def nn(load_nn=True):
"""
Output (if `load_nn` is True)
-----------------------------
Running 'nn'
Loading data ...
Loading NN ...
Evaluating NN ... Elapsed time: 6.584 sec
Test accuracy 0.9896 (error 1.04%)
"""
print "Running 'nn'"
print "Loading data ..."
X_train, y_train = load_mnist(mode='train', path='data/')
X_test, y_test = load_mnist(mode='test', path='data/')
X_train /= 255.
X_test /= 255.
y_test = one_hot(y_test)
if load_nn:
print "Loading NN ..."
nn = load_model('models/nn.json')
else:
nn = _train_nn(X_train.copy(), y_train.copy())
print_inline("Evaluating NN ... ")
with Stopwatch(verbose=True):
y_pred = nn.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print "\nTest accuracy {0:.4f} (error {1:.2f}%)".format(acc, 100. * (1. - acc))
def knn_without_nn():
"""
Possible output
---------------
Running 'knn_without_nn'
Loading data ...
Training PCA ... Elapsed time: 10.642 sec
Applying augmentation ... Elapsed time: 252.943 sec
Transforming the data ... Elapsed time: 126.107 sec
Building k-d tree ... Elapsed time: 8.022 sec
Evaluating k-NN ... Elapsed time: 127.265 sec
Test accuracy 0.9794 (error 2.06%)
"""
print "Running 'knn_without_nn'"
print "Loading data ..."
X_train, y_train = load_mnist(mode='train', path='data/')
X_test, y_test = load_mnist(mode='test', path='data/')
X_train /= 255.
X_test /= 255.
print_inline("Training PCA ... ")
with Stopwatch(verbose=True):
pca = PCA(n_components=35, whiten=True).fit(X_train)
aug = RandomAugmentator(transform_shape=(28, 28), random_seed=1337)
aug.add('RandomRotate', angle=(-7., 10.))
aug.add('RandomGaussian', sigma=(0., 0.5))
aug.add('RandomShift', x_shift=(-1, 1), y_shift=(-1, 1))
aug.add('Dropout', p=(0., 0.2))
print_inline("Applying augmentation ... ")
with Stopwatch(verbose=True):
X_train = aug.transform(X_train, 8)
y_train = np.repeat(y_train, 9)
print_inline("Transforming the data ... ")
with Stopwatch(verbose=True):
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)
z = pca.explained_variance_ratio_[:35]
z /= sum(z)
alpha = 11.6
X_train *= np.exp(alpha * z)
X_test *= np.exp(alpha * z)
knn = KNNClassifier(algorithm='kd_tree', k=3, p=2, weights='uniform')
print_inline("Building k-d tree ... ")
with Stopwatch(verbose=True):
knn.fit(X_train, y_train)
print_inline("Evaluating k-NN ... ")
with Stopwatch(verbose=True):
y_pred = knn.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print "\nTest accuracy {0:.4f} (error {1:.2f}%)".format(
acc, 100. * (1. - acc))
def knn(load_nn=True):
"""
Possible output (if `load_nn` is True)
--------------------------------------
Running 'knn'
Loading data ...
Loading NN ...
Extracting feature vectors ... Elapsed time: 11.017 sec
Building k-d tree ... Elapsed time: 0.186 sec
Evaluating k-NN ... Elapsed time: 28.483 sec
Test accuracy 0.9887 (error 1.13%)
"""
print "Running 'knn'"
print "Loading data ..."
X_train, y_train = load_mnist(mode='train', path='data/')
X_test, y_test = load_mnist(mode='test', path='data/')
X_train /= 255.
X_test /= 255.
if load_nn:
print "Loading NN ..."
nn = load_model('models/nn.json')
else:
nn = _train_nn(X_train.copy(), y_train.copy())
print_inline("Extracting feature vectors ... ")
with Stopwatch(verbose=True):
nn.forward_pass(X_train)
X_train = leaky_relu(nn.layers[13]._last_input)
nn.forward_pass(X_test)
X_test = leaky_relu(nn.layers[13]._last_input)
knn = KNNClassifier(algorithm='kd_tree', k=3, p=2, weights='uniform')
print_inline("Building k-d tree ... ")
with Stopwatch(verbose=True):
knn.fit(X_train, y_train)
print_inline("Evaluating k-NN ... ")
with Stopwatch(verbose=True):
y_pred = knn.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print "\nTest accuracy {0:.4f} (error {1:.2f}%)".format(
acc, 100. * (1. - acc))
def gp(load_nn=True):
"""
Output (if `load_nn` is True)
-----------------------------
Running 'gp'
Loading data ...
Loading NN ...
Extracting feature vectors ... Elapsed time: 1.932 sec
Training GP ... Elapsed time: 403.602 sec
Evaluating GP ... Elapsed time: 22.021 sec
Test accuracy 0.9839 (error 1.61%)
[!] 1.59% if mean is subtracted (lines 394, 395)
"""
print "Running 'gp'"
print "Loading data ..."
X_train, y_train = load_mnist(mode='train', path='data/')
X_test, y_test = load_mnist(mode='test', path='data/')
X_train /= 255.
X_test /= 255.
y_test = one_hot(y_test)
if load_nn:
print "Loading NN ..."
nn = load_model('models/nn.json')
else:
nn = _train_nn(X_train.copy(), y_train.copy())
tts = TrainTestSplitter(shuffle=True, random_seed=1337)
indices, _ = tts.split(y_train, train_ratio=800./60000., stratify=True) # 794
X_train = X_train[indices]
y_train = one_hot(y_train[indices])
print_inline("Extracting feature vectors ... ")
with Stopwatch(verbose=True):
nn.forward_pass(X_train)
X_train = leaky_relu(nn.layers[13]._last_input)
nn.forward_pass(X_test)
X_test = leaky_relu(nn.layers[13]._last_input)
X_train = StandardScaler(with_std=False).fit_transform(X_train)
X_test = StandardScaler(with_std=False).fit_transform(X_test)
gp = GPClassifier(algorithm='cg',
kernel='rbf',
kernel_params=dict(
sigma=0.4217,
gamma=0.0008511
),
sigma_n=0.,
max_iter=10000,
tol=1e-7,
cg_tol=1e-7,
n_samples=2000,
random_seed=1337)
print_inline("Training GP ... ")
with Stopwatch(verbose=True):
gp.fit(X_train, y_train)
print_inline("Evaluating GP ... ")
with Stopwatch(verbose=True):
y_pred = gp.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print "\nTest accuracy {0:.4f} (error {1:.2f}%)".format(acc, 100. * (1. - acc))
def logreg(load_nn=True):
"""
Output (if `load_nn` is True)
-----------------------------
Running 'logreg'
Loading data ...
Loading NN ...
Extracting feature vectors ... Elapsed time: 12.054 sec
Training LogReg ... Total number of parameters: 1290
Train on 49999 samples, validate on 10001 samples
Epoch 1/750 early stopping after 50 ................................
elapsed: 0.70 sec - loss: 0.009 - acc.: 0.9989 - val. loss: 0.008 - val. acc.: 0.9988
Epoch 2/750 early stopping after 50 ................................
elapsed: 2.13 sec - loss: 0.008 - acc.: 0.9991 - val. loss: 0.009 - val. acc.: 0.9986
Epoch 3/750 early stopping after 50 ................................
elapsed: 3.67 sec - loss: 0.008 - acc.: 0.9991 - val. loss: 0.009 - val. acc.: 0.9986
(...)
Epoch 89/750 early stopping after 3 ................................
elapsed: 59.28 sec - loss: 0.009 - acc.: 0.9992 - val. loss: 0.014 - val. acc.: 0.9979
Epoch 90/750 early stopping after 2 ................................
elapsed: 59.92 sec - loss: 0.009 - acc.: 0.9992 - val. loss: 0.015 - val. acc.: 0.9977
Epoch 91/750 early stopping after 1 ................................
elapsed: 60.57 sec - loss: 0.009 - acc.: 0.9994 - val. loss: 0.013 - val. acc.: 0.9985
Elapsed time: 60.960 sec
Evaluating LogReg ... Elapsed time: 0.057 sec
Test accuracy 0.9899 (error 1.01%)
"""
print "Running 'logreg'"
print "Loading data ..."
X_train, y_train = load_mnist(mode='train', path='data/')
X_test, y_test = load_mnist(mode='test', path='data/')
X_train /= 255.
X_test /= 255.
y_test = one_hot(y_test)
if load_nn:
print "Loading NN ..."
nn = load_model('models/nn.json')
else:
nn = _train_nn(X_train.copy(), y_train.copy())
print_inline("Extracting feature vectors ... ")
with Stopwatch(verbose=True):
nn.forward_pass(X_train)
X_train = leaky_relu(nn.layers[13]._last_input)
nn.forward_pass(X_test)
X_test = leaky_relu(nn.layers[13]._last_input)
tts = TrainTestSplitter(shuffle=True, random_seed=1337)
train, val = tts.split(y_train, train_ratio=50005./60000., stratify=True)
X_val = X_train[val]
X_train = X_train[train]
y_val = one_hot(y_train[val])
y_train = one_hot(y_train[train])
logreg = LogisticRegression(n_batches=32,
L2=0.000316,
random_seed=1337,
optimizer_params=dict(
learning_rate=0.001,
max_epochs=750,
plot=False,
early_stopping=50,
verbose=True))
print_inline("Training LogReg ... ")
with Stopwatch(verbose=True):
logreg.fit(X_train, y_train, X_val=X_val, y_val=y_val)
print_inline("Evaluating LogReg ... ")
with Stopwatch(verbose=True):
y_pred = logreg.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print "\nTest accuracy {0:.4f} (error {1:.2f}%)".format(acc, 100. * (1. - acc))
def _train_nn(X, y):
"""
Parameters
----------
X : training data
y : training labels (not one-hot encoded!)
Output
------
Running '_train_nn'
Applying augmentation ... Elapsed time: 112.495 sec
Training NN ...
Total number of parameters: 3142008
Train on 290000 samples, validate on 10000 samples
Epoch 1/42 ..........................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
elapsed: 327.3 sec - loss: 0.850 - acc.: 0.8904 - val. loss: 0.386 - val. acc.: 0.8889
(...)
elapsed: 15714 sec - loss: 0.013 - acc.: 0.9971 - val. loss: 0.040 - val. acc.: 0.9937
Epoch 42/42 ..........................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
......................................................................................
elapsed: 16100 sec - loss: 0.013 - acc.: 0.9980 - val. loss: 0.038 - val. acc.: 0.9948
Left '_train_nn'
"""
print "Running '_train_nn'"
aug = RandomAugmentator(transform_shape=(28, 28), random_seed=1337)
aug.add('RandomRotate', angle=(-5., 7.))
aug.add('RandomGaussian', sigma=(0., 0.5))
aug.add('RandomShift', x_shift=(-1, 1), y_shift=(-1, 1))
print_inline("Applying augmentation ... ")
with Stopwatch(verbose=True):
X = X.astype(np.float32)
X = aug.transform(X, 4)
y = np.repeat(y, 5)
train, test = TrainTestSplitter(shuffle=True, random_seed=1337).split(y, train_ratio=29./30.)
X_train = X[train]
y_train = y[train]
y_train = one_hot(y_train)
X_val = X[test]
y_val = y[test]
y_val = one_hot(y_val)
nn = NNClassifier(layers=[
FullyConnected(1337),
Activation('leaky_relu'),
Dropout(0.05),
FullyConnected(911),
Activation('leaky_relu'),
Dropout(0.1),
FullyConnected(666),
Activation('leaky_relu'),
Dropout(0.),
FullyConnected(333),
Activation('leaky_relu'),
Dropout(0.),
FullyConnected(128),
Activation('leaky_relu'),
Dropout(0.),
FullyConnected(10),
Activation('softmax')
],
n_batches=1024,
shuffle=True,
random_seed=1337,
save_weights=False,
optimizer_params=dict(
max_epochs=42,
# early_stopping=12,
verbose=True,
plot=False,
# plot_dirpath='tmp/learning_curves{0}/'.format(i),
learning_rate=5e-5
))
print "Training NN ..."
nn.fit(X_train, y_train, X_val=X_val, y_val=y_val)
print "Saving NN to file ..."
nn.save('models/nn.json')
print "Left '_train_nn'"
return nn