NewSession creates a new execution session with the associated graph. options may be nil to use the default options.

Returns the value of the attr of this op with the given `name`. Args: name: The name of the attr to fetch. Returns: The value of

A wrapper around CommandHandlerRegistry.register_command_handler(). In addition to calling the wrapped register_command_handler() method, this

Make device, colocation and dependencies stacks thread-local. Device, colocation and dependencies stacks are not thread-local be default. If

static */

Device returns a specification of the device on which this operation will be executed, or the empty string if there is no such specification.

Returns the InferenceContext for 'node', if present.

Set user memory primitive using specified dimensions, memory format tag and data_buffer. Function automatically uses element data type by using input

Add the output of another operation as the next input of the operation being built. @param input {@link Output} supposed to be the input of the opera

Returns a `tf.train.ClusterDef` protocol buffer based on this cluster.

r"""Converts two real numbers to a complex number. Given a tensor `real` representing the real part of a complex number, and a tensor `imag` repr

Creates a `Dataset` by zipping together the given datasets. This method has similar semantics to the built-in `zip()` function in Python, wit

Wrapper for the square function to reduce verbosity.

Invoke make to build tflite C++ sources. Build dependencies: apt-get install swig libjpeg-dev zlib1g-dev python3-dev python3-nump

Returns a `tf.Operation` to write a dataset to a file. Args: dataset: a `tf.data.Dataset` whose elements are to be written to a file R

Shape function for ops that output a single scalar value.

Returns a Cholesky factor as a `LinearOperator`. Given `A` representing this `LinearOperator`, if `A` is positive definite self-adjoint, retu

Functional interface for the densely-connected layer. This layer implements the operation: `outputs = activation(inputs * kernel + bias)` where

Maps `map_func` across this dataset and flattens the result. Use `flat_map` if you want to make sure that the order of your dataset stays the

Instantiates a `Loss` from its config (output of `get_config()`). Args: config: Output of `get_config()`. Returns: A `Loss`

Main Dense-Tensor-In-Dense-Tensor-Out (DTIDTO) map method. Should be implemented by subclasses. Args: input_tensor: The dense input ten

Convenience wrapper to create Numpy arrays with a np.int32 dtype.

Get the tensor value from for a debug-dumped tensor. The tensor may be dumped multiple times in the dump root directory, so a list of tensors

`TensorShape` of this `LinearOperator`. If this operator acts like the batch matrix `A` with `A.shape = [B1,...,Bb, M, N]`, then this returns

Returns a shape based on `self` with the given rank. This method promotes a completely unknown shape to one with a known rank. Args:

Sets data formats to convert from and to for specified device type.

Constructs a new instance of server. @param serverDef Server definition specified as a serialized <a href="https://github.com/tensorflow/tensorfl

Returns the value of the fuzz factor used in numeric expressions. Returns: A float. Example: ```python keras.backend.epsilon() >>>1e-0

Gets model input details. Returns: A list of input details.

Returns a `tf.Operation` that initializes this iterator on `dataset`. Args: dataset: A `Dataset` with compatible structure to this iterator

Returns one more than the maximum id assigned to any node.

Standard deviation. Standard deviation is defined as, ```none stddev = E[(X - E[X])**2]**0.5 ``` where `X` is the random variab

Track a Layer in this Network. `Network` requires that all `Layer`s used in `call()` be tracked so that the `Network` can export a complete l

Applies the given transform(s) to the image(s). Args: images: A tensor of shape (num_images, num_rows, num_columns, num_channels) (NHWC)