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 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 regression():
# Generate a random regression problem
X, y = make_regression(n_samples=5000, n_features=25, n_informative=25,
n_targets=1, random_state=100, noise=0.05)
y *= 0.01
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
random_state=1111)
model = NeuralNet(
layers=[
Dense(64, Parameters(init='normal')),
Activation('linear'),
Dense(32, Parameters(init='normal')),
Activation('linear'),
Dense(1),
],
loss='mse',
optimizer=Adam(),
metric='mse',
batch_size=256,
max_epochs=15,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("regression mse", mean_squared_error(y_test, predictions.flatten()))
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 mlp_model(n_actions, batch_size=64):
model = NeuralNet(
layers=[Dense(32), Activation("relu"),
Dense(n_actions)],
loss="mse",
optimizer=Adam(),
metric="mse",
batch_size=batch_size,
max_epochs=1,
verbose=False,
)
return model
def test_mlp():
model = NeuralNet(
layers=[
Dense(16, Parameters(init='normal')),
Activation('linear'),
Dense(8, Parameters(init='normal')),
Activation('linear'),
Dense(1),
],
loss='mse',
optimizer=Adam(),
metric='mse',
batch_size=64,
max_epochs=150,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions.flatten()) < 1.0
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 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]))
y = y[0:items_count]
print(X.shape, y.shape)
# LSTM OR RNN
# rnn_layer = RNN(128, return_sequences=False)
rnn_layer = LSTM(
128,
return_sequences=False,
)
model = NeuralNet(
layers=[
rnn_layer,
# Flatten(),
# TimeStepSlicer(-1),
Dense(X.shape[2]),
Activation('softmax'),
],
loss='categorical_crossentropy',
optimizer=RMSprop(learning_rate=0.01),
metric='accuracy',
batch_size=64,
max_epochs=1,
shuffle=False,
)
for _ in range(25):
model.fit(X, y)
start_index = random.randint(0, len(text) - maxlen - 1)
generated = ''
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),
Activation('relu'),
Dropout(0.5),
Dense(10),
Activation('softmax'),
],
loss='categorical_crossentropy',
optimizer=Adadelta(),
metric='accuracy',
batch_size=128,
max_epochs=3,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(accuracy(y_test, predictions))