Question

我想使用PGGAN生成器为基于Encoder-Generator训练的真实输入图像找到相似的图像。下面是我的实现：

# load pre-trained generator
sess = tf.InteractiveSession()
with open('network-snapshot-final.pkl', 'rb') as file:
    G, D, Gs = pickle.load(file)

# network parameters
image_size = 1024
input_shape = (image_size, image_size, 1)
batch_size = 8
kernel_size = 3
filters = 16
latent_dim = 512
epochs = 100

# build an encoder
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
for i in range(10):
    filters *= 2
    x = Conv2D(filters=filters,
               kernel_size=kernel_size,
               activation='relu',
               strides=2,
               padding='same')(x)

# generate latent vector
x = Flatten()(x)
x = Dense(2048, activation='relu')(x)
z_sim = Dense(latent_dim, name='z_sim')(x)

encoder = Model(inputs, z_sim, name='encoder')

# define a custom loss function
def loss_enc(x, z_sim):
    im_g = tf.convert_to_tensor(Gs.run(z_sim.eval(), labels))
    im_g2 = tf.reshape(im_g, [-1, 1024, 1024, 1])
    los = mse(K.flatten(x), K.flatten(im_g2))
    return los

编译模型后，遇到如下错误消息：

encoder.compile(optimizer='rmsprop', loss=loss_enc)

InvalidArgumentError：必须输入占位符张量的值 'encoder_input_19'，其dtype的浮动类型为[？，1024,1024,1] [[{{node encoder_input_19}} = Placeholderdtype = DT_FLOAT，形状= [？，1024,1024,1]， _device =“ / job：localhost / replica：0 / task：0 / device：GPU：0”]] [[{{node z_sim_12 / BiasAdd / _713}} = _Recvclient_terminated = false， recv_device =“ / job：localhost /副本：0 / task：0 / device：CPU：0”， send_device =“ / job：localhost /副本：0 / task：0 / device：GPU：0”， send_device_incarnation = 1，tensor_name =“ edge_127_z_sim_12 / BiasAdd”， tensor_type = DT_FLOAT， _device =“ / job：localhost /副本：0 /任务：0 /设备：CPU：0”]]

如何为此目的正确实现损失功能？

Answer 1

首先：

def loss_enc(x, z_sim):
   def loss(y_pred, y_true):
     # Things you would do with x, z_sim and store in 'result' (for example)
   return result
return loss

编译模型时：

encoder.compile(optimizer='rmsprop', loss=loss_enc(x, z_sim))

如何在Keras中实现包括GAN生成器的自定义损失函数？

1 个答案: