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.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.name

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.

tensorflow::WritableFile::WritableFile()

tf.transpose(a, perm=None, name='transpose') Transposes a. Permutes the dimensions according to perm. The returned tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors. For example: # 'x' is [[1 2 3] # [4 5 6]] tf.transpose(x) ==> [[1 4] [2 5] [3 6]]

tf.contrib.rnn.LayerNormBasicLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.distribution

tf.contrib.losses.absolute_difference(predictions, targets, weight=1.0, scope=None) Adds an Absolute Difference loss to the training procedure. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weight vector. If the shape of weight matches the shape of predictions, then the loss of each

tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.