import * as losses from './losses';
|
import * as metrics from './metrics';
|
/**
|
* Binary accuracy metric function.
|
*
|
* `yTrue` and `yPred` can have 0-1 values. Example:
|
* ```js
|
* const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);
|
* const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);
|
* const accuracy = tf.metrics.binaryAccuracy(x, y);
|
* accuracy.print();
|
* ```
|
*
|
* `yTrue` and `yPred` can also have floating-number values between 0 and 1, in
|
* which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,
|
* a value >= 0.5 and <= 1.0 is interpreted as 1).
|
*
|
* Example:
|
* ```js
|
* const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);
|
* const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);
|
* const accuracy = tf.metrics.binaryAccuracy(x, y);
|
* accuracy.print();
|
* ```
|
*
|
* @param yTrue Binary Tensor of truth.
|
* @param yPred Binary Tensor of prediction.
|
* @return Accuracy Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function binaryAccuracy(yTrue, yPred) {
|
return metrics.binaryAccuracy(yTrue, yPred);
|
}
|
/**
|
* Binary crossentropy metric function.
|
*
|
* Example:
|
* ```js
|
* const x = tf.tensor2d([[0], [1], [1], [1]]);
|
* const y = tf.tensor2d([[0], [0], [0.5], [1]]);
|
* const crossentropy = tf.metrics.binaryCrossentropy(x, y);
|
* crossentropy.print();
|
* ```
|
*
|
* @param yTrue Binary Tensor of truth.
|
* @param yPred Binary Tensor of prediction, probabilities for the `1` case.
|
* @return Accuracy Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function binaryCrossentropy(yTrue, yPred) {
|
return metrics.binaryCrossentropy(yTrue, yPred);
|
}
|
/**
|
* Sparse categorical accuracy metric function.
|
*
|
* Example:
|
* ```js
|
*
|
* const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);
|
* const yPred = tf.tensor2d(
|
* [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);
|
* const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);
|
* crossentropy.print();
|
* ```
|
*
|
* @param yTrue True labels: indices.
|
* @param yPred Predicted probabilities or logits.
|
* @returns Accuracy tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function sparseCategoricalAccuracy(yTrue, yPred) {
|
return metrics.sparseCategoricalAccuracy(yTrue, yPred);
|
}
|
/**
|
* Categorical accuracy metric function.
|
*
|
* Example:
|
* ```js
|
* const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);
|
* const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);
|
* const accuracy = tf.metrics.categoricalAccuracy(x, y);
|
* accuracy.print();
|
* ```
|
*
|
* @param yTrue Binary Tensor of truth: one-hot encoding of categories.
|
* @param yPred Binary Tensor of prediction: probabilities or logits for the
|
* same categories as in `yTrue`.
|
* @return Accuracy Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function categoricalAccuracy(yTrue, yPred) {
|
return metrics.categoricalAccuracy(yTrue, yPred);
|
}
|
/**
|
* Categorical crossentropy between an output tensor and a target tensor.
|
*
|
* @param target A tensor of the same shape as `output`.
|
* @param output A tensor resulting from a softmax (unless `fromLogits` is
|
* `true`, in which case `output` is expected to be the logits).
|
* @param fromLogits Boolean, whether `output` is the result of a softmax, or is
|
* a tensor of logits.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function categoricalCrossentropy(yTrue, yPred) {
|
return metrics.categoricalCrossentropy(yTrue, yPred);
|
}
|
/**
|
* Computes the precision of the predictions with respect to the labels.
|
*
|
* Example:
|
* ```js
|
* const x = tf.tensor2d(
|
* [
|
* [0, 0, 0, 1],
|
* [0, 1, 0, 0],
|
* [0, 0, 0, 1],
|
* [1, 0, 0, 0],
|
* [0, 0, 1, 0]
|
* ]
|
* );
|
*
|
* const y = tf.tensor2d(
|
* [
|
* [0, 0, 1, 0],
|
* [0, 1, 0, 0],
|
* [0, 0, 0, 1],
|
* [0, 1, 0, 0],
|
* [0, 1, 0, 0]
|
* ]
|
* );
|
*
|
* const precision = tf.metrics.precision(x, y);
|
* precision.print();
|
* ```
|
*
|
* @param yTrue The ground truth values. Expected to contain only 0-1 values.
|
* @param yPred The predicted values. Expected to contain only 0-1 values.
|
* @return Precision Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function precision(yTrue, yPred) {
|
return metrics.precision(yTrue, yPred);
|
}
|
/**
|
* Computes the recall of the predictions with respect to the labels.
|
*
|
* Example:
|
* ```js
|
* const x = tf.tensor2d(
|
* [
|
* [0, 0, 0, 1],
|
* [0, 1, 0, 0],
|
* [0, 0, 0, 1],
|
* [1, 0, 0, 0],
|
* [0, 0, 1, 0]
|
* ]
|
* );
|
*
|
* const y = tf.tensor2d(
|
* [
|
* [0, 0, 1, 0],
|
* [0, 1, 0, 0],
|
* [0, 0, 0, 1],
|
* [0, 1, 0, 0],
|
* [0, 1, 0, 0]
|
* ]
|
* );
|
*
|
* const recall = tf.metrics.recall(x, y);
|
* recall.print();
|
* ```
|
*
|
* @param yTrue The ground truth values. Expected to contain only 0-1 values.
|
* @param yPred The predicted values. Expected to contain only 0-1 values.
|
* @return Recall Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function recall(yTrue, yPred) {
|
return metrics.recall(yTrue, yPred);
|
}
|
/**
|
* Loss or metric function: Cosine proximity.
|
*
|
* Mathematically, cosine proximity is defined as:
|
* `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,
|
* wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`
|
* represents element-wise multiplication.
|
*
|
* ```js
|
* const yTrue = tf.tensor2d([[1, 0], [1, 0]]);
|
* const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);
|
* const proximity = tf.metrics.cosineProximity(yTrue, yPred);
|
* proximity.print();
|
* ```
|
*
|
* @param yTrue Truth Tensor.
|
* @param yPred Prediction Tensor.
|
* @return Cosine proximity Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function cosineProximity(yTrue, yPred) {
|
return losses.cosineProximity(yTrue, yPred);
|
}
|
/**
|
* Loss or metric function: Mean absolute error.
|
*
|
* Mathematically, mean absolute error is defined as:
|
* `mean(abs(yPred - yTrue))`,
|
* wherein the `mean` is applied over feature dimensions.
|
*
|
* ```js
|
* const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);
|
* const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);
|
* const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);
|
* mse.print();
|
* ```
|
*
|
* @param yTrue Truth Tensor.
|
* @param yPred Prediction Tensor.
|
* @return Mean absolute error Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function meanAbsoluteError(yTrue, yPred) {
|
return losses.meanAbsoluteError(yTrue, yPred);
|
}
|
/**
|
* Loss or metric function: Mean absolute percentage error.
|
*
|
* ```js
|
* const yTrue = tf.tensor2d([[0, 1], [10, 20]]);
|
* const yPred = tf.tensor2d([[0, 1], [11, 24]]);
|
* const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);
|
* mse.print();
|
* ```
|
*
|
* Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.
|
*
|
* @param yTrue Truth Tensor.
|
* @param yPred Prediction Tensor.
|
* @return Mean absolute percentage error Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function meanAbsolutePercentageError(yTrue, yPred) {
|
return losses.meanAbsolutePercentageError(yTrue, yPred);
|
}
|
export function MAPE(yTrue, yPred) {
|
return losses.meanAbsolutePercentageError(yTrue, yPred);
|
}
|
export function mape(yTrue, yPred) {
|
return losses.meanAbsolutePercentageError(yTrue, yPred);
|
}
|
/**
|
* Loss or metric function: Mean squared error.
|
*
|
* ```js
|
* const yTrue = tf.tensor2d([[0, 1], [3, 4]]);
|
* const yPred = tf.tensor2d([[0, 1], [-3, -4]]);
|
* const mse = tf.metrics.meanSquaredError(yTrue, yPred);
|
* mse.print();
|
* ```
|
*
|
* Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.
|
*
|
* @param yTrue Truth Tensor.
|
* @param yPred Prediction Tensor.
|
* @return Mean squared error Tensor.
|
*
|
* @doc {heading: 'Metrics', namespace: 'metrics'}
|
*/
|
export function meanSquaredError(yTrue, yPred) {
|
return losses.meanSquaredError(yTrue, yPred);
|
}
|
export function MSE(yTrue, yPred) {
|
return losses.meanSquaredError(yTrue, yPred);
|
}
|
export function mse(yTrue, yPred) {
|
return losses.meanSquaredError(yTrue, yPred);
|
}
|
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Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1).\n *\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.binaryAccuracy(yTrue, yPred);\n}\n\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.binaryCrossentropy(yTrue, yPred);\n}\n\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.categoricalAccuracy(yTrue, yPred);\n}\n\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to contain only 0-1 values.\n * @param yPred The predicted values. Expected to contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.precision(yTrue, yPred);\n}\n\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to contain only 0-1 values.\n * @param yPred The predicted values. Expected to contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue: Tensor, yPred: Tensor): Tensor {\n  return metrics.recall(yTrue, yPred);\n}\n\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.cosineProximity(yTrue, yPred);\n}\n\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanAbsoluteError(yTrue, yPred);\n}\n\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n\nexport function MAPE(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n\nexport function mape(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanSquaredError(yTrue, yPred);\n}\n\nexport function MSE(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanSquaredError(yTrue, yPred);\n}\n\nexport function mse(yTrue: Tensor, yPred: Tensor): Tensor {\n  return losses.meanSquaredError(yTrue, yPred);\n}\n"]}
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