for label in range(10):
index = np.where(y_test == label)[0][0]
test_images[label] = x_test[index]
results = np.zeros((2, 10, 28 * 28))
for mi, model in enumerate(("supervised", "unsupervised")):
print("Start Autoencoder using {} model".format(model))
if model == "supervised":
eforest = RandomForestClassifier(n_estimators=n_trees,
max_depth=None,
n_jobs=-1,
random_state=0)
eforest.fit(x_train, y_train)
else:
eforest = RandomTreesEmbedding(n_estimators=n_trees,
max_depth=None,
n_jobs=-1,
random_state=0)
eforest.fit(x_train)
x_encode = eforest.encode(test_images)
x_decode = eforest.decode(x_encode)
results[mi] = x_decode
rheads = ["origin", "supervised", "unsupervised"]
test_images = test_images.reshape(1, 10, 28, 28)
results = results.reshape(2, 10, 28, 28)
fig = plot_mnist(rheads, np.vstack((test_images, results)))
plt.show()
import IPython
IPython.embed()
eforest_channels = []
if model == "supervised":
for c in range(3):
eforest = RandomForestClassifier(n_estimators=n_trees,
max_depth=None,
n_jobs=-1,
random_state=0)
eforest.fit(x_train[:, :, 0], y_train)
eforest_channels.append(eforest)
else:
for c in range(3):
eforest = RandomTreesEmbedding(n_estimators=n_trees,
max_depth=None,
n_jobs=-1,
random_state=0)
eforest.fit(x_train[:, :, 0])
eforest_channels.append(eforest)
for c in range(3):
x_encode = eforest.encode(test_images[:, :, c])
x_decode = eforest.decode(x_encode)
results[mi, :, :, c] = x_decode
rheads = ["origin", "supervised", "unsupervised"]
test_images = test_images.reshape(1, 10, 32, 32, 3).astype(np.uint8)
results = results.reshape(2, 10, 32, 32, 3).astype(np.uint8)
fig = plot_cifar10(rheads, np.vstack((test_images, results)))
plt.show()
import IPython
IPython.embed()
