def test_single_activation_function(activation):
model = NeuralNetwork(
optimizer=StaticGradientDescent(
learning_rate=default_parameters['learning_rate']),
loss=CrossEntropy(),
layers=[
Dense(layer_size=50,
activation_func=activation,
weight_initializer=XavierInitializer()),
Dense(layer_size=10,
activation_func=Softmax(),
weight_initializer=XavierInitializer())
],
callbacks=[
LoggerCallback(),
PlotCallback(f'./results/activations/{activation.get_name()}')
])
model.fit(x_train=X_train,
y_train=y_train,
x_val=X_val,
y_val=y_val,
epochs=default_parameters['epochs'],
batch_size=default_parameters['batch_size'])
model.test(X_test, y_test)
def test_single_cost_and_last_layer(cost_func, last_layer):
model = NeuralNetwork(
optimizer=StaticGradientDescent(default_parameters['learning_rate']),
loss=cost_func,
layers=[
Dense(layer_size=50,
activation_func=ReLu(),
weight_initializer=XavierInitializer()),
Dense(layer_size=10,
activation_func=last_layer,
weight_initializer=XavierInitializer())
],
callbacks=[
LoggerCallback(),
PlotCallback(
f'./lab_3/cost/func={cost_func.get_name()}&last_layer={last_layer.get_name()}'
)
])
model.fit(x_train=X_train,
y_train=y_train,
x_val=X_val,
y_val=y_val,
epochs=default_parameters['epochs'],
batch_size=default_parameters['batch_size'])
model.test(X_test, y_test)
def test_single_optimizer(optimizer):
model = NeuralNetwork(
optimizer=optimizer,
loss=CrossEntropy(),
layers=[
Dense(layer_size=50,
activation_func=ReLu(),
weight_initializer=XavierInitializer()),
Dense(layer_size=10,
activation_func=Softmax(),
weight_initializer=XavierInitializer())
],
callbacks=[
LoggerCallback(),
PlotCallback(f'./lab_3/optimizers/{optimizer.get_name()}')
])
model.fit(x_train=X_train,
y_train=y_train,
x_val=X_val,
y_val=y_val,
epochs=default_parameters['epochs'],
batch_size=default_parameters['batch_size'])
model.test(X_test, y_test)
def __init__(self, num_of_filters, kernel, stride=(1, 1), kernel_initializer=XavierInitializer(),
bias_initializer=ZeroInitializer(),
activation_func=ReLu(), layer_name='Convo2D'):
super().__init__(layer_name)
self._num_of_filters = num_of_filters
self._kernel = kernel
self._stride = stride
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._activation_func = activation_func
self._z = None
def __init__(self,
layer_size,
weight_initializer=XavierInitializer(),
activation_func=Sigmoid(),
bias_initializer=ZeroInitializer(),
layer_name='dense'):
super().__init__(layer_name)
self._weight_initializer = weight_initializer
self._activation_func = activation_func
self._bias_initializer = bias_initializer
self._layer_size = layer_size
self._z = None
from initializers.he_initializer import HeInitializer
from initializers.normal_initializer import NormalInitializer
from initializers.xavier_initializer import XavierInitializer
from tests.initializer_tests import perform_initializer_test
initializers = [
XavierInitializer(gain=6),
# HeInitializer(),
# NormalInitializer(loc=0, scale=1, a=10)
]
perform_initializer_test(initializers)
from callbacks.save_best_callback import SaveBestCallback
from initializers.xavier_initializer import XavierInitializer
from layers.dense import Dense
from losses.crossentropy import CrossEntropy
from losses.mse import MeanSquaredError
from metrics.normal_accuracy import NormalAccuracy
from models.model import NeuralNetwork
from optimizers.gradient_descent_static import StaticGradientDescent
from preprocessing.data_loader import get_data
model = NeuralNetwork(optimizer=StaticGradientDescent(learning_rate=0.01),
loss=MeanSquaredError(),
layers=[
Dense(layer_size=50,
activation_func=ReLu(),
weight_initializer=XavierInitializer()),
Dense(layer_size=10,
activation_func=Softmax(),
weight_initializer=XavierInitializer())
],
callbacks=[
SaveBestCallback('./results/01_10_2019_13:00',
'best_model.pkl'),
LoggerCallback(),
PlotCallback('./results/batch_size/', 'test.pkl')
])
(X_train, y_train), (X_val, y_val), (X_test, y_test) = get_data()
model.fit(x_train=X_train,
y_train=y_train,