tf.contrib.graph_editor.assign_renamed_collections_handler(info, elem, elem_) Add the transformed elem to the (renamed) collections of elem. Args: info: Transform._Info instance. elem: the original element (tf.Tensor or tf.Operation) elem_: the transformed element

tf.contrib.distributions.LaplaceWithSoftplusScale.variance(name='variance') Variance.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.distributions.Laplace.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.bayesflow.monte_carlo.expectation(f, p, z=None, n=None, seed=None, name='expectation') Monte Carlo estimate of an expectation: E_p[f(Z)] with sample mean. This Op returns n^{-1} sum_{i=1}^n f(z_i), where z_i ~ p \approx E_p[f(Z)] User supplies either Tensor of samples z, or number of samples to draw n Args: f: Callable mapping samples from p to Tensors. p: tf.contrib.distributions.BaseDistribution. z: Tensor of samples from p, produced by p.sample_n. n: Integer Tensor. Number

tf.contrib.graph_editor.swap_ts(ts0, ts1, can_modify=None, cannot_modify=None) For each tensor's pair, swap the end of (t0,t1). B0 B1 B0 B1 | | => X A0 A1 A0 A1 Args: ts0: an object convertible to a list of tf.Tensor. ts1: an object convertible to a list of tf.Tensor. can_modify: iterable of operations which can be modified. Any operation outside within_ops will be left untouched by this function. cannot_modify: iterable of operations which cannot be modified. Any operation within canno

tf.contrib.learn.monitors.BaseMonitor.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.distributions.QuantizedDistribution.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.

tf.image.resize_bilinear(images, size, align_corners=None, name=None) Resize images to size using bilinear interpolation. Input images can be of different types but output images are always float. Args: images: A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, half, float32, float64. 4-D with shape [batch, height, width, channels]. size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images. align_corners: An optional bool. Defaul

tf.sign(x, name=None) Returns an element-wise indication of the sign of a number. y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0. For complex numbers, y = sign(x) = x / |x| if x != 0, otherwise y = 0. Args: x: A Tensor or SparseTensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor or SparseTensor, respectively. Has the same type as x.