匯入一些需要的包
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 matplotlib.pyplot as plt
import sys
import numpy as np
optimizer = adam(
0.0002
,0.5
)
n_y_value =
20d = sequential(
)d.add(dense(
512)
)d.add(leakyrelu(alpha=
0.2)
)d.add(dense(
256)
)d.add(dense(
1, activation=
'sigmoid'))
# d.summary()
img = input(shape=
(n_y_value,))
validity = d(img)
discriminator = model(img,validity)
discriminator.
compile
(loss=
'binary_crossentropy'
, optimizer=optimizer,
metrics=
['accuracy'
])
n_ideas =
5g = sequential(
)g.add(dense(
512,input_dim=n_ideas)
)g.add(leakyrelu(alpha=
0.2)
)g.add(batchnormalization(momentum=
0.8)
)g.add(dense(
512)
)g.add(leakyrelu(alpha=
0.2)
)g.add(batchnormalization(momentum=
0.8)
)g.add(dense(
1024))
g.add(leakyrelu(alpha=
0.2)
)g.add(batchnormalization(momentum=
0.8)
)g.add(dense(n_y_value, activation=
'tanh'))
# g.add(reshape(n_y_value))
# g.summary()
noise = input(shape=
(n_ideas,))
g_img = g(noise)
generator = model(noise,g_img)
z = input(shape=
(n_ideas,))
g_img = generator(z)
discriminator.trainable =
false
validity = discriminator(g_img)
gan = model(z,validity)
gan.
compile
(loss=
'binary_crossentropy'
, optimizer=optimizer)
batch_size=
64x= np.vstack(
[np.linspace(-1
,1,n_y_value)
for _ in
range
(batch_size)])
true_imgs =np.power(x,2)
valid = np.ones(
(batch_size,1)
)fake = np.zeros(
(batch_size,1)
)plt.ion(
)for i in
range
(1600):
noise = np.random.normal(0,
1,(batch_size, n_ideas)
) g_imgs = generator.predict(noise)
d_loss_fake = discriminator.train_on_batch(g_imgs,fake)
d_loss_real = discriminator.train_on_batch(true_imgs,valid)
d_loss =
0.5* np.add(d_loss_real, d_loss_fake)
g_loss = gan.train_on_batch(noise,valid)
print
("%d [d loss: %f, acc.: %.2f%%] [g loss: %f]"
%(i, d_loss[0]
,100
* d_loss[1]
, g_loss)
)# print("g_imgs.shape:",g_imgs.shape) #(64,20)
plt.cla(
) plt.xlim((-
1.2,
1.2)
) plt.ylim((-
0.2,
1.2)
) plt.plot(x[0]
, true_imgs[0]
, lw=
2, c=
'#11aaaa'
) plt.plot(x[0]
,g_imgs[0]
, lw=
2, c=
'#b62a2a'
) plt.pause(
0.01
)plt.ioff(
)plt.show(
)
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