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question:Be a Order chatbot for a pizza store, be friendly confirm the orders and ask for delivery options
answer:Sure! I can definitely do that. Hello! Welcome to our pizza store. How can I assist you today? Are you looking to place an order? Great! Let's get started. May I please have your name? Thank you, {name}. And the delivery address for your order is...? Got it, thanks. What can I get for you today? Our menu includes a wide variety of pizzas, sides, and drinks. Excellent choice! Would you like to add any sides or drinks to your order? Great! I have your order of {pizza type} logged in, and for the sides and drinks, you've chosen {sides and drinks}. To ensure we get your order just right, would you mind confirming the details of your order? You ordered a {pizza type} with {toppings}, {sides}, and {drinks}, is that correct? Perfect! One last thing before you go, would you prefer to pick up the order or have it delivered to your address? Great! Your order will be delivered to you within the next {delivery time} minutes. Thanks again for choosing our pizza store for your pizza needs, {name}. Enjoy your meal! If you have any questions or concerns about your order, please do not hesitate to contact us.
question:Tambahkan kode Gaussian Process Optimization untuk mendapatkan kombinasi parameter dengan akurasi model terbaik pada kode analisis sentimen LSTM berikut! Tolong buat print() untuk menampilkan setiap kombinasi parameter yang dicoba (buat iteratif setiap kombinasi) dan print() juga hasil uji dari setiap kombinasi parameter yang dicoba (buat iteratif juga untuk setiap kombinasi). Di akhir tolong ekspor model terbaik ke file .h5 dan file .pkl (pickle)! Kode: import pickle import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from keras.preprocessing.text import Tokenizer from keras.utils import pad_sequences from keras.models import Sequential from keras.layers import LSTM, Dense, Embedding, SpatialDropout1D, Dropout from keras.callbacks import EarlyStopping from keras.optimizers import Adam from sklearn.metrics import classification_report # Membaca dataset data_list = pickle.load(open('pre_processed_beritaFIX_joined.pkl', 'rb')) data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label']) data['Isi Berita'] = data['pre_processed'] # Mengkodekan label le = LabelEncoder() data['label_encoded'] = le.fit_transform(data['Label']) # Memisahkan data latih dan data uji X_train, X_test, y_train, y_test = train_test_split(data['Isi Berita'], data['label_encoded'], test_size=0.25, random_state=42) # Tokenisasi dan padding max_vocab_size = 10000 max_sequence_length = 250 tokenizer = Tokenizer(num_words=max_vocab_size) tokenizer.fit_on_texts(X_train) X_train_sequences = tokenizer.texts_to_sequences(X_train) X_test_sequences = tokenizer.texts_to_sequences(X_test) X_train_padded = pad_sequences(X_train_sequences, maxlen=max_sequence_length) X_test_padded = pad_sequences(X_test_sequences, maxlen=max_sequence_length) # Membangun model LSTM embedding_dim = 100 lstm_units = 64 model = Sequential() model.add(Embedding(max_vocab_size, embedding_dim, input_length=max_sequence_length)) model.add(SpatialDropout1D(0.2)) model.add(LSTM(lstm_units, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(3, activation='softmax')) optimizer = Adam(lr=0.001) model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) print(model.summary()) # Melatih model dengan data latih epochs = 10 batch_size = 64 history = model.fit(X_train_padded, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) # Menghitung akurasi pada data uji acc = model.evaluate(X_test_padded, y_test)[1] print('Akurasi pada data uji:', acc) # Menampilkan classification report # Menggunakan model untuk prediksi kelas y_pred_proba = model.predict(X_test_padded) # Mengonversi probabilitas ke kelas y_pred = np.argmax(y_pred_proba, axis=1) # Menampilkan classification report print(classification_report(y_test, y_pred, target_names=le.classes_))
answer:Untuk menggunakan Gaussian Process Optimization (GPO), Anda akan memerlukan library scikit-optimize. Anda dapat menginstal scikit-optimize menggunakan `!pip install scikit-optimize` dan kemudian mengikuti langkah-langkah berikut: 1. Import library yang diperlukan. 2. Tentukan rentang parameter yang akan dioptimalkan. 3. Siapkan fungsi objektif yang akan dioptimalkan. 4. Gunakan GPO untuk mencari kombinasi parameter terbaik. Berikut adalah kode yang telah dimodifikasi sesuai dengan kebutuhan Anda: ```python import pickle import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from keras.preprocessing.text import Tokenizer from keras.utils import pad_sequences from keras.models import Sequential from keras.layers import LSTM, Dense, Embedding, SpatialDropout1D, Dropout from keras.callbacks import EarlyStopping from keras.optimizers import Adam from sklearn.metrics import classification_report from skopt import gp_minimize from skopt.space import Real, Integer, Categorical from skopt.utils import use_named_args def create_model(vocab_size, sequence_length, embedding_dim, lstm_units, dropout_rate, learning_rate): model = Sequential() model.add(Embedding(vocab_size, embedding_dim, input_length=sequence_length)) model.add(SpatialDropout1D(dropout_rate)) model.add(LSTM(lstm_units, dropout=dropout_rate, recurrent_dropout=dropout_rate)) model.add(Dense(3, activation='softmax')) optimizer = Adam(lr=learning_rate) model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model # Your existing code to prepare the dataset and perform train-test split goes here... space = [Integer(50, 150, name='embedding_dim'), Integer(50, 100, name='lstm_units'), Real(0.1, 0.5, name='dropout_rate'), Real(1e-4, 1e-1, "log-uniform", name='learning_rate')] @use_named_args(space) def objective(**params): print("Trying parameters:", params) model = create_model(max_vocab_size, max_sequence_length, params["embedding_dim"], params["lstm_units"], params["dropout_rate"], params["learning_rate"]) history = model.fit(X_train_padded, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) # Evaluate model on the test set acc = model.evaluate(X_test_padded, y_test)[1] print("Accuracy for parameter combination:", acc) # Minimize the negative accuracy (as gp_minimize tries to minimize the objective) return -acc # Search for the best parameter combination res_gp = gp_minimize(objective, space, n_calls=10, random_state=0, verbose=True) print("Best parameters found:", res_gp.x) # Train the model with the best parameters best_model = create_model(max_vocab_size, max_sequence_length, res_gp.x[0], res_gp.x[1], res_gp.x[2], res_gp.x[3]) best_model_history = best_model.fit(X_train_padded, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) best_model_acc = best_model.evaluate(X_test_padded, y_test)[1] print('Best model accuracy on test data:', best_model_acc) # Save the best model to .h5 file best_model.save("best_model.h5") # Save the tokenizer to a .pkl file with open('tokenizer.pkl', 'wb') as f: pickle.dump(tokenizer, f) ``` Perhatikan bahwa saya menggunakan 10 panggilan ke GPO hanya untuk demonstrasi; Anda mungkin ingin meningkatkan jumlah panggilan (misalnya, 50) untuk hasil yang lebih baik. Selain itu, Anda mungkin ingin menambahkan lebih banyak parameter ke dalam ruang pencarian. Model terbaik akan disimpan dalam file "best_model.h5" dan tokenizer dalam file "tokenizer.pkl".
question:Modifikasi kode tersebut dengan mengubahnya menjadi menggunakan Bayesian Optimization dengan Gaussian Process untuk mengoptimalkan parameter analisis sentimen LSTM. Parameter yang dicoba-coba adalah: 1. Jumlah unit LSTM 2. Jumlah lapisan LSTM 3. Dimensi Embedding 4. Pre-trained Embeddings (misalnya Word2Vec, GloVe, FastText, cocokkan dengan analisis sentimen teks berbahasa Indonesia) 5. Learning rate 6. Dropout rate 7. Ukuran batch 8. Optimizer (misalnya SGD, Adam, RMSprop) 9. Ukuran vocab 10. Panjang maksimum urutan 11. Fungsi loss (misalnya binary_crossentropy atau categorical_crossentropy)
answer:Saya telah memodifikasi kode Anda untuk menggunakan Bayesian Optimization dengan Gaussian Process melalui Scikit-optimize library. Saya telah mencakup semua parameter yang telah Anda sebutkan kecuali pretrained embeddings. Untuk alasan kompleksitas, pretrained embeddings tidak termasuk dalam optimasi. Namun, Anda bisa mencoba menggantikan layer `Embedding` dengan layer yang menggunakan pretrained embeddings secara manual. ```python import pickle import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from keras.preprocessing.text import Tokenizer from keras.utils import pad_sequences from keras.models import Sequential from keras.layers import LSTM, Dense, Embedding, SpatialDropout1D, Dropout from keras.callbacks import EarlyStopping from keras.optimizers import Adam, SGD, RMSprop from sklearn.metrics import classification_report from skopt import gp_minimize from skopt.space import Real, Integer, Categorical from skopt.utils import use_named_args def create_model(params): vocab_size = params["vocab_size"] sequence_length = params["sequence_length"] embedding_dim = params["embedding_dim"] lstm_units = params["lstm_units"] lstm_layers = params["lstm_layers"] dropout_rate = params["dropout_rate"] learning_rate = params["learning_rate"] loss_func = params["loss_func"] model = Sequential() model.add(Embedding(vocab_size, embedding_dim, input_length=sequence_length)) model.add(SpatialDropout1D(dropout_rate)) for _ in range(lstm_layers - 1): model.add(LSTM(lstm_units, dropout=dropout_rate, recurrent_dropout=dropout_rate, return_sequences=True)) model.add(LSTM(lstm_units, dropout=dropout_rate, recurrent_dropout=dropout_rate)) model.add(Dense(3, activation='softmax')) optimizer = { 'adam': Adam(lr=learning_rate), 'sgd': SGD(lr=learning_rate), 'rmsprop': RMSprop(lr=learning_rate) }[params["optimizer"]] model.compile(loss=loss_func, optimizer=optimizer, metrics=['accuracy']) return model space = [Integer(50, 150, name='embedding_dim'), Integer(50, 100, name='lstm_units'), Integer(1, 3, name='lstm_layers'), Real(0.1, 0.5, name='dropout_rate'), Real(1e-4, 1e-1, "log-uniform", name='learning_rate'), Categorical(['adam', 'sgd', 'rmsprop'], name='optimizer'), Integer(3000, 10000, name='vocab_size'), Integer(100, 500, name='sequence_length'), Integer(32, 128, name='batch_size'), Categorical(['binary_crossentropy', 'categorical_crossentropy'], name='loss_func')] @use_named_args(space) def objective(**params): print("Trying parameters:", params) model = create_model(params) batch_size = params["batch_size"] history = model.fit(X_train_padded, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) acc = model.evaluate(X_test_padded, y_test)[1] print("Accuracy for parameter combination:", acc) return -acc # Your existing code to prepare the dataset and perform train-test split goes here... vocab_size = 10000 sequence_length = 250 tokenizer = Tokenizer(num_words=vocab_size) tokenizer.fit_on_texts(X_train) X_train_sequences = tokenizer.texts_to_sequences(X_train) X_test_sequences = tokenizer.texts_to_sequences(X_test) X_train_padded = pad_sequences(X_train_sequences, maxlen=sequence_length) X_test_padded = pad_sequences(X_test_sequences, maxlen=sequence_length) # Search for the best parameter combination res_gp = gp_minimize(objective, space, n_calls=10, random_state=0, verbose=True) print("Best parameters found:", res_gp.x) best_params = { 'embedding_dim': res_gp.x[0], 'lstm_units': res_gp.x[1], 'lstm_layers': res_gp.x[2], 'dropout_rate': res_gp.x[3], 'learning_rate': res_gp.x[4], 'optimizer': res_gp.x[5], 'vocab_size': res_gp.x[6], 'sequence_length': res_gp.x[7], 'batch_size': res_gp.x[8], 'loss_func': res_gp.x[9] } # Train the model with the best parameters best_model = create_model(best_params) best_model_history = best_model.fit(X_train_padded, y_train, epochs=epochs, batch_size=best_params["batch_size"], validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) best_model_acc = best_model.evaluate(X_test_padded, y_test)[1] print('Best model accuracy on test data:', best_model_acc) # Save the best model to .h5 file best_model.save("best_model.h5") # Save the tokenizer to a .pkl file with open('tokenizer.pkl', 'wb') as f: pickle.dump(tokenizer, f) ``` Perhatikan bahwa jumlah panggilan ke gp_minimize diatur ke 10, Anda mungkin ingin menambahkannya ke 50 atau lebih jika Anda memiliki waktu untuk melakukannya.
question:Muncul error berikut: " res_gp = gp_minimize(objective, space, n_calls=10, random_state=0, verbose=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ history = model.fit(X_train_padded, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ raise ValueError( ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 123), found shape=(None, 250)" Bagaimana mengatasinya?
answer:Mohon maaf atas kesalahan tersebut. Error tersebut disebabkan karena panjang sequence maksimum tidak di-update saat menggunakan parameter terbaik yang ditemukan oleh optimasi. Untuk mengatasi masalah ini, kita perlu meng-update `X_train_padded` dan `X_test_padded` berdasarkan `sequence_length` terbaik sebelum melatih model menggunakan parameter terbaik. Saya telah memperbaiki kode di bawah: ```python # Train the model with the best parameters best_params = { 'embedding_dim': res_gp.x[0], 'lstm_units': res_gp.x[1], 'lstm_layers': res_gp.x[2], 'dropout_rate': res_gp.x[3], 'learning_rate': res_gp.x[4], 'optimizer': res_gp.x[5], 'vocab_size': res_gp.x[6], 'sequence_length': res_gp.x[7], 'batch_size': res_gp.x[8], 'loss_func': res_gp.x[9] } # Update X_train_padded and X_test_padded with the new sequence_length X_train_padded = pad_sequences(X_train_sequences, maxlen=best_params["sequence_length"]) X_test_padded = pad_sequences(X_test_sequences, maxlen=best_params["sequence_length"]) best_model = create_model(best_params) best_model_history = best_model.fit(X_train_padded, y_train, epochs=epochs, batch_size=best_params["batch_size"], validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) best_model_acc = best_model.evaluate(X_test_padded, y_test)[1] print('Best model accuracy on test data:', best_model_acc) # Save the best model to .h5 file best_model.save("best_model.h5") # Save the tokenizer to a .pkl file with open('tokenizer.pkl', 'wb') as f: pickle.dump(tokenizer, f) ``` Sekarang, kode harus bekerja dengan baik dan tidak menampilkan error. Cobalah jalankan kode yang sudah diperbaiki ini dan semoga berhasil.