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utils.py
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utils.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Apr 19 17:38:22 2018
@author: lenovo
"""
import numpy as np
import torch
import pandas as pd
###=====check the acc of model on loader, if error_analysis return confuseMatrix====
def check_accuracy(model, loader, device, error_analysis=False):
# save the errors samples predicted by model
ys = np.array([])
y_preds = np.array([])
confuse_matrix = None
# correct counts
num_correct = 0
model.eval() # Put the model in test mode (the opposite of model.train(), essentially)
with torch.no_grad():
# one batch
for x, y in loader:
x.resize_(x.size()[0], 1, x.size()[1])
x, y = x.float(), y.long()
x, y = x.to(device), y.to(device)
# predictions
scores = model(x)
preds = scores.max(1, keepdim=True)[1]
# accumulate the corrects
num_correct += preds.eq(y.view_as(preds)).sum().item()
# confuse matrix: labels and preds
if error_analysis:
ys = np.append(ys, np.array(y))
y_preds = np.append(y_preds, np.array(preds))
acc = float(num_correct) / len(loader.dataset)
# confuse matrix
if error_analysis:
confuse_matrix = pd.crosstab(y_preds, ys, margins=True)
print('Got %d / %d correct (%.2f)' % (num_correct, len(loader.dataset), 100 * acc))
return acc, confuse_matrix