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 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 addition_nlp(ReccurentLayer):
X_train, X_test, y_train, y_test = addition_dataset(8, 5000)
print(X_train.shape, X_test.shape)
model = NeuralNet(
layers=[
ReccurentLayer,
TimeDistributedDense(1),
Activation('sigmoid'),
],
loss='mse',
optimizer=Adam(),
metric='mse',
batch_size=64,
max_epochs=15,
)
model.fit(X_train, y_train)
predictions = np.round(model.predict(X_test))
predictions = np.packbits(predictions.astype(np.uint8))
y_test = np.packbits(y_test.astype(np.int))
print(accuracy(y_test, predictions))