n 批次 > 1 的多元正态分布
multivariateNormal distribution with n-batch > 1
我试图将 中给出的示例概括为二维正态分布,但有多个批次。当我 运行 以下内容时:
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))
我收到不兼容的形状错误:
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_observation, n_dimensions] 张量(本例中就是这种情况: idx.shape
= TensorShape([Dimension(3600), Dimension(2)])
)。我算错了吗?
您需要在倒数第二个位置的 idx
张量中添加批处理轴,因为 60x60 无法针对 (2,)
的 mvn.batch_shape
进行广播。
# TF/TFP Imports
!pip install --quiet tfp-nightly tf-nightly
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
import tensorflow_probability as tfp
tfd = tfp.distributions
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)
print(mvn.batch_shape, mvn.event_shape)
# generate the pdf
X, Y = tf.meshgrid(tf.range(-3, 3, 0.1), tf.range(-3, 3, 0.1))
print(X.shape)
idx = tf.stack([X, Y], axis=-1)[..., tf.newaxis, :]
print(idx.shape)
probs = mvn.prob(idx)
print(probs.shape)
输出:
(2,) (2,) # mvn.batch_shape, mvn.event_shape
(60, 60) # X.shape
(60, 60, 1, 2) # idx.shape == X.shape + (1 "broadcast against batch", 2 "event")
(60, 60, 2) # probs.shape == X.shape + (2 "mvn batch shape")
我试图将
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))
我收到不兼容的形状错误:
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_observation, n_dimensions] 张量(本例中就是这种情况: idx.shape
= TensorShape([Dimension(3600), Dimension(2)])
)。我算错了吗?
您需要在倒数第二个位置的 idx
张量中添加批处理轴,因为 60x60 无法针对 (2,)
的 mvn.batch_shape
进行广播。
# TF/TFP Imports
!pip install --quiet tfp-nightly tf-nightly
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
import tensorflow_probability as tfp
tfd = tfp.distributions
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)
print(mvn.batch_shape, mvn.event_shape)
# generate the pdf
X, Y = tf.meshgrid(tf.range(-3, 3, 0.1), tf.range(-3, 3, 0.1))
print(X.shape)
idx = tf.stack([X, Y], axis=-1)[..., tf.newaxis, :]
print(idx.shape)
probs = mvn.prob(idx)
print(probs.shape)
输出:
(2,) (2,) # mvn.batch_shape, mvn.event_shape
(60, 60) # X.shape
(60, 60, 1, 2) # idx.shape == X.shape + (1 "broadcast against batch", 2 "event")
(60, 60, 2) # probs.shape == X.shape + (2 "mvn batch shape")