from datetime import datetime import os import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from six.moves import xrange data = np.load('final37.npy') data = data[:,:,0:60] #显示原始数据图像 def Show_images(data,show_nums,save=False): index = 0 for n in range(show_nums): show_images = data[index:index+100] show_images = show_images.reshape(100,3,60,1) r,c = 10,10 fig,axs = plt.subplots(r,c) cnt = 0 for i in range(r): for j in range(c): xy = show_images[cnt] for k in range(len(xy)): x = xy[k][0:30] y = xy[k][30:60] if k == 0 : axs[i,j].plot(x,y,color='blue',linewidth=2) if k == 1: axs[i,j].plot(x,y,color='red',linewidth=2) if k == 2: axs[i,j].plot(x,y,color='green',linewidth=2) axs[i,j].axis('off') cnt += 1 index += 100 if save: if not os.path.exists('This_epoch'): os.makedirs('This_epoch') fig.savefig('This_epoch/%d.jpg' % n) plt.close() else: plt.show() def Save_genImages(gen, epoch): r,c = 10,10 fig,axs = plt.subplots(r,c) cnt = 0 for i in range(r): for j in range(c): xy = gen[cnt] for k in range(len(xy)): x = xy[k][0:30] y = xy[k][30:60] if k == 0: axs[i,j].plot(x,y,color='blue') if k == 1: axs[i,j].plot(x,y,color='red') if k == 2: axs[i,j].plot(x,y,color='green') axs[i,j].axis('off') cnt += 1 if not os.path.exists('gen_img1'): os.makedirs('gen_img1') fig.savefig('gen_img1/%d.jpg' % epoch) plt.close() def Save_lossValue(epoch,iters,d_loss,g_loss): with open('losst.txt','a') as f: f.write("第%d个epoch,第%d个batch , d_loss: %.8f, g_loss: %.8f"%(epoch, iters, d_loss, g_loss)+'\n') def plot_loss(loss): fig,ax = plt.subplots(figsize=(20,7)) losses = np.array(loss) plt.plot(losses.T[0], label="Discriminator Loss") plt.plot(losses.T[1], label="Generator Loss") plt.title("Training Losses") plt.legend() plt.savefig('loss.jpg') plt.show() #定义Relu激活函数 def Relu(name, tensor): return tf.nn.relu(tensor,name) #定义LeakyRelu激活函数 def LeakyRelu(name, x, leak=0.2): return tf.maximum(x,leak*x,name=name) #定义全连接层 def Fully_connected(name, value, output_shape): with tf.variable_scope(name,reuse=None) as scope: shape = value.get_shape().as_list() w = tf.get_variable('w',[shape[1],output_shape],dtype=tf.float32, initializer=tf.random_normal_initializer(stddev=0.01)) b = tf.get_variable('b',[output_shape],dtype=tf.float32,initializer=tf.constant_initializer(0.0)) return tf.matmul(value,w) + b #定义一维卷积 def Conv1d(name, tensor, ksize, out_dim, stride, padding, stddev=0.01): with tf.variable_scope(name): w = tf.get_variable('w',[ksize,tensor.get_shape()[-1],out_dim],dtype=tf.float32, initializer=tf.random_normal_initializer(stddev=stddev)) var = tf.nn.conv1d(tensor,w,stride,padding=padding) b = tf.get_variable('b',[out_dim],'float32',initializer=tf.constant_initializer(0.01)) return tf.nn.bias_add(var,b) #定义二维卷积 def Conv2d(name, tensor, filter_size1 ,filter_size2, out_dim, stride1, stride2, padding, stddev=0.01): with tf.variable_scope(name): w = tf.get_variable('w',[filter_size1, filter_size2, tensor.get_shape()[-1], out_dim], dtype=tf.float32, initializer=tf.random_normal_initializer(stddev=stddev)) var = tf.nn.conv2d(tensor, w, [1, stride1, stride2, 1], padding=padding) b = tf.get_variable('b',[out_dim], 'float32', initializer=tf.constant_initializer(0.01)) return tf.nn.bias_add(var,b) #定义二维反卷积 def Deconv2d(name, tensor, filter_size1, filter_size2, outshape, stride1, stride2, padding, stddev=0.01): with tf.variable_scope(name): w = tf.get_variable('w', [filter_size1, filter_size2, outshape[-1], tensor.get_shape()[-1]], dtype=tf.float32, initializer=tf.random_normal_initializer(stddev=stddev)) var = tf.nn.conv2d_transpose(tensor, w, outshape, strides=[1,stride1, stride2, 1], padding=padding) b = tf.get_variable('b', [outshape[-1]],'float32', initializer=tf.constant_initializer(0.01)) return tf.nn.bias_add(var,b) def Get_inputs(real_size,noise_size): real_img = tf.placeholder(tf.float32, [None, real_size], name='real_img') noise_img = tf.placeholder(tf.float32, [None, noise_size], name='noise_img') return real_img, noise_img def Generator(noise_img, reuse=False, alpha=0.01): with tf.variable_scope('generator',reuse=reuse): # print(noise_img.shape) output = tf.layers.dense(noise_img,128) # print(output.shape) output = tf.maximum(alpha * output,output) output = tf.layers.batch_normalization(output,momentum=0.8,training=True) output = tf.layers.dropout(output, rate=0.25) output = tf.layers.dense(output,512) output = tf.maximum(alpha * output,output) output = tf.layers.batch_normalization(output,momentum=0.8,training=True) output = tf.layers.dropout(output,rate=0.25) output = tf.layers.dense(output,180) output = tf.tanh(output) return output def Discriminator(img,reuse=False,alpha=0.01): with tf.variable_scope("discriminator", reuse=reuse): print(img.shape) output = tf.layers.dense(img,512) output = tf.maximum(alpha * output, output) output = tf.layers.dense(output,128) output = tf.maximum(alpha * output, output) output = tf.layers.dense(output,1) return output mode = 'gan' #gan, wgan, wgan-gp batch_size = 100 epochs = 1 n_sample = 100 learning_rate = 0.0002 lamda = 10 img_size = 180 noise_size = 100 tf.reset_default_graph() real_img, noise_img = Get_inputs(img_size,noise_size)#feed于此 real_data = real_img fake_data = Generator(noise_img) disc_real = Discriminator(real_data,reuse=False) disc_fake = Discriminator(fake_data,reuse=True) #生成器和判别器中的tensor train_vars = tf.trainable_variables() g_vars = [var for var in train_vars if var.name.startswith("generator")] d_vars = [var for var in train_vars if var.name.startswith("discriminator")] #普通的GAN if mode == 'gan': gen_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_fake,labels=tf.ones_like(disc_fake))) #生成器loss disc_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_fake,labels=tf.zeros_like(disc_fake))) disc_cost += tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_real,labels=tf.ones_like(disc_real))) disc_cost /= 2. #判别器loss #优化器 gen_train_op = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5).minimize(gen_cost,var_list=g_vars) disc_train_op = tf.train.AdamOptimizer(learning_rate=2e-4,beta1=0.5).minimize(disc_cost,var_list=d_vars) clip_disc_weights = None #wgan elif mode == 'wgan': gen_cost = -tf.reduce_mean(disc_fake) #生成器loss disc_cost = tf.reduce_mean(disc_fake) - tf.reduce_mean(disc_real) #判别器loss #优化器 gen_train_op = tf.train.RMSPropOptimizer(learning_rate=5e-5).minimize(gen_cost,var_list=g_vars) disc_train_op = tf.train.RMSPropOptimizer(learning_rate=5e-5).minimize(disc_cost,var_list=d_vars) clip_ops = [] #将判别器权重截断到[-0.01,0.01] for var in train_vars: if var.name.startswith("discriminator"): clip_bounds = [-0.01, 0.01] clip_ops.append(tf.assign(var,tf.clip_by_value(var,clip_bounds[0],clip_bounds[1]))) clip_disc_weights = tf.group(*clip_ops) elif mode == 'wgan-gp': gen_cost = -tf.reduce_mean(disc_fake) #生成器loss disc_cost = tf.reduce_mean(disc_fake) - tf.reduce_mean(disc_real) #判别器loss #梯度惩罚 alpha = tf.random_uniform(shape=[batch_size,1],minval=0.,maxval=1.) interpolates = alpha*fake_data + (1-alpha)*real_data gradients = tf.gradients(Discriminator(interpolates,reuse=True),[interpolates])[0] slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients),reduction_indices=[1])) gradient_penalty = tf.reduce_mean((slopes-1.)**2) disc_cost += lamda * gradient_penalty clip_disc_weights = None #优化器 gen_train_op = tf.train.AdamOptimizer(learning_rate=1e-4,beta1=0.5,beta2=0.9).minimize(gen_cost,var_list=g_vars) disc_train_op = tf.train.AdamOptimizer(learning_rate=1e-4,beta1=0.5,beta2=0.9).minimize(disc_cost,var_list=d_vars) saver = tf.train.Saver() def Train(): losses = [] with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for e in range(epochs): for i in xrange(len(data)//batch_size): batch_images = data[i*batch_size:(i+1)*batch_size] batch_images = batch_images.reshape(batch_size,180) batch_images = batch_images*2 -1 batch_noise = np.random.uniform(-1,1,size=(batch_size,noise_size)) if mode == 'gan': #普通的gan，判别器，生成器各训练一次 disc_iters = 2 else: #wgan和wgan-gp，判别器训练多次，生成器训练一次 disc_iters = 2 for x in range(0,disc_iters): _,d_loss = sess.run([disc_train_op,disc_cost],feed_dict={real_data:batch_images,noise_img:batch_noise}) if clip_disc_weights is not None: _ = sess.run(clip_disc_weights) _,g_loss = sess.run([gen_train_op,gen_cost],feed_dict={noise_img:batch_noise}) Save_lossValue(e,i,d_loss,g_loss) print("第%d个epoch,第%d个batch , d_loss: %.8f, g_loss: %.8f"%(e, i, d_loss, g_loss)) losses.append((d_loss,g_loss)) sample_noise = np.random.uniform(-1,1,size=(100,100)) gen_samples = sess.run(Generator(noise_img,reuse=True),feed_dict={noise_img:sample_noise}) print(gen_samples.shape) saver.save(sess,'checkpoints/test.ckpt') if e % 1 == 0: gen = gen_samples.reshape(100,3,60,1) Save_genImages(gen, e) plot_loss(losses) def Test(): saver = tf.train.Saver(var_list=g_vars) with tf.Session() as sess: saver.restore(sess,tf.train.latest_checkpoint("checkpoints")) # saver.restore(sess,'checkppoints/b.ckpt') sample_noise = np.random.uniform(-1, 1, size=(10000,noise_size)) gen_samples = sess.run(Generator(noise_img,reuse=True),feed_dict={noise_img:sample_noise}) gen_images = (gen_samples+1)/2 show_num = len(gen_images)//100 Show_images(gen_images,show_num,save=True) if __name__ == '__main__': Train() #Test() 文章来源: WGAN-GP与GAN及WGAN的比较