数据集是10000个样本，前8000个训练集，后面的用来测试。每个样本是1*144(重构成12*12的矩阵)，将原始BiGAN有编码器、判别器和生成器，将里面的全连接层全部替换成了卷积。
from __future__ import print_function,division from keras.datasets import mnist from keras.layers import Input,Dense,Reshape,Flatten,Dropout,multiply,GaussianNoise from keras.layers import Batchnormalization,Activation,Embedding,ZeroPadding2D from keras.layers import MaxPooling2D,concatenate from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D,Conv2D from keras.models import Sequential,Model from keras.optimizers import Adam from keras import losses from keras.utils import to_categorical from pandas import read_csv import keras.backend as K import pandas as pd import matplotlib.pyplot as plt import numpy as np class BIGAN(): def __init__(self): self.img_rows = 12 self.img_cols = 12 self.channels = 1 self.img_shape = (self.img_rows,self.img_cols,self.channels) self.latent_dim = 100 optimizer = Adam(0.0002,0.5) # Build and compile the discriminator self.discriminator = self.build_discriminator() self.discriminator.compile(loss=[‘binary_crossentropy‘],optimizer=optimizer,metrics=[‘accuracy‘]) # Build the generator self.generator = self.build_generator() # Build the encoder self.encoder = self.build_encoder() # The part of the bigan that trains the discriminator and encoder self.discriminator.trainable = False # Generate image from sampled noise z = Input(shape=(self.latent_dim,)) img_ = self.generator(z) # Encode image img = Input(shape=self.img_shape) z_ = self.encoder(img) # Latent -> img is fake,and img -> latent is valid fake = self.discriminator([z,img_]) valid = self.discriminator([z_,img]) # Set up and compile the combined model # Trains generator to fool the discriminator self.bigan_generator = Model([z,img],[fake,valid]) self.bigan_generator.compile(loss=[‘binary_crossentropy‘,‘binary_crossentropy‘],optimizer=optimizer) def build_encoder(self): model = Sequential() model.add(Conv2D(16,kernel_size=3,strides=2,input_shape=self.img_shape,padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(32,padding="same")) model.add(ZeroPadding2D(padding=((0,1),(0,1)))) model.add(Batchnormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(64,padding="same")) model.add(Batchnormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Conv2D(128,strides=1,padding="same")) model.add(Batchnormalization(momentum=0.8)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(self.latent_dim)) model.summary() img = Input(shape=self.img_shape) z = model(img) return Model(img,z) def build_generator(self): model = Sequential() model.add(Dense(64 * 3 * 3,activation="relu",input_dim=self.latent_dim)) model.add(Reshape((3,3,64))) model.add(UpSampling2D()) model.add(Conv2D(64,padding="same")) model.add(Batchnormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(32,padding="same")) model.add(Batchnormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(self.channels,kernel_size=3,padding="same")) model.add(Activation("tanh")) model.summary() z = Input(shape=(self.latent_dim,)) gen_img = model(z) return Model(z,gen_img) def build_discriminator(self): z = Input(shape=(self.latent_dim,)) img = Input(shape=self.img_shape) d_in = concatenate([z,Flatten()(img)]) model = Dense(14*14,activation="relu")(d_in) model = Reshape((14,14,1))(model) model = Conv2D(16,padding="same")(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.25)(model) model = Conv2D(32,padding="same")(model) model = ZeroPadding2D(padding=((0,1)))(model) model = Batchnormalization(momentum=0.8)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.25)(model) model = Conv2D(64,padding="same")(model) model = Batchnormalization(momentum=0.8)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.25)(model) model = Conv2D(128,padding="same")(model) model = Batchnormalization(momentum=0.8)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.25)(model) model = Flatten()(model) validity = Dense(1,activation="sigmoid")(model) return Model([z,validity) def train(self,epochs,batch_size=128,sample_interval=50): # Load the dataset dataset = read_csv(‘GANData.csv‘) values = dataset.values XY= values n_train_hours1 =8000 n_train_hours2 = n_train_hours1+1 x_train=XY[:n_train_hours1,:] x_test =XY[n_train_hours2:,:] X_train = x_train.reshape(-1,12,1) X_test = x_test.reshape(-1,1) # Adversarial ground truths valid = np.ones((batch_size,1)) fake = np.zeros((batch_size,1)) FHZ=np.zeros((epochs,3)) for epoch in range(epochs): # --------------------- # Train discriminator # --------------------- # Sample noise and generate img z = np.random.normal(size=(batch_size,self.latent_dim)) imgs_ = self.generator.predict(z) # Select a random batch of images and encode idx = np.random.randint(0,X_train.shape[0],batch_size) imgs = X_train[idx] z_ = self.encoder.predict(imgs) # Train the discriminator (img -> z is valid,z -> img is fake) d_loss_real = self.discriminator.train_on_batch([z_,imgs],valid) d_loss_fake = self.discriminator.train_on_batch([z,imgs_],fake) d_loss = 0.5 * np.add(d_loss_real,d_loss_fake) # --------------------- # Train Generator # --------------------- # Train the generator (z -> img is valid and img -> z is is invalid) g_loss = self.bigan_generator.train_on_batch([z,[valid,fake]) # Plot the progress print ("%d [D loss: %f,acc: %.2f%%] [G loss: %f]" % (epoch,d_loss[0],100*d_loss[1],g_loss[0])) FHZ[epoch,0]=d_loss[0] FHZ[epoch,1]=d_loss[1] FHZ[epoch,2]=g_loss[0] # If at save interval => save generated image samples if epoch % sample_interval == 0: self.sample_interval(epoch) return FHZ def sample_interval(self,epoch): r,c = 5,5 z = np.random.normal(size=(25,self.latent_dim)) gen_imgs = self.generator.predict(z) gen_imgs = 0.5 * gen_imgs + 0.5 decoded_imgs = gen_imgs.reshape((gen_imgs.shape[0],-1)) print(‘decoded_imgs.shape:‘,decoded_imgs.shape) data=decoded_imgs data_df = pd.DataFrame(data) # create and writer pd.DataFrame to excel writer = pd.ExcelWriter(‘Result.xlsx‘) data_df.to_excel(writer,‘page_1‘,float_format=‘%.5f‘) # float_format 控制精度 writer.save() # Rescale images 0 - 1 #fig.savefig("images/mnist_%d.png" % epoch) if __name__ == ‘__main__‘: bigan = BIGAN() d_loss=bigan.train(epochs=10,batch_size=32,sample_interval=9) import numpy numpy.savetxt("d_lossnum.csv",d_loss,delimiter=‘,‘)
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