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

tf.contrib.distributions.Multinomial.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.VarLenFeature.__repr__() Return a nicely formatted representation string

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.distribution

tf.contrib.framework.assert_or_get_global_step(graph=None, global_step_tensor=None) Verifies that a global step tensor is valid or gets one if None is given. If global_step_tensor is not None, check that it is a valid global step tensor (using assert_global_step). Otherwise find a global step tensor using get_global_step and return it. Args: graph: The graph to find the global step tensor for. global_step_tensor: The tensor to check for suitability as a global step. If None is given (the def

tf.contrib.framework.assert_same_float_dtype(tensors=None, dtype=None) Validate and return float type based on tensors and dtype. For ops such as matrix multiplication, inputs and weights must be of the same float type. This function validates that all tensors are the same type, validates that type is dtype (if supplied), and returns the type. Type must be dtypes.float32 or dtypes.float64. If neither tensors nor dtype is supplied, default to dtypes.float32. Args: tensors: Tensors of input val

tf.contrib.distributions.Poisson.__init__(lam, validate_args=False, allow_nan_stats=True, name='Poisson') Construct Poisson distributions. Args: lam: Floating point tensor, the rate parameter of the distribution(s). lam must be positive. validate_args: Boolean, default False. Whether to assert that lam > 0 as well as inputs to pmf computations are non-negative integers. If validate_args is False, then pmf computations might return NaN, but can be evaluated at any real value. allow_nan_st

tf.contrib.learn.TensorFlowRNNRegressor.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.graph_editor.make_list_of_t(ts, check_graph=True, allow_graph=True, ignore_ops=False) Convert ts to a list of tf.Tensor. Args: ts: can be an iterable of tf.Tensor, a tf.Graph or a single tensor. check_graph: if True check if all the tensors belong to the same graph. allow_graph: if False a tf.Graph cannot be converted. ignore_ops: if True, silently ignore tf.Operation. Returns: A newly created list of tf.Tensor. Raises: TypeError: if ts cannot be converted to a list of tf.Ten

tf.contrib.graph_editor.filter_ops_from_regex(ops, regex) Get all the operations that match the given regex. Args: ops: an object convertible to a list of tf.Operation. regex: a regular expression matching the operation's name. For example, "^foo(/.*)?$" will match all the operations in the "foo" scope. Returns: A list of tf.Operation. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.