tf.contrib.learn.monitors.CaptureVariable.every_n_step_end(step, outputs)

tf.contrib.distributions.NormalWithSoftplusSigma.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.

tf.contrib.learn.monitors.ExportMonitor.post_step(step, session)

tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.dtype

tf.contrib.distributions.Bernoulli.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.learn.monitors.StepCounter.post_step(step, session)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate along a new outer dimension. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.cumsum(x, axis=0, exclusive=False, reverse=False, name=None) Compute the cumulative sum of the tensor x along axis. By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output: prettyprint tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c] By setting the exclusive kwarg to True, an exclusive cumsum is performed instead: prettyprint tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b] By setting the rev

tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None) Returns the element-wise sum of a list of tensors. Optionally, pass shape and tensor_dtype for shape and type checking, otherwise, these are inferred. NOTE: This operation is not differentiable and cannot be used if inputs depend on trainable variables. Please use tf.add_n for such cases. For example: # tensor 'a' is [[1, 2], [3, 4]] # tensor `b` is [[5, 0], [0, 6]] tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]] # Explicit