import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from numpy import hstack
from numpy import zeros
from numpy import ones
from numpy.random import rand
from numpy.random import randn
from keras.models import Sequential
from keras.layers import Dense
def define_discriminator(n_inputs=2):
model=Sequential()
model.add(Dense(25, activation='relu',kernel_initializer='he_uniform',input_dim=n_inputs))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])
return model
def define_generator(latent_dim,n_outputs=2):
model=Sequential()
model.add(Dense(15, activation='relu',kernel_initializer='he_uniform', input_dim=latent_dim))
model.add(Dense(n_outputs,activation='linear'))
return model
def define_gan(generator,discriminator):
discriminator.trainable=False
model=Sequential()
model.add(generator)
model.add(discriminator)
model.compile(loss='binary_crossentropy',optimizer='adam')
return model
def generate_real_samples(n):
x1=rand(n)-0.5
x2=x1*x1
x1=x1.reshape(n,1)
x2=x2.reshape(n,1)
x=hstack((x1,x2))
y=ones((n,1))
return x,y
def generate_latent_points(latent_dim,n):
x_input=randn(latent_dim*n)
x_input=x_input.reshape(n,latent_dim)
return x_input
def generate_fake_samples(generator,latent_dim,n):
x_input=generate_latent_points(latent_dim,n)
x=generator.predict(x_input)
y=zeros((n,1))
return x,y
def summarize_performance(epoch,generator,discriminator,latent_dim,n=100):
x_real,y_real=generate_real_samples(n)
_,acc_real=discriminator.evaluate(x_real,y_real,verbose=0)
x_fake, y_fake = generate_fake_samples(generator,latent_dim,n)
_, acc_fake = discriminator.evaluate(x_fake, y_fake, verbose=0)
print(epoch,acc_real,acc_fake)
plt.scatter(x_real[:,0],x_real[:,1],color='red')
plt.scatter(x_fake[:, 0], x_fake[:, 1], color='blue')
plt.show()
def train(g_model,d_model,gan_model,latent_dim,n_epochs=10000,n_batch=128,n_eval=2000):
half_batch=int(n_batch/2)
for i in range(n_epochs):
x_real,y_real=generate_real_samples(half_batch)
x_fake,y_fake=generate_fake_samples(g_model,latent_dim,half_batch)
d_model.train_on_batch(x_real,y_real)
d_model.train_on_batch(x_fake, y_fake)
x_gan=generate_latent_points(latent_dim,n_batch)
y_gan=ones((n_batch,1))
gan_model.train_on_batch(x_gan,y_gan)
if(i+1)%n_epochs==0:
summarize_performance(i,g_model,d_model,latent_dim)
latent_dim=5
discriminator=define_discriminator()
generator=define_generator(latent_dim)
gan_model=define_gan(generator,discriminator)
train(generator,discriminator,gan_model,latent_dim)
问题
