EELSpecNet

API Reference

EELSpecNet Model

EELSpecNetModel_CNN_10D

The main model class for spectral deconvolution.

Parameters

• input_size (int): Size of the input spectrum (default: 2048)

Methods

• compile(optimizer, loss, metrics): Compile the model with specified optimizer and loss function
• fit(x, y, validation_split, batch_size, epochs): Train the model on input data
• predict(x): Generate predictions for input data
• evaluate(x, y): Evaluate model performance on test data

Data Generation

GenerateData

Module for generating training and evaluation data.

Functions

• training_signal_set(size, snr, psf_width_min, psf_width_max, dim, noise_level): Generate training data
• eval_signal_set(size, snr, psf_width_min, psf_width_max, dim, noise_level): Generate evaluation data

Usage Examples

import EELSpecNet
import GenerateData as gene

# Create model
model = EELSpecNet.EELSpecNetModel_CNN_10D(2048)

# Generate training data
train_target, train_initial = gene.training_signal_set(
    6000,  # size
    -2,    # snr
    0.005, # psf_width_min
    0.015, # psf_width_max
    2048,  # dim
    0.05   # noise_level
)

# Train model
model.fit(train_initial, train_target, 
         validation_split=0.16,
         batch_size=16,
         epochs=1000)

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