gistlib - the nmr sequence wavelet coefficients as the cnn-resnet input, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet18 output for denoising in python

To use the NMR sequence wavelet coefficients as CNN-Resnet input and output for denoising in Python, you can follow the following steps:

Load the NMR sequence data.
Decompose the NMR sequence data into wavelet coefficients using PyWavelets library in Python.
Reshape the wavelet coefficients into a format that can be used as CNN-Resnet input.
Train the CNN-Resnet using the wavelet coefficients as input and output for denoising.
Use the trained network to denoise the test data.

Here's an example code to load the NMR sequence data and decompose it into wavelet coefficients using PyWavelets library:


main.py
import pywt

# Load NMR sequence data
nmr_seq = load_nmr_sequence_data()

# Decompose NMR sequence data into wavelet coefficients
coeffs = pywt.wavedec(nmr_seq, 'db4', level=3)
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Next, you can reshape the wavelet coefficients into a format that can be used as CNN-Resnet input. For example, you can reshape the coefficients into a 3D array with dimensions (num_samples, num_coefficients, num_levels).


main.py
import numpy as np

# Reshape wavelet coefficients into a format that can be used as CNN-Resnet input
num_samples = nmr_seq.shape[0]
num_levels = len(coeffs)-1
num_coefficients = len(coeffs[1])
input_shape = (num_samples, num_coefficients, num_levels)

input_data = np.zeros(input_shape)

for i in range(num_samples):
    for j in range(num_levels):
        input_data[i, :, j] = coeffs[j+1][i]
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Finally, you can use the input and output data to train the CNN-Resnet for denoising.


main.py
from keras.models import Sequential
from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Reshape, BatchNormalization
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.optimizers import Adam

# Define CNN-Resnet model
model = Sequential()
model.add(Conv1D(32, 3, activation='relu', padding='same', input_shape=input_shape))
model.add(BatchNormalization())
model.add(Conv1D(32, 3, activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv1D(32, 3, activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv1D(input_shape[1], 3, activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Dense(num_coefficients*num_levels, activation='linear'))
model.add(Reshape((num_coefficients, num_levels)))

# Define optimizer and loss function
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(optimizer=optimizer, loss='mean_squared_error')

# Train the model
early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1, mode='auto')
checkpointer = ModelCheckpoint(filepath='best_model.hdf5', verbose=1, save_best_only=True)
history = model.fit(input_data, input_data, batch_size=32, epochs=100, validation_split=0.2, callbacks=[early_stop, checkpointer])
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After the model is trained, you can use it to denoise the test data by passing the test wavelet coefficients as input to the model and getting the output wavelet coefficients as denoised data.


main.py
# Load test NMR sequence data
test_nmr_seq = load_test_nmr_sequence_data()

# Decompose test NMR sequence data into wavelet coefficients
test_coeffs = pywt.wavedec(test_nmr_seq, 'db4', level=3)

# Reshape test wavelet coefficients into a format that can be used as CNN-Resnet input
test_input_data = np.zeros(input_shape)

for i in range(num_samples):
    for j in range(num_levels):
        test_input_data[i, :, j] = test_coeffs[j+1][i]

# Denoise the test data using the trained model
denoised_coeffs = model.predict(test_input_data)

# Reshape denoised wavelet coefficients into original shape
denoised_data = np.zeros(test_nmr_seq.shape)

for i in range(num_samples):
    denoised_coeffs_i = []
    for j in range(num_levels):
        denoised_coeffs_i.append(denoised_coeffs[i, :, j])
    denoised_seq_i = pywt.waverec(denoised_coeffs_i, 'db4')
    denoised_data[i] = denoised_seq_i[:test_nmr_seq.shape[1]]
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the nmr sequence wavelet coefficients as the cnn-resnet input, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet18 output for denoising in python

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