chemron / SolarMagGAN

Pipeline:

1. run get_data.py to download AIA and HMI data in the .fits file format to the folder FITS_DATA. This also creates a pickle files with the data queries. OR
2. run get_pickle_data.py if there already exists a pickle file with the data queries to download the AIA and HMI data in the .fits file format to the folder FITS_DATA.
3. run fits_to_png.py to create normalised and centered AIA and HMI png files from the fits files, and put them into testing and training sets. This is done in the DATA folder.
4. run train.py to train the GAN on the data, saving the models in the folder MODELS.
5. run test.py to test the models on new data, and generate figures for the results. The output png files are saved in the RESULTS folder, while the generated figures are saved in the FIGURES folder.

Environments

To run train.py and test.py with the correct packages we made use of a conda environment.

To create a similar conda environment for using a gpu use the command:

conda create --name gpu_env python=3.6 tensorflow-gpu==1.12 keras-gpu imageio matplotlib

Alternatively, for a normal cpu, use the command:

conda create --name cpu_env python=3.6 tensorflow==1.12 Keras imageio matplotlib

Network architectures

Generator architectures

The generator is consist of the encoder-decoder architecture:

encoder:

1. Conv2D(filers = 64, strides = 2), LeakyReLu(slope = 0.2)
2. Conv2D(filers = 128, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
3. Conv2D(filers = 256, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
4. Conv2D(filers = 512, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
5. Conv2D(filers = 512, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
6. Conv2D(filers = 512, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
7. Conv2D(filers = 512, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
8. Conv2D(filers = 512, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
9. Conv2D(filers = 512, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
10. Conv2D(filers = 512, strides = 2), ReLu

decoder:

1. Conv2DTranspose(filter = 512, strides = 2), BatchNorm, Dropout(rate = 0.5), ReLU
2. Conv2DTranspose(filter = 512, strides = 2), BatchNorm, Dropout(rate = 0.5), ReLU
3. Conv2DTranspose(filter = 512, strides = 2), BatchNorm, Dropout(rate = 0.5), ReLU
4. Conv2DTranspose(filter = 512, strides = 2), BatchNorm, ReLU
5. Conv2DTranspose(filter = 512, strides = 2), BatchNorm, ReLU
6. Conv2DTranspose(filter = 512, strides = 2), BatchNorm, ReLU
7. Conv2DTranspose(filter = 256, strides = 2), BatchNorm, ReLU
8. Conv2DTranspose(filter = 128, strides = 2), BatchNorm, ReLU
9. Conv2DTranspose(filter = 64, strides = 2), BatchNorm, ReLU
10. Conv2DTranspose(filter = 1, strides = 2), Tanh

Also, the generator has skip-connections between layers of the encoder and layers of the decoder like the U-Net architecture.

skip-connection:

• encoder 1st layer - decoder 9th layer
• encoder 2nd layer - decoder 8th layer
• encoder 3rd layer - decoder 7th layer
• encoder 4th layer - decoder 6th layer
• encoder 5th layer - decoder 5th layer
• encoder 6th layer - decoder 4th layer
• encoder 7th layer - decoder 3rd layer
• encoder 8th layer - decoder 2nd layer
• encoder 9th layer - decoder 1st layer

Discriminator architectures

The discriminator architecture is described in the following notation:

• Conv2D(filers = 64, strides = 2), LeakyReLu(slope = 0.2)
• Conv2D(filers = 128, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
• Conv2D(filers = 256, strides = 2), BatchNorm, LeakyReLu(slope = 0.2)
• Conv2D(filers = 512, strides = 1), BatchNorm, LeakyReLu(slope = 0.2)
• Conv2D(filers = 1, strides = 1), Sigmoid

The receptive field size used in our discriminator is 70 x 70.

Hyperparameter

The loss configuration of the objective functions of the Generator

• Total loss = loss of J2 + 100 * loss of J1

Batch

• Batch iteration : 500,000
• Batch size : 1

Optimizer

• Optimizer : Adam solver
• Learning rate : 0.0002
• momentum beta 1 parameter : 0.5
• momentum beta 2 parameter : 0.999

Initializer

• The Initializer of the Convolution Layers : normal distribution, mean : 0.0, stddev : 0.02
• The gamma initializer of the BatchNormalization layers : normal distribution, mean : 1.0, stddev : 0.02