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Commit d2ef840c authored by TheRiPtide's avatar TheRiPtide
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chore: started cnn notebook

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1 merge request!23feat: deep-leaning poly(A) classifier
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%% Cell type:markdown id: tags:
# Issue 21: Inferring the code of internal priming by deep learning
In real data sets we would like to distinguish poly(A) sites from internal priming sites. To do this, we want to construct a classifier that uses the sequence flanking the sites. As a deep learning architecture we can use a convolutional neural network, for e.g. from a numpy implementation, https://pypi.org/project/numpycnn/)
Reference: https://www.analyticsvidhya.com/blog/2019/10/building-image-classification-models-cnn-pytorch/
Input: sequences of bona fide and internally-primed poly(A) sites (#16)
Output: classifier based on the nucleotide sequence around the sites
%% Cell type:code id: tags:
``` python
# importing the libraries
import pandas as pd
import numpy as np
# for creating validation set
from sklearn.model_selection import train_test_split
# for evaluating the model
from sklearn.metrics import accuracy_score
from tqdm import tqdm
# PyTorch libraries and modules
import torch
from torch.autograd import Variable
from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, Conv2d, MaxPool2d, Module, Softmax, BatchNorm2d, Dropout
from torch.optim import Adam, SGD
# adding the nn
class Net(Module):
def __init__(self):
super(Net, self).__init__()
self.cnn_layers = Sequential(
# Defining a 2D convolution layer
Conv2d(1, 4, kernel_size=3, stride=1, padding=1),
BatchNorm2d(4),
ReLU(inplace=True),
MaxPool2d(kernel_size=2, stride=2),
# Defining another 2D convolution layer
Conv2d(4, 4, kernel_size=3, stride=1, padding=1),
BatchNorm2d(4),
ReLU(inplace=True),
MaxPool2d(kernel_size=2, stride=2),
)
self.linear_layers = Sequential(
Linear(4 * 7 * 7, 10)
)
# Defining the forward pass
def forward(self, x):
x = self.cnn_layers(x)
x = x.view(x.size(0), -1)
x = self.linear_layers(x)
return x
```
%% Cell type:markdown id: tags:
## Load data
%% Cell type:code id: tags:
``` python
# TODO: Get test data from issues 25 and 26
train_x = []
train_y = []
test_x = []
test_y = []
train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size = 0.1)
# TODO: reshape shape from [n, l] to [n, 1, l]
```
%% Cell type:markdown id: tags:
# Model call and loss function definition
%% Cell type:code id: tags:
``` python
# defining the model
model = Net()
# defining the optimizer
optimizer = Adam(model.parameters(), lr=0.07)
# defining the loss function
criterion = CrossEntropyLoss()
# checking if GPU is available
if torch.cuda.is_available():
model = model.cuda()
criterion = criterion.cuda()
```
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