tf.range(start, limit=None, delta=1, name='range') Creates a sequence of integers. Creates a sequence of integers that begins at start and extends by increments of delta up to but not including limit. Like the Python builtin range, start defaults to 0, so that range(n) = range(0, n). For example: # 'start' is 3 # 'limit' is 18 # 'delta' is 3 tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] # 'limit' is 5 tf.range(limit) ==> [0, 1, 2, 3, 4] Args: start: A 0-D (scalar) of type int32.

tf.contrib.learn.monitors.GraphDump.compare(other_dump, step, atol=1e-06) Compares two GraphDump monitors and returns differences. Args: other_dump: Another GraphDump monitor. step: int, step to compare on. atol: float, absolute tolerance in comparison of floating arrays. Returns: Returns tuple: matched: list of keys that matched. non_matched: dict of keys to tuple of 2 mismatched values. Raises: ValueError: if a key in data is missing from other_dump at step.

tf.nn.rnn_cell.GRUCell.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

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

tf.matrix_diag(diagonal, name=None) Returns a batched diagonal tensor with a given batched diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has k dimensions [I, J, K, ..., N], then the output is a tensor of rank k+1 with dimensions [I, J, K, ..., N, N]` where: output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]. For example: # 'diagonal' is [[1, 2, 3, 4],

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.input_dict

tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.NormalWithSoftplusSigma.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.graph_editor.select_ops(*args, **kwargs) Helper to select operations. Args: *args: list of 1) regular expressions (compiled or not) or 2) (array of) tf.Operation. tf.Tensor instances are silently ignored. **kwargs: 'graph': tf.Graph in which to perform the regex query.This is required when using regex. 'positive_filter': an elem if selected only if positive_filter(elem) is True. This is optional. 'restrict_ops_regex': a regular expression is ignored if it doesn't start with the su

tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double Ten