tf.contrib.learn.BaseEstimator.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 altogeth

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.InverseGammaWithSoftplusAlphaBeta.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`.

bool tensorflow::Tensor::CopyFrom(const Tensor &other, const TensorShape &shape) TF_MUST_USE_RESULT Copy the other tensor into this tensor and reshape it. This tensor shares other's underlying storage. Returns true iff other.shape() has the same number of elements of the given shape.

tf.contrib.losses.sparse_softmax_cross_entropy(logits, labels, weight=1.0, scope=None) Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the loss weights apply to each corresponding sample. Args: logits: [batch_size, num_classes] logits outputs of the network . labels: [batch_size, 1] or [batch_size] tar