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...
# 定义解码器 def build_decoder(latent_dim): inputs = layers.Input(shape=(latent_dim,)) h = layers.Dense(128, activation='relu')(inputs) h = layers.Dense(28 * 28, activation='sigmoid')(h) outputs = layers.Reshape((28, 28))(h) return keras.Model(inputs, outputs) # VAE模型 inp...
我将「latent_dim」设置为 1,对 GAN 训练了 600 步,结果如下: 图14:潜在维度为 1 的 GAN 试图拟合螺旋分布。灰色的点是从真实分布中抽取出的样本,红色的点是生成的样本。每一帧都是一个训练步。 相同的,GAN 也难以学到有效的映射。在经历了 30,000 个训练步后,学到的分布如下: 图15:图 14 中的...
def build_generator(): noise_dim = 100 latent_dim = 128 num_channels = 1 # 输入层 noise = Input(shape=(noise_dim,)) # 全连接层 x = Dense(latent_dim * 7 * 7, activation='relu')(noise) # 重塑为二维图像 x = Reshape((7, 7, latent_dim))(x) # 卷积层 x = Dense(128, acti...
generator = build_generator(NET_CAPACITY, FILTER_SIZE, LATENT_DIM) # 3. Build full GAN setup by stacking generator and discriminator. gan = keras.Sequential() gan.add(generator) gan.add(discriminator) discriminator.trainable = False # Fix the discriminator part in the full setup. gan.compile(...
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) -batch_size: ...
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) ...
(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...
(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,...
(opt.latent_dim,128,normalize = False), *block(128,256), *block(256,512), *block(512,1024), nn.Linear(1024,img_area), nn.Tanh() ) def forward(self,z): imgs = self.model(z) #imgs.view的意思, #(2, batch_size * channels * height * width) # imgs.size() torch.Size([2...