Refactor code for improved readability and consistency across multiple Jupyter notebooks

- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.

M2/Deep Learning/TP1 - Dense/TP1 - Bonus Starter _ Régularisation.ipynb

@@ -27,25 +27,32 @@
     "\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.mnist.load_data()\n",
     "X_train, X_valid, y_train, y_valid = train_test_split(\n",
-    "    X_train_full, y_train_full, train_size=0.8\n",
+    "    X_train_full,\n",
+    "    y_train_full,\n",
+    "    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(\n",
-    "    -1, 28, 28\n",
+    "    -1,\n",
+    "    28,\n",
+    "    28,\n",
     ")\n",
     "X_valid = scaler.transform(X_valid.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
-    "    -1, 28, 28\n",
+    "    -1,\n",
+    "    28,\n",
+    "    28,\n",
     ")\n",
     "X_test = scaler.transform(X_test.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
-    "    -1, 28, 28\n",
+    "    -1,\n",
+    "    28,\n",
+    "    28,\n",
     ")"
    ]
   },
@@ -79,13 +86,17 @@
     "        keras.layers.Input(shape=[28, 28]),\n",
     "        keras.layers.Flatten(),\n",
     "        keras.layers.Dense(\n",
-    "            256, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(0.001)\n",
+    "            256,\n",
+    "            activation=\"relu\",\n",
+    "            kernel_regularizer=keras.regularizers.l2(0.001),\n",
     "        ),\n",
     "        keras.layers.Dense(\n",
-    "            128, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(0.001)\n",
+    "            128,\n",
+    "            activation=\"relu\",\n",
+    "            kernel_regularizer=keras.regularizers.l2(0.001),\n",
     "        ),\n",
     "        keras.layers.Dense(10, activation=\"softmax\"),\n",
-    "    ]\n",
+    "    ],\n",
     ")"
    ]
   },
@@ -174,7 +185,7 @@
     "                kernel_regularizer=keras.regularizers.l2(lambda_l2),\n",
     "            ),\n",
     "            keras.layers.Dense(10, activation=\"softmax\"),\n",
-    "        ]\n",
+    "        ],\n",
     "    )\n",
     "    model.compile(\n",
     "        loss=\"sparse_categorical_crossentropy\",\n",
@@ -220,7 +231,7 @@
     "                \"lambda_l2\": lambda_l2,\n",
     "                \"history\": pd.DataFrame(history.history),\n",
     "                \"n_epochs\": n_epochs,\n",
-    "            }\n",
+    "            },\n",
     "        )"
    ]
   },

M2/Deep Learning/TP1 - Dense/TP1 - Starter.ipynb

@@ -58,7 +58,10 @@
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "X_train, X_valid, y_train, y_valid = train_test_split(\n",
-    "    X_train_full, y_train_full, test_size=0.2, random_state=42\n",
+    "    X_train_full,\n",
+    "    y_train_full,\n",
+    "    test_size=0.2,\n",
+    "    random_state=42,\n",
     ")\n",
     "print(X_train.shape, y_train.shape)\n",
     "print(X_valid.shape, y_valid.shape)"
@@ -181,7 +184,7 @@
     "        keras.layers.Dense(256, activation=\"relu\"),\n",
     "        keras.layers.Dense(128, activation=\"relu\"),\n",
     "        keras.layers.Dense(10, activation=\"softmax\"),\n",
-    "    ]\n",
+    "    ],\n",
     ")"
    ]
   },
@@ -563,7 +566,7 @@
     "            keras.layers.Dense(256, activation=\"relu\"),\n",
     "            keras.layers.Dense(128, activation=\"relu\"),\n",
     "            keras.layers.Dense(10, activation=\"softmax\"),\n",
-    "        ]\n",
+    "        ],\n",
     "    )\n",
     "    model.compile(\n",
     "        loss=\"sparse_categorical_crossentropy\",\n",
@@ -673,7 +676,10 @@
     "        plt.subplot(1, 2, 1)\n",
     "        plt.plot(history_df[\"val_loss\"], linestyle=\"--\", color=colors[_])\n",
     "        plt.plot(\n",
-    "            history_df[\"loss\"], label=f\"LR={learning_rate}\", alpha=0.5, color=colors[_]\n",
+    "            history_df[\"loss\"],\n",
+    "            label=f\"LR={learning_rate}\",\n",
+    "            alpha=0.5,\n",
+    "            color=colors[_],\n",
     "        )\n",
     "        plt.xlabel(\"Epochs\")\n",
     "        plt.ylabel(\"Loss\")\n",