tf.contrib.learn.DNNClassifier.predict(*args, **kwargs) Returns predicted classes for given features. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below. Args: x: features. input_fn: Input function. If set, x must be None. batch_s

tf.contrib.distributions.StudentT.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 sample_shape(x) + self

tf.matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None) Solves one or more linear least-squares problems. matrix is a tensor of shape [..., M, N] whose inner-most 2 dimensions form M-by-N matrices. Rhs is a tensor of shape [..., M, K] whose inner-most 2 dimensions form M-by-K matrices. The computed output is a Tensor of shape [..., N, K] whose inner-most 2 dimensions form M-by-K matrices that solve the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :] in the le

tf.matrix_solve(matrix, rhs, adjoint=None, name=None) Solves systems of linear equations. Matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. Rhs is a tensor of shape [..., M, K]. The output is a tensor shape [..., M, K]. If adjoint is False then each output matrix satisfies matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]. If adjoint is True then each output matrix satisfies adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]. Args: m

tf.contrib.layers.stack(inputs, layer, stack_args, **kwargs) Builds a stack of layers by applying layer repeatedly using stack_args. stack allows you to repeatedly apply the same operation with different arguments stack_args[i]. For each application of the layer, stack creates a new scope appended with an increasing number. For example: y = stack(x, fully_connected, [32, 64, 128], scope='fc') # It is equivalent to: x = fully_connected(x, 32, scope='fc/fc_1') x = fully_connected(x, 64, scope='

tf.contrib.learn.TensorFlowRNNRegressor.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.distributions.QuantizedDistribution.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Produces the integers from 0 to limit-1 in a queue. Args: limit: An int32 scalar tensor. num_epochs: An integer (optional). If specified, range_input_producer produces each integer num_epochs times before generating an OutOfRange error. If not specified, range_input_producer can cycle through the integers an unlimited number of times. shuffle: Boolean. If true, the intege

tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.QueueBase.__init__(dtypes, shapes, names, queue_ref) Constructs a queue object from a queue reference. The two optional lists, shapes and names, must be of the same length as dtypes if provided. The values at a given index i indicate the shape and name to use for the corresponding queue component in dtypes. Args: dtypes: A list of types. The length of dtypes must equal the number of tensors in each element. shapes: Constraints on the shapes of tensors in an element: A list of shape tuples