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Refactor code formatting and improve readability in Jupyter notebooks for TP_4 and TP_5

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- Adjusted indentation and line breaks for better clarity in function definitions and import statements.
- Standardized string quotes for consistency across the codebase.
- Enhanced readability of DataFrame creation and manipulation by breaking long lines into multiple lines.
- Cleaned up print statements and comments for improved understanding.
- Ensured consistent use of whitespace around operators and after commas.
This commit is contained in:
Arthur Danjou
2025-11-25 10:46:16 +01:00
parent 21e376de79
commit 8400c722a5
17 changed files with 11975 additions and 11713 deletions
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22
M2/Deep Learning/TP2 - Convolution/TP2 - Bonus Starter _ ResNet.ipynb
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@@ -24,20 +24,30 @@
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.set(style='whitegrid')\n",
"sns.set(style=\"whitegrid\")\n",
"\n",
"import tensorflow as tf\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from tensorflow import keras\n",
"\n",
"(X_train_full, y_train_full), (X_test, y_test) = (keras.datasets.fashion_mnist.load_data())\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, train_size=0.8)\n",
"(X_train_full, y_train_full), (X_test, y_test) = (\n",
" keras.datasets.fashion_mnist.load_data()\n",
")\n",
"X_train, X_valid, y_train, y_valid = train_test_split(\n",
" X_train_full, y_train_full, train_size=0.8\n",
")\n",
"\n",
"scaler = StandardScaler()\n",
"X_train = scaler.fit_transform(X_train.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)\n",
"X_valid = scaler.transform(X_valid.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)\n",
"X_test = scaler.transform(X_test.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)"
"X_train = scaler.fit_transform(X_train.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
" -1, 28, 28, 1\n",
")\n",
"X_valid = scaler.transform(X_valid.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
" -1, 28, 28, 1\n",
")\n",
"X_test = scaler.transform(X_test.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
" -1, 28, 28, 1\n",
")"
]
},
{