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

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

tf.contrib.distributions.WishartFull.is_reparameterized

tf.contrib.distributions.Mixture.mean(name='mean') Mean.

tf.contrib.training.bucket(tensors, which_bucket, batch_size, num_buckets, num_threads=1, capacity=32, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, keep_input=None, shared_name=None, name=None) Lazy bucketing of input tensors according to which_bucket. The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors. The tensors entering this function are put into the bucket given by which_bucket. Each buc

tf.contrib.distributions.MultivariateNormalCholesky.mean(name='mean') Mean.

tf.TextLineReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_reparameterized

tf.InteractiveSession.close() Closes an InteractiveSession.