@tensorflow/tfjs-layers
Version:
TensorFlow layers API in JavaScript
315 lines • 27.6 kB
JavaScript
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
// Layer activation functions
import * as tfc from '@tensorflow/tfjs-core';
import { serialization, tidy } from '@tensorflow/tfjs-core';
import * as K from './backend/tfjs_backend';
import { deserializeKerasObject } from './utils/generic_utils';
/**
* Base class for Activations.
*
* Special note: due to cross-language compatibility reasons, the
* static readonly className field in this family of classes must be set to
* the initialLowerCamelCase name of the activation.
*/
export class Activation extends serialization.Serializable {
getConfig() {
return {};
}
}
/**
* Exponential linear unit (ELU).
* Reference: https://arxiv.org/abs/1511.07289
*/
class Elu extends Activation {
/**
* Calculate the activation function.
*
* @param x: Input.
* @param alpha: Scaling factor the negative section.
* @return Output of the ELU activation.
*/
apply(x, alpha = 1) {
return K.elu(x, alpha);
}
}
/** @nocollapse */
Elu.className = 'elu';
export { Elu };
serialization.registerClass(Elu);
/**
* Scaled Exponential Linear Unit. (Klambauer et al., 2017).
* Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515
* Notes:
* - To be used together with the initialization "lecunNormal".
* - To be used together with the dropout variant "AlphaDropout".
*/
class Selu extends Activation {
apply(x) {
return tfc.selu(x);
}
}
/** @nocollapse */
Selu.className = 'selu';
export { Selu };
serialization.registerClass(Selu);
/**
* Rectified linear unit
*/
class Relu extends Activation {
apply(x) {
return tfc.relu(x);
}
}
/** @nocollapse */
Relu.className = 'relu';
export { Relu };
serialization.registerClass(Relu);
/**
* Rectified linear unit activation maxing out at 6.0.
*/
class Relu6 extends Activation {
apply(x) {
return tidy(() => tfc.minimum(6.0, tfc.relu(x)));
}
}
/** @nocollapse */
Relu6.className = 'relu6';
export { Relu6 };
serialization.registerClass(Relu6);
//* Linear activation (no-op) */
class Linear extends Activation {
apply(x) {
return x;
}
}
/** @nocollapse */
Linear.className = 'linear';
export { Linear };
serialization.registerClass(Linear);
/**
* Sigmoid activation function.
*/
class Sigmoid extends Activation {
apply(x) {
return tfc.sigmoid(x);
}
}
/** @nocollapse */
Sigmoid.className = 'sigmoid';
export { Sigmoid };
serialization.registerClass(Sigmoid);
/**
* Segment-wise linear approximation of sigmoid.
*/
class HardSigmoid extends Activation {
apply(x) {
return K.hardSigmoid(x);
}
}
/** @nocollapse */
HardSigmoid.className = 'hardSigmoid';
export { HardSigmoid };
serialization.registerClass(HardSigmoid);
/**
* Softplus activation function.
*/
class Softplus extends Activation {
apply(x) {
return tfc.softplus(x);
}
}
/** @nocollapse */
Softplus.className = 'softplus';
export { Softplus };
serialization.registerClass(Softplus);
/**
* Softsign activation function.
*/
class Softsign extends Activation {
apply(x) {
return K.softsign(x);
}
}
/** @nocollapse */
Softsign.className = 'softsign';
export { Softsign };
serialization.registerClass(Softsign);
/**
* Hyperbolic tangent function.
*/
class Tanh extends Activation {
apply(x) {
return tfc.tanh(x);
}
}
/** @nocollapse */
Tanh.className = 'tanh';
export { Tanh };
serialization.registerClass(Tanh);
/**
* Softmax activation function
*/
class Softmax extends Activation {
/**
* Calculate the activation function.
*
* @param x Tensor.
* @param axis Integer, axis along which the softmax normalization is applied.
* Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be
* an error.
*
* @returns a Tensor of the same shape as x
*
* @throws ValueError: In case `dim(x) < 2`.
*/
apply(x, axis = (-1)) {
return tfc.softmax(x, axis);
}
}
/** @nocollapse */
Softmax.className = 'softmax';
export { Softmax };
serialization.registerClass(Softmax);
/**
* Log softmax activation function
*/
class LogSoftmax extends Activation {
/**
* Calculate the activation function of log softmax:
* log( exp(x_i) / sum(exp(x)) )
*
* @param x Tensor.
* @param axis Integer, axis along which the softmax normalization is applied.
* Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be
* an error.
*
* @returns a Tensor of the same shape as x
*
* @throws ValueError: In case `dim(x) < 2`.
*/
apply(x, axis = (-1)) {
return tfc.logSoftmax(x, axis);
}
}
/** @nocollapse */
LogSoftmax.className = 'logSoftmax';
export { LogSoftmax };
serialization.registerClass(LogSoftmax);
/**
* Gelu activation function
*/
class Gelu extends Activation {
/**
* Calculate the activation function.
*
* @param x Tensor.
* @returns a Tensor of the same shape as x
*/
apply(x) {
return tidy(() => {
return tfc.tidy(() => {
const sqrtTwo = Math.sqrt(2);
// Compute Φ(x) using the erf function
const cdf = tfc.mul(0.5, tfc.add(1, tfc.erf(tfc.div(x, sqrtTwo))));
// Compute GELU(x) = x * Φ(x)
return tfc.mul(x, cdf);
});
});
}
}
/** @nocollapse */
Gelu.className = 'gelu';
export { Gelu };
serialization.registerClass(Gelu);
/**
* GeluNew activation function
*/
class GeluNew extends Activation {
/**
* Calculate the activation function.
*
* @param x Tensor.
* @returns a Tensor of the same shape as x
*/
apply(x) {
return tidy(() => {
return tfc.mul(0.5, tfc.mul(x, tfc.add(1, tfc.tanh(tfc.mul(tfc.sqrt(tfc.div(2, Math.PI)), tfc.add(x, tfc.mul(0.044715, tfc.pow(x, 3))))))));
});
}
}
/** @nocollapse */
GeluNew.className = 'gelu_new';
export { GeluNew };
serialization.registerClass(GeluNew);
/**
* Mish activation function
*/
class Mish extends Activation {
/**
* Calculate the activation function.
*
* @param x Tensor.
* @returns a Tensor of the same shape as x
*/
apply(x) {
return tidy(() => tfc.mul(x, tfc.tanh(tfc.softplus(x))));
}
}
/** @nocollapse */
Mish.className = 'mish';
export { Mish };
serialization.registerClass(Mish);
/**
* Swish activation function
*/
class Swish extends Activation {
/**
* Calculate the activation function.
*
* @param x Tensor.
* @param alpha Scaling factor for the sigmoid function.
* @returns a Tensor of the same shape as x
*/
apply(x, alpha = 1) {
return tidy(() => tfc.mul(tfc.sigmoid(tfc.mul(x, alpha)), x));
}
}
/** @nocollapse */
Swish.className = 'swish';
export { Swish };
serialization.registerClass(Swish);
export function serializeActivation(activation) {
return activation.getClassName();
}
export function deserializeActivation(config, customObjects = {}) {
return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');
}
export function getActivation(identifier) {
if (identifier == null) {
const config = {};
config['className'] = 'linear';
config['config'] = {};
return deserializeActivation(config);
}
if (typeof identifier === 'string') {
const config = {};
config['className'] = identifier;
config['config'] = {};
return deserializeActivation(config);
}
else if (identifier instanceof Activation) {
return identifier;
}
else {
return deserializeActivation(identifier);
}
}
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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport {serialization, Tensor, tidy} from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport {ActivationIdentifier} from './keras_format/activation_config';\nimport {deserializeKerasObject} from './utils/generic_utils';\n\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport abstract class Activation extends serialization.Serializable {\n  abstract apply(tensor: Tensor, axis?: number): Tensor;\n  getConfig(): serialization.ConfigDict {\n    return {};\n  }\n}\n\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n  /** @nocollapse */\n  static readonly className = 'elu';\n  /**\n   * Calculate the activation function.\n   *\n   * @param x: Input.\n   * @param alpha: Scaling factor the negative section.\n   * @return Output of the ELU activation.\n   */\n  apply(x: Tensor, alpha = 1): Tensor {\n    return K.elu(x, alpha);\n  }\n}\nserialization.registerClass(Elu);\n\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n  /** @nocollapse */\n  static readonly className = 'selu';\n  apply(x: Tensor): Tensor {\n    return tfc.selu(x);\n  }\n}\nserialization.registerClass(Selu);\n\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n  /** @nocollapse */\n  static readonly className = 'relu';\n  apply(x: Tensor): Tensor {\n    return tfc.relu(x);\n  }\n}\nserialization.registerClass(Relu);\n\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n  /** @nocollapse */\n  static readonly className = 'relu6';\n  apply(x: Tensor): Tensor {\n    return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n  }\n}\nserialization.registerClass(Relu6);\n\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n  /** @nocollapse */\n  static readonly className = 'linear';\n  apply(x: Tensor): Tensor {\n    return x;\n  }\n}\nserialization.registerClass(Linear);\n\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n  /** @nocollapse */\n  static readonly className = 'sigmoid';\n  apply(x: Tensor): Tensor {\n    return tfc.sigmoid(x);\n  }\n}\nserialization.registerClass(Sigmoid);\n\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n  /** @nocollapse */\n  static readonly className = 'hardSigmoid';\n  apply(x: Tensor): Tensor {\n    return K.hardSigmoid(x);\n  }\n}\nserialization.registerClass(HardSigmoid);\n\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n  /** @nocollapse */\n  static readonly className = 'softplus';\n  apply(x: Tensor): Tensor {\n    return tfc.softplus(x);\n  }\n}\nserialization.registerClass(Softplus);\n\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n  /** @nocollapse */\n  static readonly className = 'softsign';\n  apply(x: Tensor): Tensor {\n    return K.softsign(x);\n  }\n}\nserialization.registerClass(Softsign);\n\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n  /** @nocollapse */\n  static readonly className = 'tanh';\n  apply(x: Tensor): Tensor {\n    return tfc.tanh(x);\n  }\n}\nserialization.registerClass(Tanh);\n\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n  /** @nocollapse */\n  static readonly className = 'softmax';\n  /**\n   * Calculate the activation function.\n   *\n   * @param x Tensor.\n   * @param axis Integer, axis along which the softmax normalization is applied.\n   * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n   * an error.\n   *\n   * @returns a Tensor of the same shape as x\n   *\n   * @throws ValueError: In case `dim(x) < 2`.\n   */\n  apply(x: Tensor, axis: number = (-1)): Tensor {\n    return tfc.softmax(x, axis);\n  }\n}\nserialization.registerClass(Softmax);\n\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n  /** @nocollapse */\n  static readonly className = 'logSoftmax';\n  /**\n   * Calculate the activation function of log softmax:\n   * log( exp(x_i) / sum(exp(x)) )\n   *\n   * @param x Tensor.\n   * @param axis Integer, axis along which the softmax normalization is applied.\n   * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n   * an error.\n   *\n   * @returns a Tensor of the same shape as x\n   *\n   * @throws ValueError: In case `dim(x) < 2`.\n   */\n  apply(x: Tensor, axis: number = (-1)): Tensor {\n    return tfc.logSoftmax(x, axis);\n  }\n}\nserialization.registerClass(LogSoftmax);\n\n/**\n * Gelu activation function\n */\nexport class Gelu extends Activation {\n  /** @nocollapse */\n  static readonly className = 'gelu';\n  /**\n   * Calculate the activation function.\n   *\n   * @param x Tensor.\n   * @returns a Tensor of the same shape as x\n   */\n  apply(x: Tensor): Tensor {\n    return tidy(() => {\n      return tfc.tidy(() => {\n        const sqrtTwo = Math.sqrt(2);\n        // Compute Φ(x) using the erf function\n        const cdf = tfc.mul(0.5, tfc.add(1, tfc.erf(tfc.div(x, sqrtTwo))));\n        // Compute GELU(x) = x * Φ(x)\n        return tfc.mul(x, cdf);\n      });\n    });\n  }\n}\nserialization.registerClass(Gelu);\n\n/**\n * GeluNew activation function\n */\nexport class GeluNew extends Activation {\n  /** @nocollapse */\n  static readonly className = 'gelu_new';\n  /**\n   * Calculate the activation function.\n   *\n   * @param x Tensor.\n   * @returns a Tensor of the same shape as x\n   */\n  apply(x: Tensor): Tensor {\n    return tidy(() => {\n      return tfc.mul(\n        0.5,\n        tfc.mul(\n          x,\n          tfc.add(\n              1,\n              tfc.tanh(\n                tfc.mul(\n                  tfc.sqrt(tfc.div(2, Math.PI)),\n                  tfc.add(x, tfc.mul(0.044715, tfc.pow(x, 3)))\n                  )\n              )\n          )\n        )\n      );\n    });\n  }\n}\nserialization.registerClass(GeluNew);\n\n/**\n * Mish activation function\n */\nexport class Mish extends Activation {\n  /** @nocollapse */\n  static readonly className = 'mish';\n  /**\n   * Calculate the activation function.\n   *\n   * @param x Tensor.\n   * @returns a Tensor of the same shape as x\n   */\n  apply(x: Tensor): Tensor {\n    return tidy(() => tfc.mul(x, tfc.tanh(tfc.softplus(x))));\n  }\n}\nserialization.registerClass(Mish);\n\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n  /** @nocollapse */\n  static readonly className = 'swish';\n  /**\n   * Calculate the activation function.\n   *\n   * @param x Tensor.\n   * @param alpha Scaling factor for the sigmoid function.\n   * @returns a Tensor of the same shape as x\n   */\n  apply(x: Tensor, alpha = 1): Tensor {\n    return tidy(() => tfc.mul(tfc.sigmoid(tfc.mul(x, alpha)), x));\n  }\n}\nserialization.registerClass(Swish);\n\nexport function serializeActivation(activation: Activation): string {\n  return activation.getClassName();\n}\n\nexport function deserializeActivation(\n    config: serialization.ConfigDict,\n    customObjects: serialization.ConfigDict = {}): Activation {\n  return deserializeKerasObject(\n      config, serialization.SerializationMap.getMap().classNameMap,\n      customObjects, 'activation');\n}\n\nexport function getActivation(identifier: ActivationIdentifier|\n                              serialization.ConfigDict|Activation): Activation {\n  if (identifier == null) {\n    const config: serialization.ConfigDict = {};\n    config['className'] = 'linear';\n    config['config'] = {};\n    return deserializeActivation(config);\n  }\n  if (typeof identifier === 'string') {\n    const config: serialization.ConfigDict = {};\n    config['className'] = identifier;\n    config['config'] = {};\n    return deserializeActivation(config);\n  } else if (identifier instanceof Activation) {\n    return identifier;\n  } else {\n    return deserializeActivation(identifier);\n  }\n}\n"]}