tf.fft(input, name=None) Compute the 1-dimensional discrete Fourier Transform over the inner-most dimension of input. Args: input: A Tensor of type complex64. A complex64 tensor. name: A name for the operation (optional). Returns: A Tensor of type complex64. A complex64 tensor of the same shape as input. The inner-most dimension of input is replaced with its 1D Fourier Transform.

tf.contrib.metrics.streaming_mean_cosine_distance(predictions, labels, dim, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the cosine distance between the labels and predictions. The streaming_mean_cosine_distance function creates two local variables, total and count that are used to compute the average cosine distance between predictions and labels. This average is weighted by weights, and it is ultimately returned as mean_distance, which is an idempoten

tf.contrib.graph_editor.SubGraphView.remap(new_input_indices=None, new_output_indices=None) Remap the inputs and outputs of the subgraph. Note that this is only modifying the view: the underlying tf.Graph is not affected. Args: new_input_indices: an iterable of integers representing a mapping between the old inputs and the new ones. This mapping can be under-complete and must be without repetitions. new_output_indices: an iterable of integers representing a mapping between the old outputs an

tf.assert_less(x, y, data=None, summarize=None, message=None, name=None) Assert the condition x < y holds element-wise. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_less(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_less(x, y)], x) This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] < y[i]. If both x and y are empty

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

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

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.distribution

tf.argmin(input, dimension, name=None) Returns the index with the smallest value across dimensions of a tensor. Args: input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. dimension: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. na

tf.contrib.training.NextQueuedSequenceBatch.length The lengths of the given truncated unrolled examples. For initial iterations, for which sequence * num_unroll < length, this number is num_unroll. For the remainder, this number is between 0 and num_unroll. Returns: An integer vector of length batch_size, the lengths.