tf.contrib.distributions.Mixture.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

Static helper routines for TensorShape. Includes a few common predicates on a tensor shape. Member Details static bool tensorflow::TensorShapeUtils::IsScalar(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsVector(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsVectorOrHigher(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsMatrix(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsSquareMatrix(con

tf.contrib.graph_editor.OpMatcher.__call__(op) Evaluate if the op matches or not.

tf.contrib.distributions.Distribution.is_continuous

tf.contrib.learn.monitors.BaseMonitor.post_step(step, session) Callback after the step is finished. Called after step_end and receives session to perform extra session.run calls. If failure occurred in the process, will be called as well. Args: step: int, global step of the model. session: Session object.

class tf.errors.InvalidArgumentError Raised when an operation receives an invalid argument. This may occur, for example, if an operation is receives an input tensor that has an invalid value or shape. For example, the tf.matmul() op will raise this error if it receives an input that is not a matrix, and the tf.reshape() op will raise this error if the new shape does not match the number of elements in the input tensor.

class tf.nn.rnn_cell.GRUCell Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).

tf.listdiff(x, y, out_idx=None, name=None) Computes the difference between two lists of numbers or strings. Given a list x and a list y, this operation returns a list out that represents all values that are in x but not in y. The returned list out is sorted in the same order that the numbers appear in x (duplicates are preserved). This operation also returns a list idx that represents the position of each out element in x. In other words: out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1] F

tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None) Run the cell and output projection on inputs, starting from state.

tf.nn.rnn_cell.RNNCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [ba