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…backend. NOTE: The PyTorch version of the model is not learning well unless the features are normalised.
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carschno
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Good start, thanks!
I would prefer to not change the X_train and X_test capitalization here because there are many other places in the lesson and in the slides which use it, and they should remain consistent.
| from sklearn.model_selection import train_test_split | ||
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| X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=0, shuffle=True, stratify=target) | ||
| x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=0, shuffle=True, stratify=target) |
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I would prefer X_train/X_test to remain consistent with the capitalization across all episodes.
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| Keras is a machine learning framework with ease of use as one of its main features. | ||
| It is part of the tensorflow python package and can be imported using `from tensorflow import keras`. | ||
| It is a standalone python package that supports multiple deep learning frameworks as backends, and it can be imported using `import keras`. |
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| It is a standalone python package that supports multiple deep learning frameworks as backends, and it can be imported using `import keras`. | |
| It is a Python package that supports multiple deep learning frameworks as backends, and it can be imported using `import keras`. |
| ::: spoiler | ||
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| ### On Linux/macOs | ||
| ### On Linux/MacOS |
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Also no need to change this. This is how "macOs" is written, at least nowadays.
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| ## Testing Keras Installation | ||
| Keras is available as a module within TensorFlow, as described in the [setup instructions](learners/setup.md#packages). | ||
| Keras is available as a standalone package, as described in the [setup instructions](learners/setup.md#packages). |
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Nice catch. Clearly some of the narrative has to be changed too!
Moving to PyTorch as the Keras backend.
Closes #602