tf.contrib.distributions.TransformedDistribution.mode(name='mode') Mode.

tf.contrib.learn.evaluate(graph, output_dir, checkpoint_path, eval_dict, update_op=None, global_step_tensor=None, supervisor_master='', log_every_steps=10, feed_fn=None, max_steps=None) Evaluate a model loaded from a checkpoint. Given graph, a directory to write summaries to (output_dir), a checkpoint to restore variables from, and a dict of Tensors to evaluate, run an eval loop for max_steps steps, or until an exception (generally, an end-of-input signal from a reader operation) is raised fro

tf.contrib.learn.TensorFlowRNNRegressor.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed

tf.square(x, name=None) Computes square of x element-wise. I.e., (y = x * x = x^2). 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. Has the same type as x.

tf.contrib.learn.DNNRegressor.__init__(hidden_units, feature_columns, model_dir=None, weight_column_name=None, optimizer=None, activation_fn=relu, dropout=None, gradient_clip_norm=None, enable_centered_bias=None, config=None) Initializes a DNNRegressor instance. Args: hidden_units: List of hidden units per layer. All layers are fully connected. Ex. [64, 32] means first layer has 64 nodes and second one has 32. feature_columns: An iterable containing all the feature columns used by the model.

tf.contrib.training.SequenceQueueingStateSaver.prefetch_op The op used to prefetch new data into the state saver. Running it once enqueues one new input example into the state saver. The first time this gets called, it additionally creates the prefetch_op. Subsequent calls simply return the previously created prefetch_op. It should be run in a separate thread via e.g. a QueueRunner. Returns: An Operation that performs prefetching.

tf.contrib.distributions.Bernoulli.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.distributions.DirichletMultinomial.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.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob') Probability density/mass function (depending on is_continuous). 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.