sys.path.insert(1, os.path.join(sys.path[0], '../src'))
import nn
parser = argparse.ArgumentParser()
parser.add_argument('source_layout', metavar='source-layout', help='Path source network layout specification')
parser.add_argument('source_weights', metavar='source-weights', help='Path sourcce network weights')
parser.add_argument('target_layout', metavar='target-layout', help='Path target network layout specification')
parser.add_argument('target_weights', metavar='target-weights', help='Path target network weights')
parser.add_argument('layerspec', help='Which layers to copy. Format: a-b-...-z where a-z are 0-based layer numbers')
args = parser.parse_args()
#~ Load source model
print('Loading source model from {0}'.format(args.source_layout))
source_layout = nn.load_layout(args.source_layout)
source_model, source_optimizer = nn.build_model_to_layout(source_layout)
#~ Load source weights
print('\tLoading source weights from {0}'.format(args.source_weights))
source_model.load_weights(args.source_weights)
#~ Load target model
print('Loading target model from {0}'.format(args.target_layout))
target_layout = nn.load_layout(args.target_layout)
target_model, target_optimizer = nn.build_model_to_layout(target_layout)
#~ Load target weights
if os.path.isfile(args.target_weights):
print('\tLoading target weights from {0}'.format(args.target_weights))
target_model.load_weights(args.target_weights)
else:
# paste the filter into the filter collection
filter_collection.paste(im_filter, (xpos, ypos))
# save the filter collection of layer 'id'
filter_collection.save(path + "weights_on_layer_" + str(layer_id) + "_" + filename + ".png")
if __name__ == "__main__":
#~ Parse parameters
parser = argparse.ArgumentParser()
parser.add_argument('weights', help='Path to the weights which are to be loaded')
parser.add_argument('layout', help='Path network layout specification')
parser.add_argument('-s', '--savepath', help='Path to save location of the visualized filters', default='./')
parser.add_argument('-v', '--verbose', help='Determine whether the programm shall print information in the terminal or not', action="store_true")
parser.add_argument('-n', '--filename', help='Pass a string which is appended to the created image files.', default='')
args = parser.parse_args()
# Load model
print('Loading model from {0}'.format(args.layout))
layout = nn.load_layout(args.layout)
model, optimizer = nn.build_model_to_layout(layout)
#~ Load weights
print('Loading weights from \"{0}\"'.format(args.weights))
model.load_weights(args.weights)
# visualize filters
print('Generating visualizations and storing to {0}'.format(args.savepath))
visualize_filters(model, args.savepath, args.filename)
print('Done')
x_test, y_test = dataset_io.read_data(test_data_path, resolution, normalize=normalize, grayscale=grayscale) # change
if normalize_output:
y_train /= max_dim
y_test /= max_dim
loss_callbacks = []
# do grid search
for m_idx, m in enumerate(momentums):
#for w_idx, w in enumerate(weightscales):
for l_idx, l in enumerate(learningrates):
print("Learning rate: ", l)
#print("weightscale: ", w)
print("momentum: ", m)
model, optimizer = nn.build_model_to_layout(layout, learningrate=l, momentum=m, decay=0.0) #change
if normalize_output:
callbacks = [custom_callbacks.StopEarly(3), custom_callbacks.Distortions(x_train, y_train, x_train.shape[0], (float(original_resolution[0]) / float(max_dim), float(original_resolution[1]) / float(max_dim)))]
else:
callbacks = [custom_callbacks.StopEarly(3), custom_callbacks.Distortions(x_train, y_train, x_train.shape[0], original_resolution)]
loss_callback = custom_callbacks.RecordLoss(epochs, nb_labels, x_train, y_train, resolution, model, grayscale)
callbacks.append(loss_callback)
loss_callbacks.append(loss_callback)
# scale weights
#weights = model.get_weights()
#helpers.mult_list(weights, w * 20)
#model.set_weights(weights)
# Train the model
