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-2

I got this error while trying to build linear regression function using the JavaScript framework Tensorflow:

C: .regressionsnode_modules@tensorflowtfjs-nodenode_modules@tensorflowtfjs-coredistenvironment.js:147
 throw new Error("Unknown feature " + feature + ".");
 ^
Error: Unknown feature TENSORLIKE_CHECK_SHAPE_CONSISTENCY.

I think that the bug is in this function:

 at LinearRegression.processFeatures (C:.linear-regression.js:72:19)

Here is the line 72 in the "linear-regression" directory:

 features = tf.tensor(features); 

and feature variable is called from at new LinearRegression (C:.regressionslinear-regression.js:17:26)

const tf = require('@tensorflow/tfjs');
const _ = require('lodash');

/**
 * Labels = Tensor of label data
 * Features = Tensor of feature data
 * n = Number of observations
 * W (Weights) = M and B in a tensor
 * ( ' ) = transpose 
 * 
 * Slope of MSE with respect to M and B function
 * dMSE / M and B = Features' * ( (Features*W) - Labels) / n
 */

class LinearRegression 
 constructor(features, labels, options) 
 this.features = this.processFeatures(features);
 this.labels = tf.tensor(labels);
 
 this.options = Object.assign(
 learningRate: 0.1, iterations: 1000 ,
 options
 );

 this.W = tf.zeros([this.features.shape[1],1]);
 
 

 // vectorize solution
gradientDecsent() 
 const currentGuesses = this.features.matMul(this.W);
 const differences = currentGuesses.sub(this.labels)

 const slopes = this.features
 .transpose()
 .matMul(differences)
 .div(this.features.shape[0]);

 this.W = this.W.sub(slopes.mul(this.options.learningRate)); // W.shape [2 ,1] now
 

 

 train() 
 for (let i = 0; i < this.options.iterations; i++) 
 this.gradientDecsent();
 
 

 test(testFeatures, testLabels) 
 testFeatures = this.processFeatures(testFeatures); // this.features.shape [50, 1]
 testLabels = tf.tensor(testLabels); // this.labels.shape [50, 1]

 const predictions = testFeatures.matMul(this.W); //error: multiplicatiuon NOT ALLOWED = [50,1] * [2, 1] 

 //@dev: result in (-) negative number = result of if res > tot 
 const res = testLabels
 .sub(predictions)
 .pow(2)
 .sum() 
 .get();
 const tot = testLabels
 .sub(testLabels.mean())
 .pow(2)
 .sum()
 .get();

 return 1 - res / tot;
 

 processFeatures(features) 
 features = tf.tensor(features); 

 if(this.mean && this.variance) 
 return features.sub(this.mean).div(this.variance.pow(0.5));
 
 else 
 features = this.standardize(features);
 
 
 features = tf.ones([features.shape[0], 1]).concat(features, 1);
 return features;
 

 standardize(features) 
 const mean, variance = tf.moments(features, 0);
 this.mean = mean;
 this.variance = variance;

 return features.sub(this.mean).div(this.variance.pow(0.5));
 




module.exports = LinearRegression;

I can find the error in the metrics. I am guessing the error is from a multiplication of matrices syntax which is not allowed and the compiler is giving me an error.

How can I fix this error?

javascript tensorflow machine-learning

share|improve this question

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

asked Mar 8 at 5:52

miro_murasmiro_muras

14

add a comment | 

-2

I got this error while trying to build linear regression function using the JavaScript framework Tensorflow:

C: .regressionsnode_modules@tensorflowtfjs-nodenode_modules@tensorflowtfjs-coredistenvironment.js:147
 throw new Error("Unknown feature " + feature + ".");
 ^
Error: Unknown feature TENSORLIKE_CHECK_SHAPE_CONSISTENCY.

I think that the bug is in this function:

 at LinearRegression.processFeatures (C:.linear-regression.js:72:19)

Here is the line 72 in the "linear-regression" directory:

 features = tf.tensor(features); 

and feature variable is called from at new LinearRegression (C:.regressionslinear-regression.js:17:26)

const tf = require('@tensorflow/tfjs');
const _ = require('lodash');

/**
 * Labels = Tensor of label data
 * Features = Tensor of feature data
 * n = Number of observations
 * W (Weights) = M and B in a tensor
 * ( ' ) = transpose 
 * 
 * Slope of MSE with respect to M and B function
 * dMSE / M and B = Features' * ( (Features*W) - Labels) / n
 */

class LinearRegression 
 constructor(features, labels, options) 
 this.features = this.processFeatures(features);
 this.labels = tf.tensor(labels);
 
 this.options = Object.assign(
 learningRate: 0.1, iterations: 1000 ,
 options
 );

 this.W = tf.zeros([this.features.shape[1],1]);
 
 

 // vectorize solution
gradientDecsent() 
 const currentGuesses = this.features.matMul(this.W);
 const differences = currentGuesses.sub(this.labels)

 const slopes = this.features
 .transpose()
 .matMul(differences)
 .div(this.features.shape[0]);

 this.W = this.W.sub(slopes.mul(this.options.learningRate)); // W.shape [2 ,1] now
 

 

 train() 
 for (let i = 0; i < this.options.iterations; i++) 
 this.gradientDecsent();
 
 

 test(testFeatures, testLabels) 
 testFeatures = this.processFeatures(testFeatures); // this.features.shape [50, 1]
 testLabels = tf.tensor(testLabels); // this.labels.shape [50, 1]

 const predictions = testFeatures.matMul(this.W); //error: multiplicatiuon NOT ALLOWED = [50,1] * [2, 1] 

 //@dev: result in (-) negative number = result of if res > tot 
 const res = testLabels
 .sub(predictions)
 .pow(2)
 .sum() 
 .get();
 const tot = testLabels
 .sub(testLabels.mean())
 .pow(2)
 .sum()
 .get();

 return 1 - res / tot;
 

 processFeatures(features) 
 features = tf.tensor(features); 

 if(this.mean && this.variance) 
 return features.sub(this.mean).div(this.variance.pow(0.5));
 
 else 
 features = this.standardize(features);
 
 
 features = tf.ones([features.shape[0], 1]).concat(features, 1);
 return features;
 

 standardize(features) 
 const mean, variance = tf.moments(features, 0);
 this.mean = mean;
 this.variance = variance;

 return features.sub(this.mean).div(this.variance.pow(0.5));
 




module.exports = LinearRegression;

I can find the error in the metrics. I am guessing the error is from a multiplication of matrices syntax which is not allowed and the compiler is giving me an error.

How can I fix this error?

javascript tensorflow machine-learning

share|improve this question

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

asked Mar 8 at 5:52

miro_murasmiro_muras

14

add a comment | 

I got this error while trying to build linear regression function using the JavaScript framework Tensorflow:

C: .regressionsnode_modules@tensorflowtfjs-nodenode_modules@tensorflowtfjs-coredistenvironment.js:147
 throw new Error("Unknown feature " + feature + ".");
 ^
Error: Unknown feature TENSORLIKE_CHECK_SHAPE_CONSISTENCY.

I think that the bug is in this function:

 at LinearRegression.processFeatures (C:.linear-regression.js:72:19)

Here is the line 72 in the "linear-regression" directory:

 features = tf.tensor(features); 

and feature variable is called from at new LinearRegression (C:.regressionslinear-regression.js:17:26)

const tf = require('@tensorflow/tfjs');
const _ = require('lodash');

/**
 * Labels = Tensor of label data
 * Features = Tensor of feature data
 * n = Number of observations
 * W (Weights) = M and B in a tensor
 * ( ' ) = transpose 
 * 
 * Slope of MSE with respect to M and B function
 * dMSE / M and B = Features' * ( (Features*W) - Labels) / n
 */

class LinearRegression 
 constructor(features, labels, options) 
 this.features = this.processFeatures(features);
 this.labels = tf.tensor(labels);
 
 this.options = Object.assign(
 learningRate: 0.1, iterations: 1000 ,
 options
 );

 this.W = tf.zeros([this.features.shape[1],1]);
 
 

 // vectorize solution
gradientDecsent() 
 const currentGuesses = this.features.matMul(this.W);
 const differences = currentGuesses.sub(this.labels)

 const slopes = this.features
 .transpose()
 .matMul(differences)
 .div(this.features.shape[0]);

 this.W = this.W.sub(slopes.mul(this.options.learningRate)); // W.shape [2 ,1] now
 

 

 train() 
 for (let i = 0; i < this.options.iterations; i++) 
 this.gradientDecsent();
 
 

 test(testFeatures, testLabels) 
 testFeatures = this.processFeatures(testFeatures); // this.features.shape [50, 1]
 testLabels = tf.tensor(testLabels); // this.labels.shape [50, 1]

 const predictions = testFeatures.matMul(this.W); //error: multiplicatiuon NOT ALLOWED = [50,1] * [2, 1] 

 //@dev: result in (-) negative number = result of if res > tot 
 const res = testLabels
 .sub(predictions)
 .pow(2)
 .sum() 
 .get();
 const tot = testLabels
 .sub(testLabels.mean())
 .pow(2)
 .sum()
 .get();

 return 1 - res / tot;
 

 processFeatures(features) 
 features = tf.tensor(features); 

 if(this.mean && this.variance) 
 return features.sub(this.mean).div(this.variance.pow(0.5));
 
 else 
 features = this.standardize(features);
 
 
 features = tf.ones([features.shape[0], 1]).concat(features, 1);
 return features;
 

 standardize(features) 
 const mean, variance = tf.moments(features, 0);
 this.mean = mean;
 this.variance = variance;

 return features.sub(this.mean).div(this.variance.pow(0.5));
 




module.exports = LinearRegression;

I can find the error in the metrics. I am guessing the error is from a multiplication of matrices syntax which is not allowed and the compiler is giving me an error.

How can I fix this error?

javascript tensorflow machine-learning

share|improve this question

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

asked Mar 8 at 5:52

miro_murasmiro_muras

14

C: .regressionsnode_modules@tensorflowtfjs-nodenode_modules@tensorflowtfjs-coredistenvironment.js:147
 throw new Error("Unknown feature " + feature + ".");
 ^
Error: Unknown feature TENSORLIKE_CHECK_SHAPE_CONSISTENCY.

 at LinearRegression.processFeatures (C:.linear-regression.js:72:19)

 features = tf.tensor(features); 

const tf = require('@tensorflow/tfjs');
const _ = require('lodash');

/**
 * Labels = Tensor of label data
 * Features = Tensor of feature data
 * n = Number of observations
 * W (Weights) = M and B in a tensor
 * ( ' ) = transpose 
 * 
 * Slope of MSE with respect to M and B function
 * dMSE / M and B = Features' * ( (Features*W) - Labels) / n
 */

class LinearRegression 
 constructor(features, labels, options) 
 this.features = this.processFeatures(features);
 this.labels = tf.tensor(labels);
 
 this.options = Object.assign(
 learningRate: 0.1, iterations: 1000 ,
 options
 );

 this.W = tf.zeros([this.features.shape[1],1]);
 
 

 // vectorize solution
gradientDecsent() 
 const currentGuesses = this.features.matMul(this.W);
 const differences = currentGuesses.sub(this.labels)

 const slopes = this.features
 .transpose()
 .matMul(differences)
 .div(this.features.shape[0]);

 this.W = this.W.sub(slopes.mul(this.options.learningRate)); // W.shape [2 ,1] now
 

 

 train() 
 for (let i = 0; i < this.options.iterations; i++) 
 this.gradientDecsent();
 
 

 test(testFeatures, testLabels) 
 testFeatures = this.processFeatures(testFeatures); // this.features.shape [50, 1]
 testLabels = tf.tensor(testLabels); // this.labels.shape [50, 1]

 const predictions = testFeatures.matMul(this.W); //error: multiplicatiuon NOT ALLOWED = [50,1] * [2, 1] 

 //@dev: result in (-) negative number = result of if res > tot 
 const res = testLabels
 .sub(predictions)
 .pow(2)
 .sum() 
 .get();
 const tot = testLabels
 .sub(testLabels.mean())
 .pow(2)
 .sum()
 .get();

 return 1 - res / tot;
 

 processFeatures(features) 
 features = tf.tensor(features); 

 if(this.mean && this.variance) 
 return features.sub(this.mean).div(this.variance.pow(0.5));
 
 else 
 features = this.standardize(features);
 
 
 features = tf.ones([features.shape[0], 1]).concat(features, 1);
 return features;
 

 standardize(features) 
 const mean, variance = tf.moments(features, 0);
 this.mean = mean;
 this.variance = variance;

 return features.sub(this.mean).div(this.variance.pow(0.5));
 




module.exports = LinearRegression;

const tf = require('@tensorflow/tfjs');
const _ = require('lodash');

/**
 * Labels = Tensor of label data
 * Features = Tensor of feature data
 * n = Number of observations
 * W (Weights) = M and B in a tensor
 * ( ' ) = transpose 
 * 
 * Slope of MSE with respect to M and B function
 * dMSE / M and B = Features' * ( (Features*W) - Labels) / n
 */

class LinearRegression 
 constructor(features, labels, options) 
 this.features = this.processFeatures(features);
 this.labels = tf.tensor(labels);
 
 this.options = Object.assign(
 learningRate: 0.1, iterations: 1000 ,
 options
 );

 this.W = tf.zeros([this.features.shape[1],1]);
 
 

 // vectorize solution
gradientDecsent() 
 const currentGuesses = this.features.matMul(this.W);
 const differences = currentGuesses.sub(this.labels)

 const slopes = this.features
 .transpose()
 .matMul(differences)
 .div(this.features.shape[0]);

 this.W = this.W.sub(slopes.mul(this.options.learningRate)); // W.shape [2 ,1] now
 

 

 train() 
 for (let i = 0; i < this.options.iterations; i++) 
 this.gradientDecsent();
 
 

 test(testFeatures, testLabels) 
 testFeatures = this.processFeatures(testFeatures); // this.features.shape [50, 1]
 testLabels = tf.tensor(testLabels); // this.labels.shape [50, 1]

 const predictions = testFeatures.matMul(this.W); //error: multiplicatiuon NOT ALLOWED = [50,1] * [2, 1] 

 //@dev: result in (-) negative number = result of if res > tot 
 const res = testLabels
 .sub(predictions)
 .pow(2)
 .sum() 
 .get();
 const tot = testLabels
 .sub(testLabels.mean())
 .pow(2)
 .sum()
 .get();

 return 1 - res / tot;
 

 processFeatures(features) 
 features = tf.tensor(features); 

 if(this.mean && this.variance) 
 return features.sub(this.mean).div(this.variance.pow(0.5));
 
 else 
 features = this.standardize(features);
 
 
 features = tf.ones([features.shape[0], 1]).concat(features, 1);
 return features;
 

 standardize(features) 
 const mean, variance = tf.moments(features, 0);
 this.mean = mean;
 this.variance = variance;

 return features.sub(this.mean).div(this.variance.pow(0.5));
 




module.exports = LinearRegression;

const tf = require('@tensorflow/tfjs');
const _ = require('lodash');

/**
 * Labels = Tensor of label data
 * Features = Tensor of feature data
 * n = Number of observations
 * W (Weights) = M and B in a tensor
 * ( ' ) = transpose 
 * 
 * Slope of MSE with respect to M and B function
 * dMSE / M and B = Features' * ( (Features*W) - Labels) / n
 */

class LinearRegression 
 constructor(features, labels, options) 
 this.features = this.processFeatures(features);
 this.labels = tf.tensor(labels);
 
 this.options = Object.assign(
 learningRate: 0.1, iterations: 1000 ,
 options
 );

 this.W = tf.zeros([this.features.shape[1],1]);
 
 

 // vectorize solution
gradientDecsent() 
 const currentGuesses = this.features.matMul(this.W);
 const differences = currentGuesses.sub(this.labels)

 const slopes = this.features
 .transpose()
 .matMul(differences)
 .div(this.features.shape[0]);

 this.W = this.W.sub(slopes.mul(this.options.learningRate)); // W.shape [2 ,1] now
 

 

 train() 
 for (let i = 0; i < this.options.iterations; i++) 
 this.gradientDecsent();
 
 

 test(testFeatures, testLabels) 
 testFeatures = this.processFeatures(testFeatures); // this.features.shape [50, 1]
 testLabels = tf.tensor(testLabels); // this.labels.shape [50, 1]

 const predictions = testFeatures.matMul(this.W); //error: multiplicatiuon NOT ALLOWED = [50,1] * [2, 1] 

 //@dev: result in (-) negative number = result of if res > tot 
 const res = testLabels
 .sub(predictions)
 .pow(2)
 .sum() 
 .get();
 const tot = testLabels
 .sub(testLabels.mean())
 .pow(2)
 .sum()
 .get();

 return 1 - res / tot;
 

 processFeatures(features) 
 features = tf.tensor(features); 

 if(this.mean && this.variance) 
 return features.sub(this.mean).div(this.variance.pow(0.5));
 
 else 
 features = this.standardize(features);
 
 
 features = tf.ones([features.shape[0], 1]).concat(features, 1);
 return features;
 

 standardize(features) 
 const mean, variance = tf.moments(features, 0);
 this.mean = mean;
 this.variance = variance;

 return features.sub(this.mean).div(this.variance.pow(0.5));
 




module.exports = LinearRegression;

share|improve this question

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

asked Mar 8 at 5:52

miro_murasmiro_muras

14

share|improve this question

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

asked Mar 8 at 5:52

miro_murasmiro_muras

14

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

edited Mar 8 at 6:10

Pikachu the Purple Wizard

2,06361529

asked Mar 8 at 5:52

miro_murasmiro_muras

14

asked Mar 8 at 5:52

miro_murasmiro_muras

14