我有几个DistributionLambda层作为一个模型的输出，我想在一个新层中进行类似串联的操作，以便只有一个输出是所有分布的混合，假设它们是独立的。然后，我可以将对数似然损失应用于模型的输出。否则，我无法在Concatenate层上应用损失，因为它丢失了log_prob方法。我一直在尝试Blockwise发行版，但到目前为止没有成功。
这里有一个示例代码：
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow_probability import distributions
from tensorflow_probability import layers as tfp_layers
def likelihood_loss(y_true, y_pred):
"""Adding negative log likelihood loss."""
return -y_pred.log_prob(y_true)
def distribution_fn(params):
"""Distribution function."""
return distributions.Normal(
params[:, 0], math.log(1.0 + math.exp(params[:, 1])))
output_steps = 3
...
lstm_layer = layers.LSTM(10, return_state=True)
last_layer, l_h, l_c = lstm_layer(last_layer)
lstm_state = [l_h, l_c]
dense_layer = layers.Dense(2)
last_layer = dense_layer(last_layer)
last_layer = tfp_layers.DistributionLambda(
make_distribution_fn=distribution_fn)(last_layer)
output_layers = [last_layer]
# Get output sequence, re-injecting the output of each step
for number in range(1, output_steps):
last_layer = layers.Reshape((1, 1))(last_layer)
last_layer, l_h, l_c = lstm_layer(last_layer, initial_state=lstm_states)
# Storing state for next time step
lstm_states = [l_h, l_c]
last_layer = tfp_layers.DistributionLambda(
make_distribution_fn=distribution_fn)(dense_layer(last_layer))
output_layers.append(last_layer)
# This does not work
# last_layer = distributions.Blockwise(output_layers)
# This works for the model but cannot compute loss
# last_layer = layers.Concatenate(axis=1)(output_layers)
the_model = models.Model(inputs=[input_layer], outputs=[last_layer])
the_model.compile(loss=likelihood_loss, optimizer=optimizers.Adam(lr=0.001))
问题出在输入层，而不是输出层；(
错误消息中引用了输入：0。你能试着对你的输入更具体一点吗？