with pre_graph.as_default():
pre_model = getModel(model_type)
pre_model.load_weights(
'logs/%s/kfold_%s/kfold_%s_dice_DA_K%d/kfold_%s_dice_DA_K%d_weights.h5'
% (model_type, model_type, model_type, i, model_type, i))
exp_name = 'kfold_%s_BiCLSTM_K%d' % (model_type, i)
#get parameters
params = getParams(exp_name, model_type, is_lstm=True)
#set common variables
epochs = 10
batch_size = 10
verbose = 1
tr_images, tr_masks, te_images, te_masks = dh.getKFoldData(
image_files, mask_files, kfold_indices[i])
train_generator = lstmGenerator(tr_images, tr_masks, batch_size, pre_model,
pre_graph)
val_generator = lstmGenerator(te_images, te_masks, batch_size, pre_model,
pre_graph)
#Get model and add weights
lstm_graph = tf.get_default_graph()
with lstm_graph.as_default():
model = lstmModel()
model_json = model.to_json()
with open(params['model_name'], "w") as json_file:
json_file.write(model_json)
