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Jonas Almeida
Public
Edited
Oct 26, 2022
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`The TF.js version used in this notebook is: ${tf.version.tfjs}`
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myTensor = tf.tensor([1, 2, 3, 10, 20, 30], [2, 3], 'int32')
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myT=tf.tensor([1,2,3,4,5,6],[2,3])
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myA = myT.array()

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myA[0][0]=9
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myA
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myT.toString()
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myT.print()
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myTensor.toString()
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`
This tensor's dimension is: ${myTensor.rank}.
The shape of this tensor is: [${myTensor.shape}].
The number of elements in this tensor is: ${myTensor.size}.
The data type of this tensor is: ${myTensor.dtype}.
`
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myTensor.array()
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myTensor.data()
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tf.memory()
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{
let numTensorsStart = tf.memory().numTensors;
let numTensorsInsideTidy, numTensorsOutsideTidy, numTensorsClean, numTensorsOrphaned;
let toKeep, toReturn, toCollect, toBatman;

toBatman = tf.tensor([1, 2, 3]);
tf.tidy(() => {
toKeep = tf.tensor([4, 5, 6]);
toReturn = tf.tensor([7, 8, 9]);
toCollect = tf.tensor([10, 11, 12]);

numTensorsInsideTidy = tf.memory().numTensors - numTensorsStart;
tf.keep(toKeep);
return toReturn;
});
numTensorsOutsideTidy = tf.memory().numTensors - numTensorsStart;

toKeep.dispose();
toReturn.dispose();
numTensorsClean = tf.memory().numTensors - numTensorsStart;
toBatman = toBatman.asType('int32'); // original Float32 is orphaned; source of memory leak
toBatman.dispose();
numTensorsOrphaned = tf.memory().numTensors - numTensorsStart;
return `
After all four tensors were created, the number of new tensors inside tidy() was: ${numTensorsInsideTidy}.
After tidy() garbage collected the toCollect tensor, the number of tensors just outside tidy() was: ${numTensorsOutsideTidy}.
After disposing off toKeep and toReturn, the number of tensors was: ${numTensorsClean}.
After orphaning toBatman, the number of tensors was: ${numTensorsOrphaned}.
`
}
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myTensorBuffer = myTensor.clone().buffer()
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myTensorBuffer.get(0, 2)
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myTensorBuffer.set(999, 0, 0)
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myModifiedTensor = myTensorBuffer.toTensor()
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myModifiedTensor.toString()
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numHoursStudied = [0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50,
2.75, 3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75, 5.00, 5.50]
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studentPassedExam = [0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
1, 0, 1, 0, 1, 1, 1, 1, 1, 1]
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predictor = tf.tensor(numHoursStudied, [numHoursStudied.length, 1])
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predictor.shape
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predictor.toString()
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ones = tf.onesLike(predictor)
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ones.toString()
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ones.shape
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x = tf.concat([ones, predictor], 1)
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x.toString()
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x.shape
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betas = [-4.1, 1.5]
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b = tf.tensor(betas, [1, betas.length])
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b.toString()
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b.shape
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logits = tf.matMul(x, b.transpose())
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logits.toString()
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yPred = tf.sigmoid(logits)
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yPred.toString()
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predictedOutcomes = yPred.data()
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xRange = tf.linspace(0, 6, 100).reshape([100, 1])
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xExtra = tf.concat([tf.onesLike(xRange), xRange], 1)
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yPredExtra = tf.sigmoid(tf.matMul(xExtra, b.transpose()))
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xRangeArray = xRange.data()
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yPredExtraArray = yPredExtra.data()
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tf = require('@tensorflow/tfjs@4.0.0/dist/tf.min.js')
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Plotly = require("https://cdn.plot.ly/plotly-2.14.0.min.js")
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