tf.contrib.distributions.Poisson.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.graph_editor.SubGraphView.remap_inputs(new_input_indices) Remap the inputs of the subgraph. If the inputs of the original subgraph are [t0, t1, t2], remapping to [2,0] will create a new instance whose inputs is [t2, t0]. Note that this is only modifying the view: the underlying tf.Graph is not affected. Args: new_input_indices: an iterable of integers representing a mapping between the old inputs and the new ones. This mapping can be under-complete and must be without repetitions.

tf.contrib.learn.TensorFlowEstimator.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.nn.rnn_cell.OutputProjectionWrapper.output_size

tf.contrib.distributions.Mixture.dtype The DType of Tensors handled by this Distribution.

tf.contrib.layers.avg_pool2d(*args, **kwargs) Adds a 2D average pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A Tensor of size [batch_size, height, width, channels]. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note tha

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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.NanLoss.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

tf.TensorArray.gather(indices, name=None) Return selected values in the TensorArray as a packed Tensor. All of selected values must have been written and their shapes must all match. Args: indices: A 1-D Tensor taking values in [0, max_value). If the TensorArray is not dynamic, max_value=size(). name: A name for the operation (optional). Returns: The in the TensorArray selected by indices, packed into one tensor.

tf.contrib.layers.layer_norm(*args, **kwargs) Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton Can be used as a normalizer function for conv2d and fully_connected. Args: inputs: a tensor with 2 or more dimensions. The normalization occurs over all but the first dimension. center: If True, subtract beta. If False, beta is ignored. scale: If True, multiply by gamma. If False, gamma is not used. Whe