tensorflow - 如何从Tensorflow概率将多个参数传递给DistributionLambda层？

我正在使用Keras和Tensorflow概率构建模型，该模型应输出Gamma函数（alpha和beta）的参数，而不是如以下示例中所示的单个参数（将t传递给Normal分布函数）。

import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions

# Build model.
model = tf.keras.Sequential([
  tf.keras.layers.Dense(1),
  tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1)),
])

# Do inference.
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.05), loss=negloglik)
model.fit(x, y, epochs=500, verbose=False)

# Make predictions.
yhat = model(x_tst)

取而代之的是，我想从两个alpha层输出beta和Dense，然后将此参数传递给Gamma分布函数。

最佳答案

像这样吗

import tensorflow as tf
tf.enable_eager_execution()

print(tf.__version__) # 1.14.1-dev20190503

import tensorflow_probability as tfp
tfd = tfp.distributions

X = np.random.rand(4, 1).astype(np.float32)

d0 = tf.keras.layers.Dense(2)(X)
s0, s1 = tf.split(d0, 2)
dist = tfp.layers.DistributionLambda(lambda t: tfd.Gamma(t[0], t[1]))(s0, s1)

dist.sample()
# <tf.Tensor: id=10580, shape=(2,), dtype=float32, numpy=array([1.1754944e-38, 1.3052921e-01], dtype=float32)>