The report is in the REPORT directory.
Start the terminal and you can install the virtual environment as follow:
# Go to assignment folder
cd ML395_NN
# Change permissions to execute bash script
chmod 755 install_lab_env.sh
# Create virtual environment 'env' and install requirements
# This can take several minutes to finish
./install_lab_env.shHere is the example of how to run src/test.py.
model_path_q5 = "PATH/TO/THIS/REPO/pkl/final_model.pkl"
img_folder_q5 = "PATH/TO/IMAGE"
# Call test_fer_model() for Q5
test_fer_model(img_folder, model_path)
model_path_q6 = "PATH/TO/THIS/REPO/src/question6/models/vgg_netvgg.hdf5"
img_folder_q6 = "PATH/TO/IMAGE"
# Call test_deep_fer_model() for Q6
test_deep_fer_model(img_folder, model_path)
train_fcnet_optimise_params.py is used to train the optimised model for question 5. The model can be trained directly from the JPEG data on bitbucket, however this is slow to load. Also provided is a function to load the data from a pickle object, which loads significantly faster.
train_fcnet_optimise_params_gridsearch and train_fcnet_optimise_params_gridsearch_2dims are used only to search for parameters and were not used to train the final model.
Please notice that this year, we have an intermediate and an advanced version for the second assignment. Identify if your group consists of:
- Master's Computing students: You must complete this advanced assignment
(
manuals/assignment2_advanced.md, pdf format is also available). - Non Master's Computing students (Bachelor's Computing/non-Computing/external/exchange): We recommend you complete the the intermediate assignment which is available on the group web site for download. Optionally: You can also choose this advanced assignment, it is up to you.
- Mixed of Master's Computing and non Master's Computing students: You
must complete the advanced assignment (
manuals/assignment2_advanced.md, pdf format is also available).