https://github.com/bamos/densenet.pytorch
Tip revision: d1cd5e1957975628286e516512c6d1c14430f810 authored by Brandon Amos on 11 February 2017, 14:17:25 UTC
Add int casts around math.floor for python2.
Add int casts around math.floor for python2.
Tip revision: d1cd5e1
plot.py
#!/usr/bin/env python3
import argparse
import os
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('bmh')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('expDir', type=str)
args = parser.parse_args()
trainP = os.path.join(args.expDir, 'train.csv')
trainData = np.loadtxt(trainP, delimiter=',').reshape(-1, 3)
testP = os.path.join(args.expDir, 'test.csv')
testData = np.loadtxt(testP, delimiter=',').reshape(-1, 3)
N = 392*2 # Rolling loss over the past epoch.
trainI, trainLoss, trainErr = np.split(trainData, [1,2], axis=1)
trainI, trainLoss, trainErr = [x.ravel() for x in
(trainI, trainLoss, trainErr)]
trainI_, trainLoss_, trainErr_ = rolling(N, trainI, trainLoss, trainErr)
testI, testLoss, testErr = np.split(testData, [1,2], axis=1)
fig, ax = plt.subplots(1, 1, figsize=(6, 5))
# plt.plot(trainI, trainLoss, label='Train')
plt.plot(trainI_, trainLoss_, label='Train')
plt.plot(testI, testLoss, label='Test')
plt.xlabel('Epoch')
plt.ylabel('Cross-Entropy Loss')
plt.legend()
ax.set_yscale('log')
loss_fname = os.path.join(args.expDir, 'loss.png')
plt.savefig(loss_fname)
print('Created {}'.format(loss_fname))
fig, ax = plt.subplots(1, 1, figsize=(6, 5))
# plt.plot(trainI, trainErr, label='Train')
plt.plot(trainI_, trainErr_, label='Train')
plt.plot(testI, testErr, label='Test')
plt.xlabel('Epoch')
plt.ylabel('Error')
ax.set_yscale('log')
plt.legend()
err_fname = os.path.join(args.expDir, 'error.png')
plt.savefig(err_fname)
print('Created {}'.format(err_fname))
loss_err_fname = os.path.join(args.expDir, 'loss-error.png')
os.system('convert +append {} {} {}'.format(loss_fname, err_fname, loss_err_fname))
print('Created {}'.format(loss_err_fname))
def rolling(N, i, loss, err):
i_ = i[N-1:]
K = np.full(N, 1./N)
loss_ = np.convolve(loss, K, 'valid')
err_ = np.convolve(err, K, 'valid')
return i_, loss_, err_
if __name__ == '__main__':
main()