return valid_loss
hyperloss(transform_parser.vect, 0, record_results=True)
return results['train_loss'][-1], results['valid_loss'][-1]
def plot():
import matplotlib.pyplot as plt
import matplotlib as mpl
with open('results.pkl') as f:
train_loss, valid_loss = zip(*pickle.load(f))
fig = plt.figure(0)
fig.set_size_inches((6, 4))
ax = fig.add_subplot(111)
ax.set_title('Performance vs weight_sharing')
ax.plot(all_script_corr, train_loss, 'o-', label='train_loss')
ax.plot(all_script_corr, valid_loss, 'o-', label='valid_loss')
ax.set_xlabel('Weight sharing')
ax.set_ylabel('Negative log prob')
ax.legend(loc=1, frameon=False)
plt.savefig('performance.png')
all_script_corr = np.linspace(0, 1, 10)
if __name__ == '__main__':
results = omap(run, all_script_corr)
with open('results.pkl', 'w') as f:
pickle.dump(results, f, 1)
plot()
d_losses = zip(*[d_loss for loss, d_loss in data])
fig = plt.figure(0)
fig.clf()
fig.set_size_inches((8,8))
ax = fig.add_subplot(211)
for loss_curve, N_iter in zip(losses, all_N_iters):
ax.plot(all_log_alpha_0, loss_curve, 'o-', label="{0} iters".format(N_iter))
ax.set_title("Loss vs step size")
ax.set_ylim([-0, 3])
ax.set_ylabel("Negative log loss per datum")
ax.set_xlabel(xlabel)
# ax.legend(loc=4)
ax = fig.add_subplot(212)
for d_loss_curve, N_iter in zip(d_losses, all_N_iters):
ax.plot(all_log_alpha_0, d_loss_curve, 'o-', label="{0} iters".format(N_iter))
ax.set_title("Grad loss vs step size")
ax.set_ylim([-0.5, 0.5])
ax.set_ylabel("Negative log loss per datum")
ax.set_xlabel(xlabel)
ax.legend(loc=2)
plt.savefig("/tmp/fig.png")
plt.savefig("fig.png")
if __name__ == '__main__':
results = omap(run, range(N_oiter))
# results = collect_results(225866836343)
with open('results.pkl', 'w') as f:
pickle.dump(results, f)
plot()
results['train_loss'].append(total_loss(train_data, z_vect_final))
# results['tests_loss'].append(total_loss(tests_data, z_vect_final))
return valid_loss
hyperloss(transform_parser.vect, 0, record_results=True)
return results['train_loss'][-1], results['valid_loss'][-1]
def plot():
import matplotlib.pyplot as plt
import matplotlib as mpl
with open('results.pkl') as f:
train_loss, valid_loss = zip(*pickle.load(f))
fig = plt.figure(0)
fig.set_size_inches((6,4))
ax = fig.add_subplot(111)
ax.set_title('Performance vs weight_sharing')
ax.plot(all_script_corr, train_loss, 'o-', label='train_loss')
ax.plot(all_script_corr, valid_loss, 'o-', label='valid_loss')
ax.set_xlabel('Weight sharing')
ax.set_ylabel('Negative log prob')
ax.legend(loc=1, frameon=False)
plt.savefig('performance.png')
all_script_corr = np.linspace(0, 1, 10)
if __name__ == '__main__':
results = omap(run, all_script_corr)
with open('results.pkl', 'w') as f:
pickle.dump(results, f, 1)
plot()
def check_omap():
# This won't work with nosetest. Needs to be run from the same directory as the file.
ans = omap(identity, objects)
for x, y in zip(ans, objects):
assert x == y, "Failed on {0}".format(y)
print "test ok"
def check_omap():
# This won't work with nosetest. Needs to be run from the same directory as the file.
ans = omap(identity, objects)
for x, y in zip(ans, objects):
assert x == y, "Failed on {0}".format(y)
print "test ok"
ax.plot(all_log_alpha_0,
loss_curve,
'o-',
label="{0} iters".format(N_iter))
ax.set_title("Loss vs step size")
ax.set_ylim([-0, 3])
ax.set_ylabel("Negative log loss per datum")
ax.set_xlabel(xlabel)
# ax.legend(loc=4)
ax = fig.add_subplot(212)
for d_loss_curve, N_iter in zip(d_losses, all_N_iters):
ax.plot(all_log_alpha_0,
d_loss_curve,
'o-',
label="{0} iters".format(N_iter))
ax.set_title("Grad loss vs step size")
ax.set_ylim([-0.5, 0.5])
ax.set_ylabel("Negative log loss per datum")
ax.set_xlabel(xlabel)
ax.legend(loc=2)
plt.savefig("/tmp/fig.png")
plt.savefig("fig.png")
if __name__ == '__main__':
results = omap(run, range(N_oiter))
# results = collect_results(225866836343)
with open('results.pkl', 'w') as f:
pickle.dump(results, f)
plot()