tf.unsorted_segment_sum(data, segment_ids, num_segments, name=None) Computes the sum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that (output[i] = sum_{j...} data[j...] where the sum is over tuples j... such that segment_ids[j...] == i. Unlike SegmentSum, segment_ids need not be sorted and need not cover all values in the full range of valid values. If the sum is empty for a given segment ID i, output[i] = 0. num_segments

tf.unique(x, out_idx=None, name=None) Finds unique elements in a 1-D tensor. This operation returns a tensor y containing all of the unique elements of x sorted in the same order that they occur in x. This operation also returns a tensor idx the same size as x that contains the index of each value of x in the unique output y. In other words: y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1] For example: # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] y, idx = unique(x) y ==> [1, 2, 4, 7, 8] idx

tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a truncated normal distribution. The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. mean: A 0-D Tensor or Python value of type dtype. The mean

tf.truediv(x, y, name=None) Divides x / y elementwise, always producing floating point results. The same as tf.div for floating point arguments, but casts integer arguments to floating point before dividing so that the result is always floating point. This op is generated by normal x / y division in Python 3 and in Python 2.7 with from __future__ import division. If you want integer division that rounds down, use x // y or tf.floordiv. x and y must have the same numeric type. If the inputs are

tf.transpose(a, perm=None, name='transpose') Transposes a. Permutes the dimensions according to perm. The returned tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors. For example: # 'x' is [[1 2 3] # [4 5 6]] tf.transpose(x) ==> [[1 4] [2 5] [3 6]]

tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Output strings (e.g. filenames) to a queue for an input pipeline. Args: string_tensor: A 1-D string tensor with the strings to produce. num_epochs: An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the s

tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Produces a slice of each Tensor in tensor_list. Implemented using a Queue -- a QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection. Args: tensor_list: A list of Tensor objects. Every Tensor in tensor_list must have the same size in the first dimension. num_epochs: An integer (optional). If specified, slice_input_producer produces each sl

tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None) Create batches by randomly shuffling tensors. The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.train.shuffle_batch(). This version enqueues a different list of tensors in different threa

tf.train.shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None) Creates batches by randomly shuffling tensors. This function adds the following to the current Graph: A shuffling queue into which tensors from tensors are enqueued. A dequeue_many operation to create batches from the queue. A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors.

tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Produces the integers from 0 to limit-1 in a queue. Args: limit: An int32 scalar tensor. num_epochs: An integer (optional). If specified, range_input_producer produces each integer num_epochs times before generating an OutOfRange error. If not specified, range_input_producer can cycle through the integers an unlimited number of times. shuffle: Boolean. If true, the intege