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/TP3 - Compléments/TP3 - Starter.ipynb

@@ -20,7 +20,6 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import pandas as pd\n",
     "\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -300,23 +299,35 @@
     "        [\n",
     "            keras.layers.InputLayer(shape=(32, 32, 3)),\n",
     "            keras.layers.Conv2D(\n",
-    "                filters=32, kernel_size=3, activation=\"relu\", padding=\"same\"\n",
+    "                filters=32,\n",
+    "                kernel_size=3,\n",
+    "                activation=\"relu\",\n",
+    "                padding=\"same\",\n",
     "            ),\n",
     "            keras.layers.Dropout(0.2),\n",
     "            keras.layers.Conv2D(\n",
-    "                filters=32, kernel_size=3, activation=\"relu\", padding=\"same\"\n",
+    "                filters=32,\n",
+    "                kernel_size=3,\n",
+    "                activation=\"relu\",\n",
+    "                padding=\"same\",\n",
     "            ),\n",
     "            keras.layers.MaxPooling2D(pool_size=2),\n",
     "            keras.layers.Conv2D(\n",
-    "                filters=16, kernel_size=3, activation=\"relu\", padding=\"same\"\n",
+    "                filters=16,\n",
+    "                kernel_size=3,\n",
+    "                activation=\"relu\",\n",
+    "                padding=\"same\",\n",
     "            ),\n",
     "            keras.layers.Dropout(0.2),\n",
     "            keras.layers.Conv2D(\n",
-    "                filters=16, kernel_size=3, activation=\"relu\", padding=\"same\"\n",
+    "                filters=16,\n",
+    "                kernel_size=3,\n",
+    "                activation=\"relu\",\n",
+    "                padding=\"same\",\n",
     "            ),\n",
     "            keras.layers.Flatten(),\n",
     "            keras.layers.Dense(10, activation=\"softmax\"),\n",
-    "        ]\n",
+    "        ],\n",
     "    )\n",
     "\n",
     "    return model\n",
@@ -348,7 +359,9 @@
    "outputs": [],
    "source": [
     "def compile_train(\n",
-    "    optimizer_function: str, learning_rate: float, **kwargs\n",
+    "    optimizer_function: str,\n",
+    "    learning_rate: float,\n",
+    "    **kwargs,\n",
     ") -> keras.callbacks.History:\n",
     "    model = get_model()\n",
     "    optimizer = optimizer_function(learning_rate=learning_rate)\n",
@@ -401,7 +414,10 @@
     "epochs = 5\n",
     "batch_size = 64\n",
     "history_adam = compile_train(\n",
-    "    keras.optimizers.Adam, learning_rate=0.001, epochs=epochs, batch_size=batch_size\n",
+    "    keras.optimizers.Adam,\n",
+    "    learning_rate=0.001,\n",
+    "    epochs=epochs,\n",
+    "    batch_size=batch_size,\n",
     ")"
    ]
   },
@@ -557,7 +573,10 @@
     "histories = []\n",
     "for optimizer in optimizers:\n",
     "    history = compile_train(\n",
-    "        optimizer, learning_rate=learning_rate, epochs=epochs, batch_size=batch_size\n",
+    "        optimizer,\n",
+    "        learning_rate=learning_rate,\n",
+    "        epochs=epochs,\n",
+    "        batch_size=batch_size,\n",
     "    )\n",
     "    name = optimizer.__name__\n",
     "    label = f\"{name} (lr={learning_rate:.06})\"\n",