Question

我正在构建一个使用LSTM作为鉴别器和分类器的GAN系统。具有相同错误的另一个问题对我没有帮助。错误是：

tensorflow.python.framework.errors_impl.InvalidArgumentError：您必须使用dtype float和shape [1,30,2]为占位符张量'sequential_2_input'提供值 [[节点：sequential_2_input = Placeholderdtype = DT_FLOAT，shape = [1,30,2]，_ device =“/ job：localhost / replica：0 / task：0 / device：CPU：0”]]

我正在尝试重新排列this示例，但我无法使其正常工作。当我尝试生成向发生器提供噪声的假例子时会出现异常。

这是我的代码：

from keras import Sequential
from keras.layers import LSTM, Dense, np, TimeDistributed
from keras.optimizers import RMSprop, Adam


def discriminator():
    net = Sequential()
    input_shape = (1, 30, 2)

    net.add(LSTM(10, stateful=True, batch_input_shape=input_shape))

    net.add(Dense(2, activation='softmax'))

    return net


def generator():
    net = Sequential()

    input_shape = (1, 30, 2)

    net.add(LSTM(10, return_sequences=True, stateful=True, batch_input_shape=input_shape))

    net.add(TimeDistributed(Dense(2, activation='linear')))

    return net


net_discriminator = discriminator()
# net_discriminator.summary()
net_generator = generator()
# net_generator.summary()

optim_discriminator = RMSprop(lr=0.0008, clipvalue=1.0, decay=1e-10)
model_discriminator = Sequential()
model_discriminator.add(net_discriminator)
model_discriminator.compile(loss='binary_crossentropy', optimizer=optim_discriminator, metrics=['accuracy'])

model_discriminator.summary()


optim_adversarial = Adam(lr=0.0004, clipvalue=1.0, decay=1e-10)
model_adversarial = Sequential()
model_adversarial.add(net_generator)

# Disable layers in discriminator
for layer in net_discriminator.layers:
    layer.trainable = False

model_adversarial.add(net_discriminator)
model_adversarial.compile(loss='binary_crossentropy', optimizer=optim_adversarial, metrics=['accuracy'])

model_adversarial.summary()


noise = np.random.normal(0, 1, (1, 30, 2))

fake_data = net_generator.predict(noise)

知道我做错了什么？

您必须为占位符张量'lstm_2_input'例外提供值

0 个答案: