Construct a weights group entry. Args: group: The HDF5 group data, possibly nested. Returns: An array of weight groups (see `write_weigh

Create a TensorFlow V1 SavedModel for testing. Args: save_dir: directory name of where the saved model will be stored.

Test a TF V1 model with HashTable Ops. Args: save_dir: directory name of where the saved model will be stored.

Test a basic TF V2 model with functions to make sure functions are inlined. Args: save_dir: directory name of where the saved model will be sto

Test a TF V2 model with complex dtype. Args: save_dir: directory name of where the saved model will be stored.

Test a basic TF v2 model with control flow to inlined. Args: save_dir: directory name of where the saved model will be stored.

Test a TF V2 model with control flow v2. Args: save_dir: directory name of where the saved model will be stored.

Test a TF V2 model with HashTable Ops. Args: save_dir: directory name of where the saved model will be stored.

Test a TF V2 model with TensorList Ops. Args: save_dir: directory name of where the saved model will be stored.

Test a basic model with fusable conv2d. Args: save_dir: directory name of where the saved model will be stored.

Test a basic model with prelu activation. Args: save_dir: directory name of where the saved model will be stored.

* Should be used for testing purposes only.

* Should be used for testing purposes only.

* Should be used for testing purposes only.

* Should be used for testing purposes only.

* Computes absolute value element-wise: `abs(x)` * * ```js * const x = tf.tensor1d([-1, 2, -3, 4]); * * x.abs().print(); // or tf.abs(x) * ```

* Computes the absolute difference loss between two tensors. * * @param labels The ground truth output tensor, same dimensions as * 'predictions

* Computes acos of the input `tf.Tensor` element-wise: `acos(x)` * * ```js * const x = tf.tensor1d([0, 1, -1, .7]); * * x.acos().print(); // or

* Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise: * `acosh(x)` * * ```js * const x = tf.tensor1d([10, 1, 3, 5.7]); *

* Add a Feed to the FeedDict. * @param feed The new `Feed` to add. * @returns This `FeedDict`.

* Internal method to create an inbound node for the layer. * * @param inputTensors List of input tensors. * @param outputTensors List of outp

* Add losses to the layer. * * The loss may potentially be conditional on some inputs tensors, * for instance activity losses are conditional

* Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype. * * ```js * const a = tf.tensor1d([1, 2]); * const b = tf.tensor1d

* Adds a weight variable to the layer. * * @param name Name of the new weight variable. * @param shape The shape of the weight. * @param d

* Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting. * * * ```js * const a = tf.tensor1d([1, 2, 3, 4]); * const b = tf.tensor1d([1

* Computes the logical and of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepD

* Computes the logical or of elements across dimensions of a `tf.Tensor`. * * Reduces the input along the dimensions given in `axes`. Unless `keepDi

* Porting note: Renamed from __call__. * @param x Variable of which to calculate the regularization score.

* Calculate the activation function. * * @param x Tensor. * @param axis Integer, axis along which the softmax normalization is applied. *

* Calculate the activation function of log softmax: * log( exp(x_i) / sum(exp(x)) ) * * @param x Tensor. * @param axis Integer, axis along

* Calculate the activation function. * * @param x Tensor. * @returns a Tensor of the same shape as x

* Calculate the activation function. * * @param x Tensor. * @returns a Tensor of the same shape as x

* Calculate the activation function. * * @param x Tensor. * @returns a Tensor of the same shape as x

* Calculate the activation function. * * @param x Tensor. * @param alpha Scaling factor for the sigmoid function. * @returns a Tensor of t