a long nmr sequence are input as cnn-resnet, and the corresponding nmr sequence are used as cnn-resnet18 output for denoising in python

Assuming you have a dataset consisting of pairs of noisy NMR sequences and clean NMR sequences, you can use the following steps to denoise the noisy NMR sequences using a CNN-ResNet18 model in Python:

Load the dataset: Load the dataset consisting of pairs of noisy NMR sequences and the corresponding clean NMR sequences. Split the dataset into training and validation sets.
Prepare the data: Convert the NMR sequences to spectrograms using a Fast Fourier Transform (FFT). Normalize the spectrograms to have zero mean and unit variance.
Define the model: Create a CNN-ResNet18 model using any deep learning framework like Keras or PyTorch. The model should take the normalized spectrograms as input and output a denoised spectrogram.
Train the model: Train the CNN-ResNet18 model on the training dataset. The training process should involve minimizing a loss function that measures the difference between the denoised spectrogram and the clean spectrogram. Use the validation dataset to monitor the performance of the model during training and prevent overfitting.
Evaluate the model: Evaluate the performance of the trained model on a test dataset consisting of noisy NMR sequences.
Denoise new NMR sequences: Use the trained CNN-ResNet18 model to denoise new noisy NMR sequences by converting them to spectrograms, normalizing them, passing them through the model, and converting the output back to the time domain using an Inverse Fast Fourier Transform (IFFT).

Here's some sample code for step 3 using PyTorch:


main.py
import torch.nn as nn
import torch.nn.functional as F

class CNNResNet18(nn.Module):
    def __init__(self):
        super(CNNResNet18, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256)
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.layer1(x) + x
        x = self.layer2(x) + x
        x = self.layer3(x) + x
        x = self.layer4(x) + x
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc(x))
        x = F.relu(self.fc2(x))
        x = torch.sigmoid(self.fc3(x))
        return x
2076 chars
56 lines

Note that this is just a sample PyTorch implementation of CNN-ResNet18 and you may need to modify it based on your specific needs.

similar python code snippets

implement fastercnn in python

self growth neural network in python

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

neural network word recognition in python

implement a machine learning model to generate images of cats in python

custom neural network in python

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

write code that learns from code in python

train a instance segmentation neural network in python

how to implement yolo in python

related categories