tf.contrib.framework.assign_from_values(var_names_to_values) Creates an assignment operation from a given mapping. This function provides a mechanism for performing assignment of variables to values in a way that does not fill the graph with large assignment values. Args: var_names_to_values: A map from variable names to values. Returns: assign_op: An Operation that assigns each of the given variables to the requested values. feed_dict: The feed dictionary to use when evaluating assign_op

tf.contrib.framework.variable(*args, **kwargs) Gets an existing variable with these parameters or creates a new one. Args: name: the name of the new or existing variable. shape: shape of the new or existing variable. dtype: type of the new or existing variable (defaults to DT_FLOAT). initializer: initializer for the variable if one is created. regularizer: a (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection GraphK

tf.image.resize_images(images, size, method=0, align_corners=False) Resize images to size using the specified method. Resized images will be distorted if their original aspect ratio is not the same as size. To avoid distortions see resize_image_with_crop_or_pad. method can be one of: ResizeMethod.BILINEAR: Bilinear interpolation. ResizeMethod.NEAREST_NEIGHBOR: Nearest neighbor interpolation. ResizeMethod.BICUBIC: Bicubic interpolation. ResizeMethod.AREA: Area interpolation. Args: ima

tf.contrib.learn.monitors.ValidationMonitor.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

string tensorflow::PartialTensorShapeUtils::PartialShapeListString(const gtl::ArraySlice< PartialTensorShape > &shapes)

tf.sparse_reorder(sp_input, name=None) Reorders a SparseTensor into the canonical, row-major ordering. Note that by convention, all sparse ops preserve the canonical ordering along increasing dimension number. The only time ordering can be violated is during manual manipulation of the indices and values to add entries. Reordering does not affect the shape of the SparseTensor. For example, if sp_input has shape [4, 5] and indices / values: [0, 3]: b [0, 1]: a [3, 1]: d [2, 0]: c then the outpu

tf.SparseTensor.__div__(sp_x, y) Component-wise divides a SparseTensor by a dense Tensor. Limitation: this Op only broadcasts the dense side to the sparse side, but not the other direction. Args: sp_indices: A Tensor of type int64. 2-D. N x R matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. sp_values: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8,

tf.contrib.distributions.Gamma.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.ba

tf.contrib.distributions.Dirichlet.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.contrib.graph_editor.get_within_boundary_ops(ops, seed_ops, boundary_ops=(), inclusive=True, control_inputs=False, control_outputs=None, control_ios=None) Return all the tf.Operation within the given boundary. Args: ops: an object convertible to a list of tf.Operation. those ops define the set in which to perform the operation (if a tf.Graph is given, it will be converted to the list of all its operations). seed_ops: the operations from which to start expanding. boundary_ops: the ops for