def feature_test_mnist(verbose=True):
print("... loading date")
# load train data
mnist = fetch_mldata('MNIST original')
X_origin = mnist.data
y = mnist.target
target_names = np.unique(y)
# standardize
X_origin = X_origin.astype(np.float64)
X_origin /= X_origin.max()
print("--- done")
print("... encoding with denoising auto-encoder")
# get feature & create input
ae = AutoEncoder(X=X_origin,
hidden_size=22 * 22,
activation_function=T.nnet.sigmoid,
output_function=T.nnet.sigmoid)
ae.train(n_epochs=5, mini_batch_size=20)
X = ae.get_hidden(data=X_origin)[0]
print("--- done")
# get classifier
clf = nn.NN(ni=X.shape[1],
nh=int(0.16 * X.shape[1]),
no=len(target_names),
learning_rate=0.3,
inertia_rate=0.12,
corruption_level=0.0,
epochs=150000)
# cross validation
skf = StratifiedKFold(y, n_folds=3)
scores = np.zeros(len(skf))
for i, (train_index, test_index) in enumerate(skf):
# train the model
clf.fit(X[train_index], y[train_index])
# get score
score = clf.score(X[test_index], y[test_index])
scores[i] = score
# stdout of the score
if verbose is True:
print(scores)
print("... plotting the autoencoder hidden layer")
# get tiled image
p = np.random.randint(0, len(X), 400)
tile = tile_raster_images(X[p], (22, 22), (20, 20),
scale_rows_to_unit_interval=True,
output_pixel_vals=True,
tile_spacing=(1, 1))
# save tiled data's image
plt.axis('off')
plt.title('MNIST dataset')
plt.imshow(tile, cmap=plt.cm.gray_r)
plt.savefig('../output/tiled_autoencoder_hidden_mnist.png')
print("--- done")
print("... saving the results")
data = {
'scores': scores,
'hidden layer': X,
}
with gzip.open('../output/feature_test_mnist.pkl.gz', 'wb') as f:
cPickle.dump(data, f)
print("--- done")
