def train_model(learning_rate, batch_size, num_epochs, steps_per_epoch,
validation_steps, workers, optimizer, weight_file_name):
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes)
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
if optimizer == 'Nadam':
model.compile(optimizer=keras.optimizers.Adam(learning_rate),
loss='categorical_crossentropy')
elif optimizer == 'Adam':
model.compile(optimizer=keras.optimizers.Nadam(lr=learning_rate,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
schedule_decay=0.004),
loss='categorical_crossentropy')
else:
raise RuntimeError('Invalid optimizer type "{}"'.format(optimizer))
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data',
'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter(save_graphs=True)
callbacks = [logger_cb]
model.fit_generator(
train_iter,
steps_per_epoch=steps_per_epoch, # the number of batches per epoch,
epochs=num_epochs, # the number of epochs to train for,
validation_data=val_iter, # validation iterator
validation_steps=
validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers=workers)
# Save your trained model weights
model_tools.save_network(model, weight_file_name)
model.compile(optimizer=keras.optimizers.Adam(learning_rate),
loss='categorical_crossentropy')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data', 'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
callbacks = [logger_cb]
model.fit_generator(
train_iter,
steps_per_epoch=steps_per_epoch, # the number of batches per epoch,
epochs=num_epochs, # the number of epochs to train for,
validation_data=val_iter, # validation iterator
validation_steps=validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers=workers)
# In[12]:
# Save your trained model weights
weight_file_name = 'model_weights'
def train_model(fcn_func, filter_set, learning_params=None):
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
print("Training: ", fcn_func.__name__, str(filter_set))
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
output_layer = fcn_func(inputs, num_classes, filter_set)
if learning_params is None:
learning_rate = 0.002
batch_size = 40
num_epochs = 15
steps_per_epoch = 200
validation_steps = 50
workers = 4
else:
learning_rate = learning_params["learning_rate"]
batch_size = learning_params["batch_size"]
num_epochs = learning_params["num_epochs"]
steps_per_epoch = learning_params["steps_per_epoch"]
validation_steps = learning_params["validation_steps"]
workers = learning_params["workers"]
############# Training code ##############
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate),
loss='categorical_crossentropy')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join(
'..', 'data',
'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
callbacks = [logger_cb]
model.fit_generator(
train_iter,
steps_per_epoch=steps_per_epoch, # the number of batches per epoch,
epochs=num_epochs, # the number of epochs to train for,
validation_data=val_iter, # validation iterator
validation_steps=
validation_steps, # the number of batches to validate on
# callbacks=callbacks,
workers=workers)
print("Done training", fcn_func.__name__, str(filter_set))
return model