Create Train_n_save.py

Train_n_save.py

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+import tensorflow as tf
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
+import pandas as pd
+from neural_network import create_neural_network_model  # Import your custom model function
+
+# Load training data from CSV using pandas
+training_data = pd.read_csv('training_data.csv')
+
+# Assuming your training data has columns like 'features', 'labels', and 'rewards'
+train_inputs = training_data['features']  # Replace 'features' with the actual column name
+train_labels = training_data['labels']    # Replace 'labels' with the actual column name
+train_rewards = training_data['rewards']  # Replace 'rewards' with the actual column name
+
+# Define your model architecture
+seq_length = 128  # Example sequence length
+d_model = 512     # Example dimension
+action_space_size = 10  # Example action space size
+
+model = create_neural_network_model(seq_length, d_model, action_space_size)
+
+# Define loss functions and metrics
+losses = {'Output': 'categorical_crossentropy', 'Reward': 'mean_squared_error'}
+metrics = {'Output': 'accuracy'}
+
+# Compile the model
+opt = tf.keras.optimizers.Adam(learning_rate=0.001)
+model.compile(optimizer=opt, loss=losses, metrics=metrics)
+
+# Define callbacks (e.g., ModelCheckpoint, EarlyStopping) as needed
+callbacks = [
+    ModelCheckpoint(filepath='model_weights.h5', save_best_only=True),
+    EarlyStopping(patience=5, restore_best_weights=True)
+]
+
+# Train the model
+history = model.fit(
+    x=train_inputs,  # Your training data
+    y={'Output': train_labels, 'Reward': train_rewards},  # Your training labels and rewards
+    batch_size=32,
+    epochs=50,
+    callbacks=callbacks
+)
+
+# Save the trained model
+model.save('custom_model.h5')
+
+# You can also save training history for analysis and plotting
+import pickle
+with open('training_history.pickle', 'wb') as history_file:
+    pickle.dump(history.history, history_file)