def validate(data_type,
model,
seq_length=40,
saved_model=None,
class_limit=None,
image_shape=None):
batch_size = 32
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length, class_limit=class_limit)
else:
data = DataSet(seq_length=seq_length,
class_limit=class_limit,
image_shape=image_shape)
val_generator = data.frame_generator(batch_size, 'test', data_type)
# Get the model.
rm = ResearchModels(len(data.classes), model, seq_length, saved_model)
# Evaluate!
results = rm.model.evaluate_generator(generator=val_generator,
val_samples=3200)
print(results)
print(rm.model.metrics_names)
def train(data_type, seq_length, model, saved_model=None,
class_limit=None, image_shape=None,
load_to_memory=False, batch_size=32, nb_epoch=100):
# Helper: Save the model.
checkpointer = ModelCheckpoint(
filepath=os.path.join('data', 'checkpoints', model + '-' + data_type + \
'.{epoch:03d}-{val_accuracy:.3f}.hdf5'),
monitor='val_accuracy',
verbose=1,
save_best_only=True)
# Helper: TensorBoard
tb = TensorBoard(log_dir=os.path.join('data', 'logs', model))
# Helper: Stop when we stop learning.
early_stopper = EarlyStopping(patience=50, monitor='val_accuracy')
# Helper: Save results.
timestamp = time.time()
csv_logger = CSVLogger(os.path.join('data', 'logs', model + '-' + 'training-' + \
str(timestamp) + '.log'))
# Get the data and process it.
if image_shape is None:
data = DataSet(
seq_length=seq_length,
class_limit=class_limit
)
else:
data = DataSet(
seq_length=seq_length,
class_limit=class_limit,
image_shape=image_shape
)
# Get samples per epoch.
# Multiply by 0.7 to attempt to guess how much of data.data is the train set.
steps_per_epoch = (len(data.data) * 0.7) // batch_size
if load_to_memory:
# Get data.
X, y = data.get_all_sequences_in_memory('train', data_type)
X_test, y_test = data.get_all_sequences_in_memory('test', data_type)
else:
# Get generators.
generator = data.frame_generator(batch_size, 'train', data_type)
val_generator = data.frame_generator(batch_size, 'test', data_type)
# Get the model.
rm = ResearchModels(len(data.classes), model, seq_length, saved_model)
# Fit!
if load_to_memory:
# Use standard fit.
rm.model.fit(
X,
y,
batch_size=batch_size,
validation_data=(X_test, y_test),
verbose=1,
# callbacks=[tb, early_stopper, csv_logger],
callbacks=[early_stopper, csv_logger, checkpointer],
epochs=nb_epoch)
else:
# Use fit generator.
rm.model.fit_generator(
generator=generator,
steps_per_epoch=steps_per_epoch,
epochs=nb_epoch,
verbose=1,
# callbacks=[tb, early_stopper, csv_logger, checkpointer],
callbacks=[ early_stopper, checkpointer],
validation_data=val_generator,
validation_steps=40,
workers=4)