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

tf.matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None) Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. If lower is True then the strictly upper triangular part of each inner-most matrix is assumed to be zero and not accessed. If lower is False then the strictly lower triangular part of each inner-most matrix is assumed to be zero and

bool tensorflow::Tensor::IsAligned() const Returns true iff this tensor is aligned.

tf.contrib.learn.TensorFlowRNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided model_fn and training data. Note: called first time constructs the graph and initializers variables. Consecutives times it will continue training the same model. This logic follows partial_fit() interface in scikit-learn. To restart learning, create new estimator. Args: x: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of featur

tf.contrib.distributions.Mixture.__init__(cat, components, validate_args=False, allow_nan_stats=True, name='Mixture') Initialize a Mixture distribution. A Mixture is defined by a Categorical (cat, representing the mixture probabilities) and a list of Distribution objects all having matching dtype, batch shape, event shape, and continuity properties (the components). The num_classes of cat must be possible to infer at graph construction time and match len(components). Args: cat: A Categorical

tf.contrib.distributions.Binomial.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.learn.LinearClassifier.bias_

tf.contrib.learn.NanLossDuringTrainingError.__str__()

class tf.SparseTensor Represents a sparse tensor. TensorFlow represents a sparse tensor as three separate dense tensors: indices, values, and shape. In Python, the three tensors are collected into a SparseTensor class for ease of use. If you have separate indices, values, and shape tensors, wrap them in a SparseTensor object before passing to the ops below. Concretely, the sparse tensor SparseTensor(indices, values, shape) comprises the following components, where N and ndims are the number of