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SephsRL_modelv2

Reinforcement Learning

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SephsRL_modelv2 / 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('sensor_input.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('Sephs_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)