tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.graph_editor.assign_renamed_collections_handler(info, elem, elem_) Add the transformed elem to the (renamed) collections of elem. Args: info: Transform._Info instance. elem: the original element (tf.Tensor or tf.Operation) elem_: the transformed element

tf.sparse_placeholder(dtype, shape=None, name=None) Inserts a placeholder for a sparse tensor that will be always fed. Important: This sparse tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run(). For example: x = tf.sparse_placeholder(tf.float32) y = tf.sparse_reduce_sum(x) with tf.Session() as sess: print(sess.run(y)) # ERROR: will fail because x was not fed. indices = np.array([[3, 2,

tf.contrib.distributions.WishartCholesky.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

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.image.crop_and_resize(image, boxes, box_ind, crop_size, method=None, extrapolation_value=None, name=None) Extracts crops from the input image tensor and bilinearly resizes them (possibly with aspect ratio change) to a common output size specified by crop_size. This is more general than the crop_to_bounding_box op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change. Returns a tensor with crops from the input image at positions defined at

tf.image.resize_image_with_crop_or_pad(image, target_height, target_width) Crops and/or pads an image to a target width and height. Resizes an image to a target width and height by either centrally cropping the image or padding it evenly with zeros. If width or height is greater than the specified target_width or target_height respectively, this op centrally crops along that dimension. If width or height is smaller than the specified target_width or target_height respectively, this op centrall

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.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.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width) Pad image with zeros to the specified height and width. Adds offset_height rows of zeros on top, offset_width columns of zeros on the left, and then pads the image on the bottom and right with zeros until it has dimensions target_height, target_width. This op does nothing if offset_* is zero and the image already has size target_height by target_width. Args: image: 3-D tensor with shape [height, widt

tf.contrib.learn.monitors.BaseMonitor.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.