def get_network_mse_1(x_all,
y_all,
num_of_neurons=(2, 25, 2),
activation='relu',
lr=0.001,
momentum_coef=0.0,
weight_decay=0.0,
p_dropout=0.0,
num_of_epochs=100,
val_split=0.2,
verbose=0):
"""
model with 1 hidden layer, loss is MSE
"""
mse = LossMSE()
model = Sequential()
model.add(
Linear(out=num_of_neurons[1],
input_size=num_of_neurons[0],
activation='relu'))
model.add(Dropout(prob=p_dropout))
model.add(Linear(out=num_of_neurons[2], activation=activation))
model.loss = mse
sgd = SGD(lr, momentum_coef, weight_decay=weight_decay)
report = sgd.train(model,
x_all,
y_all,
num_of_epochs,
val_split=val_split,
verbose=verbose)
return model, report
def get_network_ce_1(x_all,
y_all,
num_of_neurons=(2, 25, 2),
activation='relu',
lr=0.1,
momentum_coef=0.0,
weight_decay=0.0,
p_dropout=0.0,
num_of_epochs=100,
val_split=0.2,
verbose=0):
"""
1 hidden layer, CE
"""
ce = LossCrossEntropy()
model = Sequential()
model.add(
Linear(out=num_of_neurons[1],
input_size=num_of_neurons[0],
activation=activation))
model.add(Dropout(prob=p_dropout))
model.add(Linear(out=num_of_neurons[2], activation='softmax'))
model.loss = ce
sgd = SGD(lr, momentum_coef, weight_decay=weight_decay)
# initialize SGD optimizer with given learning rate, momentum coefficient and weight decay parameter
sgd = SGD(lr, momentum_coef, weight_decay)
# train model, take report
report = sgd.train(model,
x_all,
y_all,
num_of_epochs,
val_split=val_split,
verbose=verbose)
# return model and report
return model, report
def get_network_ce_4(x_all,
y_all,
num_of_neurons=(2, 25, 25, 25, 2),
activation='relu',
lr=0.1,
momentum_coef=0.0,
weight_decay=0.0,
p_dropout=0.0,
num_of_epochs=100,
val_split=0.2,
verbose=0):
"""
model with 3 hidden layers, loss is CE
"""
ce = LossCrossEntropy()
model = Sequential()
model.add(
Linear(out=num_of_neurons[1],
input_size=num_of_neurons[0],
activation=activation))
model.add(Dropout(prob=p_dropout))
model.add(Linear(out=num_of_neurons[2], activation=activation))
model.add(Dropout(prob=p_dropout))
model.add(Linear(out=num_of_neurons[3], activation=activation))
model.add(Dropout(prob=p_dropout))
model.add(Linear(out=num_of_neurons[4], activation='softmax'))
model.loss = ce
sgd = SGD(lr, momentum_coef, weight_decay=weight_decay)
report = sgd.train(model,
x_all,
y_all,
num_of_epochs,
val_split=val_split,
verbose=verbose)
return model, report
def get_network(x_all,
y_all,
num_of_hidden_layers=3,
loss='ce',
num_of_neurons=(2, 25, 25, 25, 2),
activation='relu',
lr=0.1,
momentum_coef=0.0,
weight_decay=0.0,
p_dropout=0.0,
num_of_epochs=100,
val_split=0.2,
verbose=0):
"""
creates model with given parameters
x_all - features
y_all - targets
num_of_hidden_layers - int, number of hidden layers in model
loss - 'ce' for Cross Entropy, 'mse' for Mean Squared Error
num_of_neurons - tuple of ints with size num_of_hidden_layers + 2,
first element is number of features in x_all and last element is number of possible targets
activation - 'relu' for ReLu, 'tanh' for Tanh
lr - float, learning rate
momentum_coef - float in range (0, 1), momentum coefficient
weight_decay - float, L2-regularization parameter
p_dropout - float in range [0, 1), probability of dropout
num_of_epochs - int, number of epochs
val_split - float in range [0, 1), ratio of validation set
verbose - 0 or 1, for printing out results
"""
# set loss and last activation
if loss == 'ce':
loss = LossCrossEntropy()
last_activation = 'softmax'
else:
loss = LossMSE()
last_activation = activation
# initialize empty Sequential as model
model = Sequential()
# add linear layers with given activations and dropout layers after linear modules with given p_dropout
if num_of_hidden_layers > 0:
model.add(
Linear(out=num_of_neurons[1],
input_size=num_of_neurons[0],
activation=activation))
model.add(Dropout(prob=p_dropout))
for i in range(num_of_hidden_layers - 1):
model.add(Linear(out=num_of_neurons[i + 2], activation=activation))
model.add(Dropout(prob=p_dropout))
model.add(Linear(out=num_of_neurons[-1], activation=last_activation))
else:
model.add(
Linear(out=num_of_neurons[-1],
input_size=num_of_neurons[0],
activation=last_activation))
# set loss of model
model.loss = loss
sgd = SGD(lr, momentum_coef, weight_decay=weight_decay)
report = sgd.train(model,
x_all,
y_all,
num_of_epochs,
val_split=val_split,
verbose=verbose)
return model, report
