我试图将How to use a MultiVariateNormal distribution in the latest version of Tensorflow中给出的例子推广到二维正态分布,但有多个批次。当我运行以下命令时:
from tensorflow_probability import distributions as tfd
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
mu = [[1, 2],
[-1,-2]]
cov = [[1, 3./5],
[3./5, 2]]
cov = [cov, cov] # for demonstration purpose, use same cov for both batches
mvn = tfd.MultivariateNormalFullCovariance(
loc=mu,
covariance_matrix=cov)
# generate the pdf
X, Y = tf.meshgrid(tf.range(-3, 3, 0.1), tf.range(-3, 3, 0.1))
idx = tf.concat([tf.reshape(X, [-1, 1]), tf.reshape(Y,[-1,1])], axis =1)
prob = tf.reshape(mvn.prob(idx), tf.shape(X))
我得到一个不兼容的形状错误:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [3600,2] vs. [2,2] [Op:Sub] name: MultivariateNormalFullCovariance/log_prob/affine_linear_operator/inverse/sub/
我对文档(https://www.tensorflow.org/api_docs/python/tf/contrib/distributions/MultivariateNormalFullCovariance)的理解是,要计算pdf,需要一个[n\u观测,n\u维]张量(这个例子就是这样:idx.shape
=TensorShape([Dimension(3600), Dimension(2)])
)。我的数学错了吗?你知道吗
您需要在倒数第二个位置向
idx
张量添加一个批处理轴,因为60x60不能针对(2,)
的mvn.batch_shape
进行广播。你知道吗输出:
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