tf.string_join(inputs, separator=None, name=None) Joins the strings in the given list of string tensors into one tensor; with the given separator (default is an empty separator). Args: inputs: A list of at least 1 Tensor objects of type string. A list of string tensors. The tensors must all have the same shape, or be scalars. Scalars may be mixed in; these will be broadcast to the shape of non-scalar inputs. separator: An optional string. Defaults to "". string, an optional join separator.

tf.contrib.losses.cosine_distance(predictions, targets, dim, weight=1.0, scope=None) Adds a cosine-distance loss to the training procedure. Note that the function assumes that the predictions and targets are already unit-normalized. Args: predictions: An arbitrary matrix. targets: A Tensor whose shape matches 'predictions' dim: The dimension along which the cosine distance is computed. weight: Coefficients for the loss a scalar, a tensor of shape [batch_size] or a tensor whose shape matche

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape

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

tf.contrib.distributions.Distribution.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.input_dict

tf.contrib.distributions.Binomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Binomial') Initialize a batch of Binomial distributions. Args: n: Non-negative floating point tensor with shape broadcastable to [N1,..., Nm] with m >= 0 and the same dtype as p or logits. Defines this as a batch of N1 x ... x Nm different Binomial distributions. Its components should be equal to integer values. logits: Floating point tensor representing the log-odds of a po

tf.contrib.learn.monitors.EveryN.every_n_step_end(step, outputs) Callback after every n'th step finished. This callback provides access to the tensors/ops evaluated at this step, including the additional tensors for which evaluation was requested in step_begin. In addition, the callback has the opportunity to stop training by returning True. This is useful for early stopping, for example. Args: step: int, the current value of the global step. outputs: dict mapping string values representing

tf.nn.rnn_cell.LSTMCell.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 the shapes [b

tf.contrib.learn.monitors.NanLoss.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this step, or