tf.contrib.distributions.TransformedDistribution.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

Status tensorflow::Env::RegisterFileSystem(const string &scheme, FileSystemRegistry::Factory factory)

class tf.contrib.bayesflow.stochastic_tensor.NormalTensor NormalTensor is a StochasticTensor backed by the distribution Normal.

tf.contrib.distributions.InverseGamma.is_reparameterized

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.input_dict

tf.IdentityReader.supports_serialize Whether the Reader implementation can serialize its state.

tf.contrib.distributions.NormalWithSoftplusSigma.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='NormalWithSoftplusSigma')

tf.contrib.distributions.matrix_diag_transform(matrix, transform=None, name=None) Transform diagonal of [batch-]matrix, leave rest of matrix unchanged. Create a trainable covariance defined by a Cholesky factor: # Transform network layer into 2 x 2 array. matrix_values = tf.contrib.layers.fully_connected(activations, 4) matrix = tf.reshape(matrix_values, (batch_size, 2, 2)) # Make the diagonal positive. If the upper triangle was zero, this would be a # valid Cholesky factor. chol = matrix_di

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.entropy(name='entropy')

tf.nn.rnn_cell.EmbeddingWrapper.__call__(inputs, state, scope=None) Run the cell on embedded inputs.