tf.contrib.learn.BaseEstimator.predict(*args, **kwargs) Returns predictions 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: Matrix of shape [n_samples, n_features...]. Can be iterator that returns

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.__init__(dist_cls, name=None, dist_value_type=None, loss_fn=score_function, **dist_args) Construct a StochasticTensor. StochasticTensor will instantiate a distribution from dist_cls and dist_args and its value method will return the same value each time it is called. What value is returned is controlled by the dist_value_type (defaults to SampleAndReshapeValue). Some distributions' sample functions are not differentiable (e.g. a sample fr

tf.segment_mean(data, segment_ids, name=None) Computes the mean along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \frac{\sum_j data_j}{N}\) where mean is over j such that segment_ids[j] == i and N is the total number of values summed. Args: data: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. segment_ids: A Tensor. Must be one of the following

tf.contrib.distributions.Chi2.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`.

tf.contrib.distributions.Uniform.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`.

tf.contrib.learn.BaseEstimator.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None) Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training. This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want

tf.contrib.learn.LinearClassifier.weights_

tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tens

tf.cross(a, b, name=None) Compute the pairwise cross product. a and b must be the same shape; they can either be simple 3-element vectors, or any shape where the innermost dimension is 3. In the latter case, each pair of corresponding 3-element vectors is cross-multiplied independently. Args: a: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. A tensor containing 3-element vectors. b: A Tensor. Must have the same type as a. Anoth