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抛物线在线生成,GAN生成抛物线

互联网 2021-02-28 14:03:24

本文主要讲解GAN的原理以及一个小实战,利用GAN生成抛物线,首先我们看一下GAN的原理。

GAN是2014年提出来的,他的原理可以这样理解,他有一个生成器和一个判别器,生成器是不断的生成数据,判别器的原理是将真实图片和生成器制作的数据区分开来,目的就是鉴别生成器生成的数据是假的,把原始数据判定为真。为生成器相反,他的目的就是源源不断的生成数据,让判别器无法分辨真假,从而以假乱真。常看到的一个例子就是坏人制作假币,警察差查假币。最终达到判别器无法分辨谁真谁假,也就是对于生成数据,它判断的为假的概率也是0.5,对于一个真实数据,它判断为真的概率也是0.5,这是最理想的状态。

任何一个神经网络模型都有损失函数,对于GAN模型,自然也不例外,他也有损失函数,因为他有丄生成网络和判别网络之分,所以当然是两个损失函数的,我们先看判别网路的损失函数,

min (-log(D(x)) -log(1-D(G(x)))) 这个就是判别网络的损失函数,这样理解,我们把真实数据判定为1是对的,把生成网络生成的数据判定为0是对的,也就是我们把真实数据判定为1那么损失函数越小,把生成数据判定为0,判别网络损失函数越小,然后我们再看损失函数,不难发现,要想损失函数越小,D(x)越接近1越好,这里x表示真实数据输入判别网络,可以看出D(G(x))越接近0损失越小,其中G(x)表示生成网络的输出输入判别网络。这也很符合我们平时的理解。再看生成网络的损失函数

min(-log(D(G(x))) 从生成网络我们可以看出,生成网络的目的是让D(G(x))越接近1越好,越就是D(G(x))越接近1,损失函数越小,这就和判别网络矛盾了,那就形成了竞争。

有了这些理论,我们再看实际代码实现生成抛物线的对抗神经网络:

import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltnp.random.seed(2018)tf.set_random_seed(2018)def real_data(num):#x = np.random.uniform(-10,10,[num,1])x = np.linspace(-3,3, num) + np.random.random(num) * 0.01x = x.reshape([-1,1])#sample = np.sin(x) + 1sample = x**2 +1return x,sampledef fake_data(num):x = np.linspace(-3,3, num) + np.random.random(num) * 0.01return x.reshape([-1,1])batch_size = 64iters = 2000#hidden_units = batch_size//2alpha = 0.01lr = 0.0001#gen_num = 10# out_dim = Nonedef generator(inputs,alpha,reuse=False):# out_dim 表示输出的大小,最后一层全连接层输出的大小,所以和batch_size大小一样# hidden_units隐藏层神经元的个数# alpha Leaky ReLU激活函数的参数# reuse 是否重用参数变量with tf.variable_scope('generator',reuse=reuse) as scope:hidden_1 = tf.layers.dense(inputs, 64, activation=None)ac1 = tf.maximum(alpha * hidden_1, hidden_1)#ac1 = tf.nn.tanh( hidden_1)bn1 = tf.layers.batch_normalization(ac1)hidden_2 = tf.layers.dense(bn1, 128, activation=None)ac2 = tf.maximum(alpha * hidden_2, hidden_2)#ac2 = tf.nn.tanh(hidden_2)bn2 = tf.layers.batch_normalization(ac2)hidden_3 = tf.layers.dense(bn2, 256, activation=None)ac3 = tf.maximum(alpha *hidden_3, hidden_3)#ac3 = tf.nn.tanh(ac2)bn3 = tf.layers.batch_normalization(ac3)out = tf.layers.dense(bn3,1,activation=None)return outdef discriminator(discr_input,alpha,reuse=False,name='discriminator'):# discr_input 判别器的输入# alpha Leaky ReLU激活函数的参数# hidden_units 隐藏层的神经元个数# reuse 是否重用变量with tf.variable_scope(name,reuse=reuse) as scope:hidden_1 = tf.layers.dense(discr_input,units=64,activation=None)#ac1 = tf.maximum(alpha*hidden_1,hidden_1)ac1 = tf.nn.tanh(hidden_1)hidden_2 = tf.layers.dense(ac1, units=128, activation=None)#ac2 = tf.maximum(alpha * hidden_2, hidden_2)ac2 = tf.nn.tanh(hidden_2)hidden_3 = tf.layers.dense(ac2, units=128, activation=None)#ac3 = tf.maximum(alpha * hidden_3, hidden_3)ac3 = tf.nn.tanh(hidden_3)logits = tf.layers.dense(ac3,1,activation=None)out = tf.nn.sigmoid(logits)return logits,outdef plot_data(gen_x,gen_y):x_r,y_r = real_data(64)plt.scatter(x_r, y_r, label='real data')plt.scatter(gen_x,gen_y, label='generated data')plt.title('GAN')plt.xlabel('x')plt.ylabel('y')plt.legend()plt.show()with tf.name_scope('gen_input') as scope:gen_input = tf.placeholder(dtype=tf.float32,shape=[None,1],name='gen_input')with tf.name_scope('discriminator_input') as scope:real_input = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='real_input')out_gen = generator(gen_input,alpha,reuse=False)real_logits,label_real = discriminator(real_input,alpha,reuse=False)logits_gen,label_fake = discriminator(out_gen,alpha,reuse=True)with tf.name_scope('discr_train') as scope:train_input = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='train_input')train_disc = discriminator(train_input,alpha,reuse=False,name='train_dis')para = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='train_dis')train_loss = tf.reduce_mean(tf.square(train_disc-train_input))#with tf.Session() as sess:#sess.run(tf.global_variables_initializer())with tf.name_scope('metrics') as name:loss_g = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits_gen)*0.99,logits=logits_gen))loss_d_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits_gen),logits=logits_gen))loss_d_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(real_logits)*0.99, logits=real_logits))loss_d = loss_d_fake+loss_d_realvar_list_g = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='generator')var_list_d = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='discriminator')d_optimizer = tf.train.AdamOptimizer(lr).minimize(loss_d,var_list=var_list_d)g_optimizer = tf.train.AdamOptimizer(lr).minimize(loss_g,var_list=var_list_g)with tf.Session() as sess:sess.run(tf.global_variables_initializer())saver = tf.train.Saver()writer = tf.summary.FileWriter('./graph/gan',sess.graph)# for i in range(1000):# _, real = real_data(batch_size)# _ = sess.run(train_loss,feed_dict={train_input:real})# train_weig = sess.run(para)# for i in range(len((var_list_d))):# sess.run(var_list_d[i].assign(train_weig[i]))for iter in range(iters):_,real = real_data(batch_size)fake = fake_data(batch_size)_,train_loss_d = sess.run([d_optimizer,loss_d],feed_dict={real_input:real,gen_input: fake})_, train_loss_g = sess.run([g_optimizer, loss_g], feed_dict={gen_input: fake})fake = fake_data(batch_size)_, train_loss_g = sess.run([g_optimizer, loss_g], feed_dict={gen_input: fake})fake = fake_data(batch_size)_, train_loss_g = sess.run([g_optimizer, loss_g], feed_dict={gen_input: fake})if iter % 200 == 0:print(train_loss_d)print(train_loss_g)gen_x = np.linspace(-3,3,500).reshape([-1,1])gen_y = sess.run(out_gen,feed_dict={gen_input:gen_x})plot_data(gen_x, gen_y)saver.save(sess, "./checkpoints/gen")writer.close() 下面展示一个训练过程的图像: 在做这个的时候,我试图生成正弦曲线,但是效果比较差,我猜测是和正弦函数泰勒展开有关系,又知道的希望提点一下。

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