赞
踩
“”"
#数据加载及预处理
(x_train,y_train),_ = keras.datasets.mnist.load_data()
image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1,image_size,image_size,1])
x_train = x_train.astype(‘float32’) / 255.
num_labels = len(np.unique(y_train))
y_train = keras.utils.to_categorical(y_train)
#超参数
model_name = ‘acgan-mnist’
latent_size = 100
batch_size = 64
train_steps = 40000
lr = 2e-4
decay = 6e-8
input_shape = (image_size,image_size,1)
label_shape = (num_labels,)
#discriminator
inputs = keras.layers.Input(shape=input_shape,name=‘discriminator_input’)
discriminator = discriminator(inputs,num_labels=num_labels)
optimizer = keras.optimizers.RMSprop(lr=lr,decay=decay)
loss = [‘binary_crossentropy’,‘categorical_crossentropy’]
discriminator.compile(loss=loss,optimizer=optimizer,metrics=[‘acc’])
discriminator.summary()
#generator
input_shape = (latent_size,)
inputs = keras.layers.Input(shape=input_shape,name=‘z_input’)
labels = keras.layers.Input(shape=label_shape,name=‘labels’)
generator = generator(inputs,image_size,labels=labels)
generator.summary()
optimizer = keras.optimizers.RMSprop(lr=lr0.5,decay=decay0.5)
discriminator.trainable = False
adversarial = keras.Model([inputs,labels],discriminator(generator([inputs,labels])),
name=model_name)
adversarial.compile(loss=loss,optimizer=optimizer,metrics=[‘acc’])
adversarial.summary()
models = (generator,discriminator,adversarial)
data = (x_train,y_train)
params = (batch_size,latent_size,train_steps,num_labels,model_name)
train(models,data,params)
def train(models,data,params):
“”"Train the discriminator and adversarial Networks
Arguments:
models (list): generator,discriminator,adversarial
data (list): x_train,y_train
params (list): network parameter
“”"
generator,discriminator,adversarial = models
x_train,y_train = data
batch_size,latent_size,train_steps,num_labels,model_name = params
save_interval = 500
noise_input = np.random.uniform(-1.,1.,size=[16,latent_size])
noise_label = np.eye(num_labels)[np.arange(0,16) % num_labels]
train_size = x_train.shape[0]
print(model_name,'Labels for generated images: ',np.argmax(noise_label,axis=1))
for i in range(train_steps):
#训练鉴别器
rand_indexes = np.random.randint(0,train_size,size=batch_size)
real_images = x_train[rand_indexes]
real_labels = y_train[rand_indexes]
#产生伪造图片
noise = np.random.uniform(-1.,1.,size=(batch_size,latent_size))
fake_labels = np.eye(num_labels)[np.random.choice(num_labels,batch_size)]
fake_images = generator.predict([noise,fake_labels])
#构造输入
x = np.concatenate((real_images,fake_images))
#训练类别标签
labels = np.concatenate((real_labels,fake_labels))
#标签
y = np.ones([2*batch_size,1])
y[batch_size:,:] = 0.0
#训练模型
metrics = discriminator.train_on_batch(x,[y,labels])
fmt = ‘%d: [disc loss: %f, srcloss: %f],’
fmt += ‘lbloss: %f, srcacc: %f, lblacc: %f’
log = fmt % (i,metrics[0],metrics[1],metrics[2],metrics[3],metrics[4])
#train adversarial network for 1 batch
noise = np.random.uniform(-1.,1.,size=(batch_size,latent_size))
fake_labels = np.eye(num_labels)[np.random.choice(num_labels,batch_size)]
y = np.ones([batch_size,1])
metrics = adversarial.train_on_batch([noise,fake_labels],[y,fake_labels])
fmt = “%s [advr loss: %f, srcloss: %f,”
fmt += “lblloss: %f, srcacc: %f, lblacc: %f]”
log = fmt % (log, metrics[0], metrics[1], metrics[2], metrics[3], metrics[4])
print(log)
if (i + 1) % save_interval == 0:
plot_images(generator,noise_input=noise_input,
noise_label=noise_label,show=False,
step=(i + 1),
model_name=model_name)
generator.save(model_name + “.h5”)
def plot_images(generator,
noise_input,
noise_label=None,
noise_codes=None,
show=False,
step=0,
model_name=“gan”):
“”"生成虚假图片及绘制
generator (Model): 生成模型
noise_input (ndarray): 潜在模型
show (bool): 是否展示
step (int): step值
model_name (string): 模型名称
“”"
os.makedirs(model_name, exist_ok=True)
filename = os.path.join(model_name, “%05d.png” % step)
rows = int(math.sqrt(noise_input.shape[0]))
if noise_label is not None:
noise_input = [noise_input, noise_label]
if noise_codes is not None:
noise_input += noise_codes
images = generator.predict(noise_input)
plt.figure(figsize=(2.2, 2.2))
num_images = images.shape[0]
image_size = images.shape[1]
for i in range(num_images):
plt.subplot(rows, rows, i + 1)
image = np.reshape(images[i], [image_size, image_size])
plt.imshow(image, cmap=‘gray’)
plt.axis(‘off’)
plt.savefig(filename)
if show:
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