def finetune_q_model():
X_train, X_valid, Y_train, Y_valid = Datasets.make_stacked_frame_data(
data_folder_path)
# Only need steering targets
Y_train = Y_train[0]
Y_valid = Y_valid[0]
model = q_categorical(input_dimension=(120, 160, 4))
# Load pretrained Q model for finetuning
model.load_weights('saved_models/robin_track_v2_highres.h5')
model.layers[-1].activation = softmax
adam = Adam(lr=1e-4) # Use a smaller learning rate for fine-tuning?
model.compile(loss='categorical_crossentropy', optimizer=adam)
print("weights Load Successfully!")
callbacks = [EarlyStopping(monitor='val_loss', patience=3)]
#ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
model.fit(X_train,
Y_train,
epochs=20,
batch_size=64,
validation_data=(X_valid, Y_valid))
timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
model.save_weights('saved_models/finetune_q_' + timestamp + '.h5',
overwrite=True)
def train_stacked_frame_simple_model():
X_train, X_valid, Y_train, Y_valid = Datasets.make_stacked_frame_data(
data_folder_path)
model = simple_categorical(input_dimension=(120, 160, 4))
callbacks = [EarlyStopping(monitor='val_loss', patience=3)]
#ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
model.fit(X_train,
Y_train,
epochs=3,
batch_size=64,
validation_data=(X_valid, Y_valid))
timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
model.save_weights('saved_models/stackedframe_simple_categorical_' +
timestamp + '.h5',
overwrite=True)
