def test_mlp_classification():
y_train_onehot = one_hot(y_train)
y_test_onehot = one_hot(y_test)
model = NeuralNet(
layers=[
Dense(256, Parameters(init='uniform', regularizers={'W':
L2(0.05)})),
Activation('relu'),
Dropout(0.5),
Dense(128, Parameters(init='normal', constraints={'W':
MaxNorm()})),
Activation('relu'),
Dense(2),
Activation('softmax'),
],
loss='categorical_crossentropy',
optimizer=Adadelta(),
metric='accuracy',
batch_size=64,
max_epochs=25,
)
model.fit(X_train, y_train_onehot)
predictions = model.predict(X_test)
assert roc_auc_score(y_test_onehot[:, 0], predictions[:, 0]) >= 0.95
def clasifier(optimizer):
X, y = make_classification(n_samples=1000, n_features=100, n_informative=75, random_state=1111, n_classes=2,
class_sep=2.5, )
y = one_hot(y)
X -= np.mean(X, axis=0)
X /= np.std(X, axis=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1111)
model = NeuralNet(
layers=[
Dense(128, Parameters(init='uniform')),
Activation('relu'),
Dropout(0.5),
Dense(64, Parameters(init='normal')),
Activation('relu'),
Dense(2),
Activation('softmax'),
],
loss='categorical_crossentropy',
optimizer=optimizer,
metric='accuracy',
batch_size=64,
max_epochs=10,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
return roc_auc_score(y_test[:, 0], predictions[:, 0])
def classification():
# Generate a random binary classification problem.
X, y = make_classification(
n_samples=1000, n_features=100, n_informative=75, random_state=1111, n_classes=2, class_sep=2.5
)
y = one_hot(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1111)
model = NeuralNet(
layers=[
Dense(256, Parameters(init="uniform", regularizers={"W": L2(0.05)})),
Activation("relu"),
Dropout(0.5),
Dense(128, Parameters(init="normal", constraints={"W": MaxNorm()})),
Activation("relu"),
Dense(2),
Activation("softmax"),
],
loss="categorical_crossentropy",
optimizer=Adadelta(),
metric="accuracy",
batch_size=64,
max_epochs=25,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("classification accuracy", roc_auc_score(y_test[:, 0], predictions[:, 0]))
def clasifier(optimizer):
X, y = make_classification(n_samples=1000,
n_features=100,
n_informative=75,
random_state=1111,
n_classes=2,
class_sep=2.5)
y = one_hot(y)
X -= np.mean(X, axis=0)
X /= np.std(X, axis=0)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.15,
random_state=1111)
model = NeuralNet(
layers=[
Dense(128, Parameters(init="uniform")),
Activation("relu"),
Dropout(0.5),
Dense(64, Parameters(init="normal")),
Activation("relu"),
Dense(2),
Activation("softmax"),
],
loss="categorical_crossentropy",
optimizer=optimizer,
metric="accuracy",
batch_size=64,
max_epochs=10,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
return roc_auc_score(y_test[:, 0], predictions[:, 0])
def test_mlp():
y_train_onehot = one_hot(y_train)
y_test_onehot = one_hot(y_test)
model = NeuralNet(
layers=[
Dense(256, Parameters(init="uniform", regularizers={"W": L2(0.05)})),
Activation("relu"),
Dropout(0.5),
Dense(128, Parameters(init="normal", constraints={"W": MaxNorm()})),
Activation("relu"),
Dense(2),
Activation("softmax"),
],
loss="categorical_crossentropy",
optimizer=Adadelta(),
metric="accuracy",
batch_size=64,
max_epochs=25,
)
model.fit(X_train, y_train_onehot)
predictions = model.predict(X_test)
assert roc_auc_score(y_test_onehot[:, 0], predictions[:, 0]) >= 0.95
def test_mlp():
y_train_onehot = one_hot(y_train)
y_test_onehot = one_hot(y_test)
model = NeuralNet(
layers=[
Dense(256, Parameters(init='uniform', regularizers={'W': L2(0.05)})),
Activation('relu'),
Dropout(0.5),
Dense(128, Parameters(init='normal', constraints={'W': MaxNorm()})),
Activation('relu'),
Dense(2),
Activation('softmax'),
],
loss='categorical_crossentropy',
optimizer=Adadelta(),
metric='accuracy',
batch_size=64,
max_epochs=25,
)
model.fit(X_train, y_train_onehot)
predictions = model.predict(X_test)
assert roc_auc_score(y_test_onehot[:, 0], predictions[:, 0]) >= 0.95
def classification():
X, y = make_classification(
n_samples=1000,
n_features=100,
n_informative=75,
random_state=1111,
n_classes=2,
class_sep=2.5,
)
y = one_hot(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.15,
random_state=1111)
model = NeuralNet(
layers=[
Dense(256, Parameters(init='uniform', regularizers={'W':
L2(0.05)})),
Activation('relu'),
Dropout(0.5),
Dense(128, Parameters(init='normal', constraints={'W':
MaxNorm()})),
Activation('relu'),
Dense(2),
Activation('softmax'),
],
loss='categorical_crossentropy',
optimizer=Adadelta(),
metric='accuracy',
batch_size=64,
max_epochs=25,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('classification accuracy',
roc_auc_score(y_test[:, 0], predictions[:, 0]))
from mla.metrics import accuracy
from mla.neuralnet import NeuralNet
from mla.neuralnet.layers import Activation, Convolution, MaxPooling, Flatten, Dropout, Parameters
from mla.neuralnet.layers import Dense
from mla.neuralnet.optimizers import Adadelta
from mla.utils import one_hot
logging.basicConfig(level=logging.DEBUG)
X_train, X_test, y_train, y_test = load_mnist()
# Normalization
X_train /= 255.
X_test /= 255.
y_train = one_hot(y_train.flatten())
y_test = one_hot(y_test.flatten())
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
# Approx. 15-20 min. per epoch
model = NeuralNet(
layers=[
Convolution(n_filters=32,
filter_shape=(3, 3),
padding=(1, 1),
stride=(1, 1)),
Activation('relu'),
Convolution(n_filters=32,
filter_shape=(3, 3),
padding=(1, 1),
stride=(1, 1)),
from mla.neuralnet.layers import Activation, Convolution, MaxPooling, Flatten, Dropout, Parameters
from mla.neuralnet.layers import Dense
from mla.neuralnet.optimizers import Adadelta
from mla.utils import one_hot
logging.basicConfig(level=logging.DEBUG)
# Load MNIST dataset
X_train, X_test, y_train, y_test = load_mnist()
# Normalize data
X_train /= 255.
X_test /= 255.
y_train = one_hot(y_train.flatten())
y_test = one_hot(y_test.flatten())
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
# Approx. 15-20 min. per epoch
model = NeuralNet(
layers=[
Convolution(n_filters=32, filter_shape=(3, 3), padding=(1, 1), stride=(1, 1)),
Activation('relu'),
Convolution(n_filters=32, filter_shape=(3, 3), padding=(1, 1), stride=(1, 1)),
Activation('relu'),
MaxPooling(pool_shape=(2, 2), stride=(2, 2)),
Dropout(0.5),
Flatten(),
Dense(128),
