(b1)+tf.nn.l2_loss(w2)+tf.nn.l2_loss(b2)) #L2 regularization        cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=tf.transpose(z2),labels=tf.transpose(Y_place))) #+ L2_loss    optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005).minimize(cost)       init=tf.global_variables_initializer()        with tf.Session() as sess:        sess.run(init)            train_loss=[]        test_loss=[]            for epoch in range(1000):            _,each_cost=sess.run([optimizer,cost],feed_dict={X_place:x_train,Y_place:y_train})            # _,each_cost=sess.run([optimizer,cost],feed_dict={X_place:X_train,Y_place:Y_train,dropout:0.8}) # if use drouout            train_loss.append(each_cost)            test_cost=sess.run(cost,feed_dict={X_place:x_test,Y_place:y_test})            # test_cost=sess.run(cost,feed_dict={X_place:x_test,Y_place:y_test,dropout:1}) # if use dropout            test_loss.append(test_cost)            if (epoch+1)%50==0:                print('epoch'+str(epoch+1)+':cost'+str(each_cost))                parameters_new = sess.run(parameters)                w1_new=parameters_new["w1"]        b1_new=parameters_new["b1"]        w2_new=parameters_new["w2"]        b2_new=parameters_new["b2"]                a1 = layers(w1,b1,tf.to_float(x_test),actfunction=tf.nn.relu)        a2 = layers(w2,b2,a1,actfunction=tf.nn.softmax)        predict_test=sess.run(a2)                a1 = layers(w1,b1,tf.to_float(x_train),actfunction=tf.nn.relu)        a2 = layers(w2,b2,a1,actfunction=tf.nn.softmax)        predict_train=sess.run(a2)        a=0        for i in range(120):            if np.argmax(predict_train[:,i])==np.argmax(y_train[:,i]):                a=a+1        print('trainaccury:'+str(a/120))        b=0        for i in range(30):            if np.argmax(predict_test[:,i])==np.argmax(y_test[:,i]):                b=b+1        print('testaccury:'+str(b/30))    return train_loss,test_loss    train_loss,test_loss = model(x_train,y_train,x_test,y_test)%matplotlib inline# Plot loss (MSE) over timeplt.plot(train_loss, 'k-', label='Train Loss')plt.plot(test_loss, 'r--', label='test Loss')plt.title('Loss per Generation')plt.legend(loc='upper right')plt.xlabel('Generation')plt.ylabel('Loss')plt.show()