(batch_size, opt.latent_dim))) gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, batch_size))) # Generate a batch of images gen_imgs = generator(z, gen_labels) # Loss measures generator's ability to fool the discriminator validity = discriminator(gen_imgs, gen_lab...
Model): # 构造函数初始化 def __init__(self, discriminator, generator, latent_dim): super().__init__() # 调用超类的构造函数 # 将判别器、生成器和潜在维度作为对象属性 self.discriminator = discriminator self.generator = generator self.latent_dim = latent_dim # 编译模型 def compile(self, d...
d_loss=discriminator.test_on_batch(combined_images, labels) random_latent_vectors= np.random.normal(size=(batch_size, latent_dim)) misleading_targets= np.zeros((batch_size, 1)) a_loss=gan.train_on_batch(random_latent_vectors, misleading_targets) start+=batch_sizeifstart > len(x_train) -...
LATENT_DIM) pred_raw = generator.predict(noise)[0] pred = pred_raw * 0.5 + 0.5 plt.subplot(1, NUM_SAMPLES, i + 1) plt.imshow(pred) plt.show()Samples from trained GAN
latent_dim = 32 height = 32 width = 32 channels = 3 # 输入是一个潜在的随机向量,向量shape为(32,) generator_input = keras.Input(shape=(latent_dim,)) # 第一层是一个全连接成,神经元的个数为(128*16*16) x = layers.Dense(128*16*16)(generator_input) ...
def __init__(self, latent_dim): super(Generator, self).__init__() self.model = nn.Sequential( nn.Linear(latent_dim, 256), nn.ReLU(), nn.BatchNorm1d(256), nn.Linear(256, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 784), ...
generator_input = keras.Input(shape=(latent_dim,)) # First, transform the input into a 16x16 128-channels feature map # 将输入转换为大小为 16×16 的128 个通道的特征图 x = layers.Dense(128 * 16 * 16)(generator_input) x = layers.LeakyReLU()(x) ...
(256, input_dim=self.latent_dim)) 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(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(Batch...
self.parse.add_argument("--latent_dim", type=int, default=5) # 隐含变量的维度 self.parse.add_argument("--data_dim", type=int, default=10) # 观测变量的维度 self.parse.add_argument("--data_size", type=int, default=10000) # 样本数 ...
(opt.latent_dim,128,normalize=False),# 100 -> 128*block(128,256),# 128 -> 256*block(256,512),# 256 -> 512*block(512,1024),# 512 -> 1024nn.Linear(1024,int(np.prod(img_shape))),# 1024 -> 764nn.Tanh())# 前向计算defforward(self,z):img=self.model(z)# torch.Size([64,...