tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.rnn.GridLSTMCell.state_tuple_type

tf.contrib.graph_editor.swap_inputs(sgv0, sgv1) Swap all the inputs of sgv0 and sgv1 (see reroute_inputs).

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.clone(name=None, **dist_args)

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

tf.contrib.distributions.QuantizedDistribution.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.image.central_crop(image, central_fraction) Crop the central region of the image. Remove the outer parts of an image but retain the central region of the image along each dimension. If we specify central_fraction = 0.5, this function returns the region marked with "X" in the below diagram. -------- | | | XXXX | | XXXX | | | where "X" is the central 50% of the image. -------- Args: image: 3-D float Tensor of shape [height, width, depth] central_fraction: float (0, 1]

tf.contrib.learn.monitors.ExportMonitor.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.