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/TP4 - Récurrents/TP4 - Bonus.ipynb

@@ -17,10 +17,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import keras\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
     "\n",
+    "import keras\n",
+    "\n",
     "sns.set(style=\"whitegrid\")\n",
     "\n",
     "\n",
@@ -149,7 +150,7 @@
     "        keras.layers.Embedding(\n",
     "            input_dim=vocabulary_size,\n",
     "            output_dim=dimension,\n",
-    "        )\n",
+    "        ),\n",
     "    )\n",
     "    model.add(keras.layers.SimpleRNN(128, return_sequences=False))\n",
     "    model.add(keras.layers.Dense(vocabulary_size, activation=\"softmax\"))\n",

M2/Deep Learning/TP4 - Récurrents/TP4 - Starter.ipynb

@@ -120,9 +120,7 @@
    },
    "outputs": [],
    "source": [
-    "character_to_index = {\n",
-    "    character: index for index, character in enumerate(characters)\n",
-    "}\n",
+    "character_to_index = {character: index for index, character in enumerate(characters)}\n",
     "index_to_character = dict(enumerate(characters))"
    ]
   },
@@ -317,7 +315,7 @@
     "        keras.layers.SimpleRNN(128, return_sequences=False),\n",
     "        # Ajouter une couche Dense\n",
     "        keras.layers.Dense(n_characters, activation=\"softmax\"),\n",
-    "    ]\n",
+    "    ],\n",
     ")\n",
     "\n",
     "model.summary()"
@@ -429,11 +427,14 @@
     "print(len(epochs), len(historic[\"loss\"]))\n",
     "\n",
     "for index, (metric_name, axis) in enumerate(\n",
-    "    zip([\"loss\", \"accuracy\"], [axis_1, axis_2], strict=False)\n",
+    "    zip([\"loss\", \"accuracy\"], [axis_1, axis_2], strict=False),\n",
     "):\n",
     "    color = sns.color_palette()[index]\n",
     "    axis.plot(\n",
-    "        epochs[: len(historic[metric_name])], historic[metric_name], lw=2, color=color\n",
+    "        epochs[: len(historic[metric_name])],\n",
+    "        historic[metric_name],\n",
+    "        lw=2,\n",
+    "        color=color,\n",
     "    )\n",
     "    axis.plot(\n",
     "        epochs[: len(historic[\"val_\" + metric_name])],\n",
@@ -604,7 +605,8 @@
    "outputs": [],
    "source": [
     "random_index = np.random.multinomial(\n",
-    "    1, y_test[np.random.randint(0, len(X_test) - 1)].ravel()\n",
+    "    1,\n",
+    "    y_test[np.random.randint(0, len(X_test) - 1)].ravel(),\n",
     ").argmax()"
    ]
   },