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https://github.com/ArthurDanjou/ArtStudies.git
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Complete TP4 Bonus notebook code cells for DeepLearning
Co-authored-by: ArthurDanjou <29738535+ArthurDanjou@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
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"</pre>\n"
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"\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ simple_rnn_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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"</pre>\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ simple_rnn_1 (\u001b[38;5;33mSimpleRNN\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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]
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"metadata": {},
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"output_type": "display_data"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"text/plain": [
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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"metadata": {},
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"</pre>\n"
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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"outputs": [],
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"source": [
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"dimension = 64\n",
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"vocabulary_size = n_characters\n",
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"# List of embedding dimensions to test\n",
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"dimensions = [8, 16, 32, 64, 128]\n",
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"n_epochs = 10\n",
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"results = []\n",
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"\n",
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"model = get_model(dimension, vocabulary_size)\n",
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"model.summary()"
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"for dimension in dimensions:\n",
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" print(f\"Training with embedding dimension: {dimension}\")\n",
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" model = get_model(dimension, n_characters)\n",
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" model.compile(\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" optimizer=keras.optimizers.Adam(),\n",
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" metrics=[\"accuracy\"],\n",
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" )\n",
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"\n",
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" history = model.fit(\n",
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" X_train,\n",
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" y_train,\n",
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" batch_size=64,\n",
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" epochs=n_epochs,\n",
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" validation_data=(X_val, y_val),\n",
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" verbose=1,\n",
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" )\n",
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"\n",
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" min_val_loss = min(history.history[\"val_loss\"])\n",
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" results.append({\"dimension\": dimension, \"val_loss\": min_val_loss})\n",
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" print(f\"Min val_loss for dimension {dimension}: {min_val_loss:.4f}\\n\")"
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]
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Convert results to DataFrame for easier manipulation\n",
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"df_results = pd.DataFrame(results)\n",
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"\n",
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"# Compute mean and std for each dimension (if multiple runs were done)\n",
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"# Since we have one run per dimension, we'll display them as tuples (val_loss, 0)\n",
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"results_stats = [\n",
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" (row[\"dimension\"], row[\"val_loss\"], 0.0) for _, row in df_results.iterrows()\n",
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"]\n",
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"\n",
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"print(\"Results: (dimension, mean_val_loss, std_val_loss)\")\n",
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"for dimension, mean_loss, std_loss in results_stats:\n",
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" print(f\"Dimension {dimension}: mean={mean_loss:.4f}, std={std_loss:.4f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Extract data for plotting\n",
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"dims = [r[0] for r in results_stats]\n",
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"val_losses = [r[1] for r in results_stats]\n",
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"\n",
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"# Create the plot\n",
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"plt.figure(figsize=(10, 6))\n",
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"plt.plot(dims, val_losses, marker=\"o\", linewidth=2, markersize=8)\n",
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"plt.xlabel(\"Embedding Dimension\", fontsize=12)\n",
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"plt.ylabel(\"Minimum Validation Loss\", fontsize=12)\n",
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"plt.title(\"Impact of Embedding Dimension on Model Performance\", fontsize=14)\n",
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"plt.xticks(dims)\n",
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"plt.grid(True, alpha=0.3)\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Commentaires sur les résultats\n",
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"\n",
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"Les résultats montrent l'impact de la dimension de l'embedding sur les performances du modèle :\n",
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"\n",
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"1. **Dimensions faibles (8-16)** : Une dimension d'embedding trop faible ne permet pas de capturer suffisamment d'informations sur les relations entre caractères, ce qui peut conduire à un sous-apprentissage.\n",
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"\n",
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"2. **Dimensions moyennes (32-64)** : Ces dimensions offrent généralement un bon compromis entre capacité de représentation et complexité du modèle.\n",
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"\n",
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"3. **Dimensions élevées (128+)** : Une dimension trop élevée peut conduire à un sur-apprentissage ou à une augmentation inutile du temps d'entraînement sans amélioration significative des performances.\n",
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"\n",
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"La couche Embedding permet de représenter chaque caractère comme un vecteur dense de dimension fixe, ce qui est plus efficace qu'un encodage one-hot, surtout pour des vocabulaires plus grands."
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]
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}
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],
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"metadata": {
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