from __future__ import print_function, division
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
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
import os
import matplotlib.pyplot as plt
import sys
import numpy as np
class LSGAN():
def __init__(self):
self.img_rows = 3
self.img_cols = 60
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='mse',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
# The generator takes noise as input and generated imgs
z = Input(shape=(self.latent_dim,))
img = self.generator(z)
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The valid takes generated images as input and determines validity
valid = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains generator to fool discriminator
self.combined = Model(z, valid)
# (!!!) Optimize w.r.t. MSE loss instead of crossentropy
self.combined.compile(loss='mse', optimizer=optimizer)
def build_generator(self):
model = Sequential()
model.add(Dense(2048, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
# model.add(Dense(128))
# model.add(LeakyReLU(alpha=0.2))
# model.add(BatchNormalization(momentum=0.8))
#
# model.add(Dense(64))
# model.add(LeakyReLU(alpha=0.2))
# model.add(BatchNormalization(momentum=0.8))
model.add(Dense(np.prod(self.img_shape), activation='tanh'))
model.add(Reshape(self.img_shape))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
def build_discriminator(self):
model = Sequential()
model.add(Flatten(input_shape=self.img_shape))
model.add(Dense(2048))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.2))
# model.add(Dense(128))
# model.add(LeakyReLU(alpha=0.2))
# (!!!) No softmax
model.add(Dense(1))
model.summary()
img = Input(shape=self.img_shape)
validity = model(img)
return Model(img, validity)
def train(self, epochs, batch_size=128, sample_interval=50):
############################################################
#自己数据集此部分需要更改
# 加载数据集
data = np.load('data/相对大小分叉.npy')
data = data[:,:,0:60]
print(data.shape)
# 归一化到-1到1
data = data * 2 - 1
data = np.expand_dims(data, axis=3)
############################################################
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random batch of images
idx = np.random.randint(0, data.shape[0], batch_size)
imgs = data[idx]
# Sample noise as generator input
noise = np.random.uniform(-1, 1, (batch_size, self.latent_dim))
# Generate a batch of new images
gen_imgs = self.generator.predict(noise)
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, valid)
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
g_loss = self.combined.train_on_batch(noise, valid)
# Plot the progress
print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
# 每sample_interval个epoch保存一次生成图片
if epoch % sample_interval == 0:
self.sample_images(epoch)
if not os.path.exists("keras_model"):
os.makedirs("keras_model")
self.generator.save_weights("keras_model/G_model%d.hdf5" % epoch,True)
self.discriminator.save_weights("keras_model/D_model%d.hdf5" %epoch,True)
def sample_images(self, epoch):
r, c = 10, 10
# 重新生成一批噪音，维度为(100,100)
noise = np.random.uniform(-1, 1, (r * c, self.latent_dim))
gen_imgs = self.generator.predict(noise)
# 将生成的图片重新归整到0-1之间
gen = 0.5 * gen_imgs + 0.5
gen = gen.reshape(-1,3,60)
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')
plt.xlim(0.,1.)
plt.ylim(0.,1.)
plt.xticks(np.arange(0,1,0.1))
plt.xticks(np.arange(0,1,0.1))
axs[i,j].axis('off')
cnt += 1
if not os.path.exists("keras_imgs"):
os.makedirs("keras_imgs")
fig.savefig("keras_imgs/%d.png" % epoch)
plt.close()
def test(self,gen_nums=100,save=False):
self.generator.load_weights("keras_model/G_model4000.hdf5",by_name=True)
self.discriminator.load_weights("keras_model/D_model4000.hdf5",by_name=True)
noise = np.random.uniform(-1,1,(gen_nums,self.latent_dim))
gen = self.generator.predict(noise)
gen = 0.5 * gen + 0.5
gen = gen.reshape(-1,3,60)
print(gen.shape)
###############################################################
#直接可视化生成图片
if save:
for i in range(0,len(gen)):
plt.figure(figsize=(128,128),dpi=1)
plt.plot(gen[i][0][0:30],gen[i][0][30:60],color='blue',linewidth=300)
plt.plot(gen[i][1][0:30],gen[i][1][30:60],color='red',linewidth=300)
plt.plot(gen[i][2][0:30],gen[i][2][30:60],color='green',linewidth=300)
plt.axis('off')
plt.xlim(0.,1.)
plt.ylim(0.,1.)
plt.xticks(np.arange(0,1,0.1))
plt.yticks(np.arange(0,1,0.1))
if not os.path.exists("keras_gen"):
os.makedirs("keras_gen")
plt.savefig("keras_gen"+os.sep+str(i)+'.jpg',dpi=1)
plt.close()
##################################################################
#重整图片到0-1
else:
for i in range(len(gen)):
plt.plot(gen[i][0][0:30],gen[i][0][30:60],color='blue')
plt.plot(gen[i][1][0:30],gen[i][1][30:60],color='red')
plt.plot(gen[i][2][0:30],gen[i][2][30:60],color='green')
plt.xlim(0.,1.)
plt.ylim(0.,1.)
plt.xticks(np.arange(0,1,0.1))
plt.xticks(np.arange(0,1,0.1))
plt.show()
if __name__ == '__main__':
gan = LSGAN()
gan.train(epochs=30000000, batch_size=64, sample_interval=1000)
