cpeg_ml's Introduction

CPEG_ML

Based on the artifical Neural Network to fit the optical parameters

Before starting this project, you should install following libraries:

You can modify the training details/saving path in config.py.

You can start to train model by following command: python train.py.

The function of each py.files are defined as follows:

config.py

vth： the nth velocity with range of v1~v6

epoch： total step of training process.

N1/N2 mean that the nums of nerons in each layer and layesr is the nums of hidden layers.

save_path is the training saving path, which include the training results.

save_infer_path include the inference results.
train.py

Your can modify the training parameters in config.py. For example, your can change the vth to modify the target vth velocity in the machine learning process.
inference.py

(1). inference_results_mesh().

In this function, you can infer the vth by feeding the mesh inputs into the well training model. The process of this calculation function is :

for x in (-0.05, 0.05, 0.01):
for y in (-0.05, 0.05, 0.01):
for z in (-0.05, 0.05, 0.01):
vth = model([x, y, z])

(2). inference_results_uniform().

In this function, you can infer the vth by feeding the uniform inputs into the well training model. The process of this calculation function is:

for x in (-0.05, 0.05, 0.01):
y, z = x
vth = model([x, y, z])

Additionally, you can change the strain direction by modifying the input_d vector. For example, input_d = [1, 0, 0] corresponds to strain along the x-direction, [1, 1, 0] corresponds xy direction.

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