def l2reg_model(cnt_features):
return Model(network=Sequence(cnt_features=cnt_features,
layers=[
Dense(20),
ReLu(),
Dense(3),
ReLu(),
Dense(1),
],
classifier=SigmoidBinaryClassifier(),
regularizer=L2Regularizer(lambd=0.7)),
training=batch_gd)
def model(cnt_features):
return Model(network=Sequence(
cnt_features=cnt_features,
layers=[
Dense(5),
ReLu(),
Dense(2),
ReLu(),
Dense(1),
],
classifier=SigmoidBinaryClassifier(),
))
def default_model(cnt_features):
return Model(network=Sequence(
cnt_features=cnt_features,
layers=[
Dense(20),
ReLu(),
Dense(3),
ReLu(),
Dense(1),
],
classifier=SigmoidBinaryClassifier(),
),
training=batch_gd)
def model(cnt_features):
return Model(network=Sequence(
cnt_features=cnt_features,
layers=[
Dense(25),
ReLu(),
Dense(12),
ReLu(),
Dense(6),
],
classifier=SoftmaxClassifier(classes=6),
),
training=MiniBatchTraining(batch_size=32,
optimizer=AdamOptimizer(0.0001),
listener=CostListener()))