deep = DeepAutoencoder()
data = train_images
for i in range(n_layers):
vis_func = None if i == 0 else neuron_fn
# create autoencoder for the next layer
auto = Autoencoder(shapes[i],
shapes[i + 1],
rf_shape=rf_shapes[i],
vis_func=vis_func,
hid_func=neuron_fn)
deep.autos.append(auto)
# train the autoencoder using SGD
auto.auto_sgd(data, deep, test_images, n_epochs=n_epochs, rate=rates[i])
# hidden layer activations become training data for next layer
data = auto.encode(data)
plt.figure(99)
plt.clf()
recons = deep.reconstruct(test_images)
show_recons(test_images, recons)
print "recons error", rms(test_images - recons, axis=1).mean()
deep.auto_sgd(train_images, test_images, rate=0.3, n_epochs=30)
print "recons error", rms(test_images - recons, axis=1).mean()
# --- train classifier with backprop
deep.train_classifier(train, test)
assert len(rf_shapes) == n_layers
assert len(rates) == n_layers
n_epochs = 5
batch_size = 100
deep = DeepAutoencoder()
data = train_images
for i in range(n_layers):
savename = "sigmoid-auto-%d.npz" % i
if not os.path.exists(savename):
auto = Autoencoder(
shapes[i], shapes[i+1], rf_shape=rf_shapes[i],
vis_func=funcs[i], hid_func=funcs[i+1])
deep.autos.append(auto)
auto.auto_sgd(data, deep, test_images, noise=0.1,
n_epochs=n_epochs, rate=rates[i])
auto.to_file(savename)
else:
auto = FileObject.from_file(savename)
assert type(auto) is Autoencoder
deep.autos.append(auto)
data = auto.encode(data)
plt.figure(99)
plt.clf()
recons = deep.reconstruct(test_images)
show_recons(test_images, recons)
print "recons error", rms(test_images - recons, axis=1).mean()
