List the files inside a dir where path is relative to tensorflowjs/. Args: path: a string path to a resource directory relative to tensorflowjs

* Load a model from local storage. * * See the documentation to `browserLocalStorage` for details on the saved * artifacts. * * @return

* Load a model from local storage. * * See the documentation to `browserLocalStorage` for details on the saved * artifacts. * * @return

* Loads all layer weights from a JSON object. * * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript / * TypeScript. T

Load a Keras Model from TensorFlow.js-format artifacts from file system Args: config_json_path: Path to the TensorFlow.js-format JSON file that

Load a Keras Model from TensorFlow.js-format artifacts from file system Args: config_json_path: Path to the TensorFlow.js-format JSON file that

* Normalizes the activation of a local neighborhood across or within * channels. * * @param x The input tensor. The 4-D input tensor is treated as

* Computes natural logarithm of the input `tf.Tensor` plus one * element-wise: `ln(1 + x)` * * ```js * const x = tf.tensor1d([1, 2, Math.E - 1]);

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

* Computes log sigmoid of the input `tf.Tensor` element-wise: * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`. * * ```js * c

* Computes the log softmax. * * ```js * const a = tf.tensor1d([1, 2, 3]); * * a.logSoftmax().print(); // or tf.logSoftmax(a) * ``` * * ```js

* Computes the log(sum(exp(elements across the reduction dimensions))). * * Reduces the input along the dimensions given in `axis`. Unless `keepDims

* Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)` * * ```js * const x = tf.tensor1d([1, 2, Math.E]); * * x.log().print(

* Returns the truth value of `a AND b` element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([false, false, true, true], 'bool');

* Returns the truth value of `NOT x` element-wise. * * ```js * const a = tf.tensor1d([false, true], 'bool'); * * a.logicalNot().print(); * ```

* Returns the truth value of `a OR b` element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([false, false, true, true], 'bool');

* Returns the truth value of `a XOR b` element-wise. Supports broadcasting. * * ```js * const a = tf.tensor1d([false, false, true, true], 'bool');

* Look up the encoded outputs of given tokens.

* This function makes the result object returned from benchmark app aligned * with target firestore collection's schema. * * @param result Individu

* Create or return the compiled generation function.

* Maps this stream through a 1-to-1 transform. * * @param transform A function mapping a stream element to a transformed * element. *

* Maps this dataset through an async 1-to-1 transform. * * ```js * const a = * tf.data.array([1, 2, 3]).mapAsync(x => new Promise(functio

* Maps this stream through an async 1-to-1 transform. * * @param transform A function mapping a stream element to a `Promise` for a * trans

Maps node names to their quantization dtypes. Given a quantization_dtype_map which maps dtypes `uint8`, `uint16`, `float16` to node patterns, e.g

* Computes the dot product of two matrices, A * B. These must be matrices. * * ```js * const a = tf.tensor2d([1, 2], [1, 2]); * const b = tf.tenso

* Computes the backprop of a 3d max pool. * * @param dy The dy error, of rank 5 of shape * [batchSize, depth, height, width, channels]. * assu

* Computes the 3D max pooling. * * ```js * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); * const result = tf.maxPool3d(x, 2, 1

* Computes the backprop of a 2D max pool. * * @param dy The dy error, of rank 4 or rank 3 of shape * [batchSize, height, width, channels]. If r

* Computes the 2D max pooling of an image with Argmax index. * The indices in argmax are flattened, so that a maximum value at position `[b, * y, x,

* Computes the 2D max pooling of an image. * * @param x The input tensor, of rank 4 or rank 3 of shape * `[batch, height, width, inChannels]`.