TensorFlow 2.0 Crash Course
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Date: 2022-03-14
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Comments and reviews: 10
Shippy
Great tutorial! One question about the globalaveragepooling layer. After embedding we are actually taking the average of the embedding features over all the word vectors and not the average of every individual vector? Say we have 2 words in a sentence that we want to predict the sentiment of: -Very nice- -> [1,1,1,1], [2,2,2,2] -> 2 words, 2 word vectors with 4 embedding features (contexts). The correct way is to take the average over these vectors so the lower dimensional output is [1.5, 1.5, 1.5, 1.5], that we then pass to the dense layer. And the incorrect way is to output a 2 dimensional vector averaging the 2 vectors individually -> output: [1, 2]? Just averaging every word vector individually and passing every single one in a new vector doesn't make sense to me and would just throw away the context.
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Great tutorial! One question about the globalaveragepooling layer. After embedding we are actually taking the average of the embedding features over all the word vectors and not the average of every individual vector? Say we have 2 words in a sentence that we want to predict the sentiment of: -Very nice- -> [1,1,1,1], [2,2,2,2] -> 2 words, 2 word vectors with 4 embedding features (contexts). The correct way is to take the average over these vectors so the lower dimensional output is [1.5, 1.5, 1.5, 1.5], that we then pass to the dense layer. And the incorrect way is to output a 2 dimensional vector averaging the 2 vectors individually -> output: [1, 2]? Just averaging every word vector individually and passing every single one in a new vector doesn't make sense to me and would just throw away the context.
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Srinivas
Great video. Very informative. I tried to follow along and created a model in google colab. My training and testing sizes are 60,000 and 10,000 respectively. However, when the model is being trained, below where it says Epoch 1/5... it shows a total of 1875, whereas in the video tutorial it shows 60,000. Can someone please explain to me why my model is only taking 1875 images as input instead of 60,000. I have checked my training and testing sizes and they are 60,000 and 10,000 respectively. But later it shows to be 1875
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Great video. Very informative. I tried to follow along and created a model in google colab. My training and testing sizes are 60,000 and 10,000 respectively. However, when the model is being trained, below where it says Epoch 1/5... it shows a total of 1875, whereas in the video tutorial it shows 60,000. Can someone please explain to me why my model is only taking 1875 images as input instead of 60,000. I have checked my training and testing sizes and they are 60,000 and 10,000 respectively. But later it shows to be 1875
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Subrat
1:47:14 - The verbose parameter is a simple debugging tool which prints the status of epochs while the model is being trained. In the case, verbose=1 displays the epoch number with a little decoration. Please feel free to correct if any and add more info.
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1:47:14 - The verbose parameter is a simple debugging tool which prints the status of epochs while the model is being trained. In the case, verbose=1 displays the epoch number with a little decoration. Please feel free to correct if any and add more info.
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raj
37:53 its not the RGB values. They are the values obtained after using back propagation and adding biases. Its like conversion of images of T-shirt into a matrix that a computer can understand and then compare that matrix.
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37:53 its not the RGB values. They are the values obtained after using back propagation and adding biases. Its like conversion of images of T-shirt into a matrix that a computer can understand and then compare that matrix.
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Amir
Thanks for making this insightful video. One quick question, when flattening the data, shouldn't we expect 28 by 28 by 3 values for each image given that each pixel is represented by three RGB values?
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Thanks for making this insightful video. One quick question, when flattening the data, shouldn't we expect 28 by 28 by 3 values for each image given that each pixel is represented by three RGB values?
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Sarthak
ValueError: Input arrays should have the same number of samples as target arrays. Found 60000 input samples and 10000 target samples.-
I am getting this error.. an someone help me
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ValueError: Input arrays should have the same number of samples as target arrays. Found 60000 input samples and 10000 target samples.-
I am getting this error.. an someone help me
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Jonas
For everyone who as problems installing the pip package, its only for python versions up to 3.8. If you are running python 3.9 or higher install the alpha version of the pip package
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For everyone who as problems installing the pip package, its only for python versions up to 3.8. If you are running python 3.9 or higher install the alpha version of the pip package
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Theoretical
If you don't feel like going through an installation process rn, just use google colab, it has numpy, pandas, matplotlib, and tensorflow already available to use.
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If you don't feel like going through an installation process rn, just use google colab, it has numpy, pandas, matplotlib, and tensorflow already available to use.
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Aedal
We're at shella ka deulim metric abullah equals and bibbidi babbidi is why put them together ? and you've got booty babbdi boo x plus y equals y, x equals zero
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We're at shella ka deulim metric abullah equals and bibbidi babbidi is why put them together ? and you've got booty babbdi boo x plus y equals y, x equals zero
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Smit
For those facing problems installing TensorFlow and other libraries I recommend using Google Colab as it already has all the required libraries per-installed.-
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For those facing problems installing TensorFlow and other libraries I recommend using Google Colab as it already has all the required libraries per-installed.-
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