tf.contrib.distributions.WishartFull.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.dtype

class tf.contrib.learn.monitors.GraphDump Dumps almost all tensors in the graph at every step. Note, this is very expensive, prefer PrintTensor in production.

static bool tensorflow::TensorShapeUtils::IsSquareMatrix(const TensorShape &shape)

tf.contrib.graph_editor.make_placeholder_from_dtype_and_shape(dtype, shape=None, scope=None) Create a tf.placeholder for the Graph Editor. Note that the correct graph scope must be set by the calling function. The placeholder is named using the function placeholder_name (with no tensor argument). Args: dtype: the tensor type. shape: the tensor shape (optional). scope: absolute scope within which to create the placeholder. None means that the scope of t is preserved. "" means the root scope.

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.clone(name=None, **dist_args)

tf.contrib.training.stratified_sample(tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None) Stochastically creates batches based on per-class probabilities. This method discards examples. Internally, it creates one queue to amortize the cost of disk reads, and one queue to hold the properly-proportioned batch. See stratified_sample_unknown_dist for a function that performs stratified sampling with one queue per class

tf.contrib.distributions.StudentT.parameters Dictionary of parameters used by this Distribution.

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.sparse_to_indicator(sp_input, vocab_size, name=None) Converts a SparseTensor of ids into a dense bool indicator tensor. The last dimension of sp_input.indices is discarded and replaced with the values of sp_input. If sp_input.shape = [D0, D1, ..., Dn, K], then output.shape = [D0, D1, ..., Dn, vocab_size], where output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True and False elsewhere in output. For example, if sp_input.shape = [2, 3, 4] with non-empty values: [0, 0, 0]: 0 [0,