必须为占位符张量'lstm\u 2\u input'异常提供一个值

Question

我正在建立一个以LSTM作为鉴别器和分类器的GAN系统。 另一个同样错误的问题对我没有帮助。 错误是：

tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'sequential_2_input' with dtype float and shape [1,30,2] [[Node: sequential_2_input = Placeholderdtype=DT_FLOAT, shape=[1,30,2], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

我试图重新安排this的例子，但我不能使它工作。当我试图生成向生成器提供噪声的假示例时，会出现异常。你知道吗

这是我的密码：

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)

你知道我做错了什么吗？你知道吗