train_datafile.py

import sys
sys.path.append('src')


from nn import *


# [ ] dataset with fewer "not turn", and more "turn left"/"turn right"



# Load data
df = np.genfromtxt('__other/generated_datafile.csv', delimiter=',', usecols=(0,1,2,3,4,5,6), dtype=np.double)
np.random.shuffle(df)

X = df[:, 0:4]
y = df[:, 4:]

# Initialize model
callback = tf.keras.callbacks.EarlyStopping(monitor='accuracy', patience=400, restore_best_weights=True)

model = NeuralNetwork()
opt = tf.keras.optimizers.Adam(learning_rate=0.001)
#opt = tf.keras.optimizers.SGD(learning_rate=0.001)
model.compile(optimizer=opt, loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics = ["Accuracy"])

# Train
#model.fit(x=np.array([[0,1,0,0.1]]), y=np.array([[0, 1, 0]]))
model.fit(x=X, y=y, epochs=7500, callbacks=[callback])

print("----------------")
print("Fitting done!!!!")
print("----------------")


# Save model
#model.save_weights('__models/training_from_datafile', overwrite=True)
print("Saving model")
model.save('__models/saved_model_from_datafile')