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CNN.py
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CNN.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 22 16:10:25 2019
@author: will
"""
import copy
import time
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils import clip_grad_value_
def train_cnn(model,optimizer,train_loader,valid_loader,n_epochs,clip,scheduler):
start_time = time.time()
criterion = nn.SmoothL1Loss()
valid_loss_min = np.Inf # track change in validation loss
for epoch in range(1, n_epochs+1):
# keep track of training and validation loss
train_loss = 0.0
valid_loss = 0.0
###################
# train the model #
###################
model.train()
for ind, (data, target) in enumerate(train_loader):
print(ind, end='\r')
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output.view(data.shape[0]), target.float())
loss.backward()
clip_grad_value_(model.parameters(),clip)
optimizer.step()
train_loss += loss.item()*data.size(0)
######################
# validate the model #
######################
model.eval()
with torch.no_grad():
for data, target in valid_loader:
data, target = data.cuda(), target.cuda()
output = model(data)
loss = criterion(output.view(data.shape[0]), target.float())
valid_loss += loss.item() * data.size(0)
# calculate average losses
train_loss = train_loss / len(train_loader.sampler)
valid_loss = valid_loss / len(valid_loader.sampler)
# print training/validation statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch, train_loss, valid_loss))
# save model if validation loss has decreased
if valid_loss <= valid_loss_min:
bestWeight = copy.deepcopy(model.state_dict())
scheduler.step(valid_loss)
model.load_state_dict(bestWeight)
time_elapsed = time.time() - start_time
print('Training completed in {}s'.format(time_elapsed))
return model