tf.contrib.graph_editor.Transformer.__init__() Transformer constructor. The following members can be modified: transform_op_handler: handle the transformation of a tf.Operation. This handler defaults to a simple copy. assign_collections_handler: handle the assignment of collections. This handler defaults to assigning new collections created under the given name-scope. transform_external_input_handler: handle the transform of the inputs to the given subgraph. This handler defaults to creating p

tf.contrib.distributions.MultivariateNormalFull.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_s

tf.contrib.learn.DNNRegressor.weights_

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.InverseGamma.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.reduce_join(inputs, reduction_indices, keep_dims=None, separator=None, name=None) Joins a string Tensor across the given dimensions. Computes the string join across dimensions in the given string Tensor of shape [d_0, d_1, ..., d_n-1]. Returns a new Tensor created by joining the input strings with the given separator (default: empty string). Negative indices are counted backwards from the end, with -1 being equivalent to n - 1. Passing an empty reduction_indices joins all strings in linear

void tensorflow::TensorShape::AppendShape(const TensorShape &shape) Appends all the dimensions from shape.

tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: 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.Multinomial.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) + s

tf.contrib.distributions.Multinomial.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`.