tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the mean of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is returned

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.name Name prepended to all ops created by this Distribution.

tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) Evaluates given model with provided evaluation data. See superclass Estimator for more details. Args: x: features. y: targets. input_fn: Input function. feed_fn: Function creating a feed dict every time it is called. batch_size: minibatch size to use on the input. steps: Number of steps for which to evaluate model. metrics: Dict of metric ops t

tf.contrib.distributions.WishartFull.df Wishart distribution degree(s) of freedom.

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

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

tf.IdentityReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, learning_rate_decay_fn=None, update_ops=None, variables=None, name=None, summaries=None) Given loss and parameters for optimizer, returns a training op. Various ways of passing optimizers, include: - string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES for full list. E.g. optimize_loss(..., optimizer='Adam'). - function, ta

tf.contrib.distributions.LaplaceWithSoftplusScale.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.