如何在一个张量流概率层中混合多个分布？

Question

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连接的操作，以便只有一个输出是所有分布的混合，假设他们是独立的。然后，我可以对模型的输出应用对数似然损失。否则，我无法将损失应用于Concatenate 层，因为它丢失了log_prob 方法。我一直在尝试使用 Blockwise 分发，但到目前为止没有运气。

这里是一个示例代码：

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))

Answer 1

问题是你的输入，而不是你的输出层；）

Input:0 在您的错误消息中被引用。 您能否尝试更具体地说明您的意见？