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Morvan Zhou
2017-06-18 00:18:42 +10:00
committed by Morvan Zhou
parent dcc4e7cb84
commit 51f1c938f3
7 changed files with 32 additions and 79 deletions
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32
tutorial-contents/504_batch_normalization.py
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@ -99,30 +99,17 @@ loss_func = torch.nn.MSELoss()
f, axs = plt.subplots(4, N_HIDDEN+1, figsize=(10, 5))
plt.ion() # something about plotting
plt.show()
def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):
for i, (ax_pa, ax_pa_bn, ax, ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):
[a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]
if i == 0:
p_range = (-7, 10)
the_range = (-7, 10)
else:
p_range = (-4, 4)
the_range = (-1, 1)
if i == 0: p_range = (-7, 10);the_range = (-7, 10)
else:p_range = (-4, 4);the_range = (-1, 1)
ax_pa.set_title('L' + str(i))
ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5)
ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)
ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359')
ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')
for a in [ax_pa, ax, ax_pa_bn, ax_bn]:
a.set_yticks(())
a.set_xticks(())
ax_pa_bn.set_xticks(p_range)
ax_bn.set_xticks(the_range)
axs[0, 0].set_ylabel('PreAct')
axs[1, 0].set_ylabel('BN PreAct')
axs[2, 0].set_ylabel('Act')
axs[3, 0].set_ylabel('BN Act')
ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)
ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')
for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(())
ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range)
axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act')
plt.pause(0.01)
# training
@ -155,10 +142,7 @@ plt.ioff()
plt.figure(2)
plt.plot(losses[0], c='#FF9359', lw=3, label='Original')
plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')
plt.xlabel('step')
plt.ylabel('test loss')
plt.ylim((0, 2000))
plt.legend(loc='best')
plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best')
# evaluation
# set net to eval mode to freeze the parameters in batch normalization layers