def go(model_name, epochs=50, batch_size=128, inputs='./log/*.jpg', limit=None, aug_mult=1, aug_perc=0.0, resume=False):
print('working on model', model_name)
'''
modify config.json to select the model to train.
'''
if conf.model_selection == "nvidia_transposed_inputs":
model = models.get_nvidia_model()
elif conf.model_selection == "nvidia_standard_inputs":
model = models.get_nvidia_model2()
elif conf.model_selection == "simple":
model = models.get_simple_model()
else:
model = models.get_nvidia_model()
if resume:
print("resuming training of", model_name)
model = keras.models.load_model(model_name)
model.ch_order = 'channel_last'
transposeImages = (model.ch_order == 'channel_first')
callbacks = [
#keras.callbacks.EarlyStopping(monitor='val_loss', patience=conf.training_patience, verbose=0),
keras.callbacks.ModelCheckpoint(model_name, monitor='val_loss', save_best_only=True, verbose=0),
]
#Train on session images
train_generator, validation_generator, n_train, n_val = make_generators(inputs, limit=limit, batch_size=batch_size, aug_perc=aug_perc, transposeImages=transposeImages)
history = model.fit_generator(train_generator,
samples_per_epoch = n_train,
validation_data = validation_generator,
nb_val_samples = n_val,
nb_epoch=epochs,
verbose=1,
callbacks=callbacks)
try:
if do_plot:
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig('loss.png')
except:
print("problems with loss graph")
def go(model_name, epochs=50, inputs='./log/*.jpg', limit=None):
print('working on model', model_name)
'''
modify config.json to select the model to train.
'''
model = models.get_nvidia_model(conf.num_outputs)
'''
display layer summary and weights info
'''
#models.show_model_summary(model)
callbacks = [
keras.callbacks.EarlyStopping(monitor='val_loss',
patience=conf.training_patience,
verbose=0),
keras.callbacks.ModelCheckpoint(model_name,
monitor='val_loss',
save_best_only=True,
verbose=0),
]
batch_size = conf.training_batch_size
#Train on session images
train_generator, validation_generator, n_train, n_val = make_generators(
inputs, limit=limit, batch_size=batch_size)
if n_train == 0:
print('no training data found')
return
steps_per_epoch = n_train // batch_size
validation_steps = n_val // batch_size
print("steps_per_epoch", steps_per_epoch, "validation_steps",
validation_steps)
history = model.fit_generator(train_generator,
steps_per_epoch=steps_per_epoch,
validation_data=validation_generator,
validation_steps=validation_steps,
epochs=epochs,
verbose=1,
callbacks=callbacks)
try:
if do_plot:
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig(model_name + 'loss.png')
plt.show()
except:
print("problems with loss graph")
def go(model_name, epochs=50, inputs='./log/*.jpg', limit=None, aug_mult=1, aug_perc=0.0):
print('working on model', model_name)
'''
modify config.json to select the model to train.
'''
if conf.model_selection == "nvidia_transposed_inputs":
model = models.get_nvidia_model()
elif conf.model_selection == "nvidia_standard_inputs":
model = models.get_nvidia_model_sw()
else:
model = models.get_nvidia_model()
transposeImages = (model.ch_order == 'channel_first')
callbacks = [
keras.callbacks.EarlyStopping(monitor='val_loss', patience=conf.training_patience, verbose=0),
keras.callbacks.ModelCheckpoint(model_name + "_best", monitor='val_loss', save_best_only=True, verbose=0),
]
batch_size = conf.training_batch_size
#Train on session images
X, Y = load_dataset(inputs, limit=limit, transposeImages=transposeImages, augmentMult=aug_mult, aug_perc=aug_perc)
history = model.fit(X,Y, nb_epoch=epochs, batch_size=batch_size, validation_split=conf.training_validation_split, callbacks=callbacks)
model.save(model_name)
try:
if do_plot:
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig('loss.png')
except:
print("problems with loss graph")
def go(model_name, epochs=50, inputs="./log/*.jpg", limit=None):
print("working on model", model_name)
"""
modify config.json to select the model to train.
"""
model = models.get_nvidia_model(conf.num_outputs)
"""
display layer summary and weights info
"""
# models.show_model_summary(model)
callbacks = [
keras.callbacks.EarlyStopping(monitor="val_loss",
patience=conf.training_patience,
verbose=0),
keras.callbacks.ModelCheckpoint(model_name,
monitor="val_loss",
save_best_only=True,
verbose=0),
]
batch_size = conf.training_batch_size
# Train on session images
train_generator, validation_generator, n_train, n_val = make_generators(
inputs, limit=limit, batch_size=batch_size)
if n_train == 0:
print("no training data found")
return
steps_per_epoch = n_train // batch_size
validation_steps = n_val // batch_size
print("steps_per_epoch", steps_per_epoch, "validation_steps",
validation_steps)
history = model.fit_generator(
train_generator,
steps_per_epoch=steps_per_epoch,
validation_data=validation_generator,
validation_steps=validation_steps,
epochs=epochs,
verbose=1,
callbacks=callbacks,
)
try:
if do_plot:
# summarize history for loss
plt.plot(history.history["loss"])
plt.plot(history.history["val_loss"])
plt.title("model loss")
plt.ylabel("loss")
plt.xlabel("epoch")
plt.legend(["train", "test"], loc="upper left")
plt.savefig(model_name + "loss.png")
plt.show()
except: # noqa: E722, B001
print("problems with loss graph")
type=str,
default='model.h5',
help='Name for the output model H5 file.')
args = parser.parse_args()
driving_log_path = os.path.join(args.basedir, args.datadir, driving_log_fname)
driving_data_dir = os.path.join(args.basedir, args.datadir,
driving_data_subdir)
max_augmentations = args.augmentations
print("driving_log_path = '%s'" % driving_log_path)
print("driving_data_dir = '%s'" % driving_data_dir)
print("max_augmentations = %s" % max_augmentations)
# Load the Keras model and print its summary
model = get_nvidia_model()
print(model.summary())
# Read in the drive log csv
lines = []
with open(driving_log_path) as csvfile:
reader = csv.reader(csvfile)
header_line = next(reader)
for line in reader:
lines.append(line)
# Load the training data and augment it (by using both left, center, and right cameras and flipping images vertically)
images = []
measurements = []
for j, line in enumerate(lines):
correction = 0.2 # parameter to offset the steering for left/right camera