import numpy as np import dataset as ds from neural_networks import NeuralNetwork from layers import InputLayer, OutputLayer, DenseLayer from functions._init_functions import init_functions from functions._activation_functions import activation_functions, activation_functions_derivatives from functions._loss_functions import loss_functions import plot as plt data = ds.MLCupDataset() data = ds.MLCupDataset() model = NeuralNetwork() model.add(InputLayer(10)) model.add(DenseLayer(50, fanin=10, activation="sigmoid")) model.add(DenseLayer(30, fanin=50, activation="sigmoid")) model.add(OutputLayer(2, fanin=30)) # configuration 322, line 324 model.compile(1143, 600, 0.03, None, 0.000008, 0.3, "mean_squared_error") loss = model.fit(data.train_data_patterns, data.train_data_targets) print(loss[-1]) plt.plot_loss(loss)
fp = open(filepath, "w")
config = 0
for epoch in epochs:
for lr in learning_rates:
for reg in regularizations:
for alpha in momentums:
mean_loss = 0
mean_validation = 0
for i in range(k):
model = NeuralNetwork()
model.add(InputLayer(10))
model.add(DenseLayer(50, fanin=10))
model.add(DenseLayer(30, fanin=50))
model.add(OutputLayer(2, fanin=30))
model.compile(size, epoch, lr / size, None, reg, alpha,
"mean_squared_error")
(train, val) = data.kfolds(index=i, k=k)
mean_loss = mean_loss + model.fit(train[0], train[1])[-1]
mean_validation = mean_validation + model.evaluate(
val[0], val[1])
fp.write("{}, {}, {}, {}, {}, {}, {}\n".format(
config, epoch, lr, reg, alpha, mean_loss / k,
mean_validation / k))
config = config + 1
fp.close()
import numpy as np
import dataset as ds
from neural_networks import NeuralNetwork
from layers import InputLayer, OutputLayer, DenseLayer
import matplotlib.pyplot as plt
data = ds.MonksDataset()
my_model = NeuralNetwork()
my_model.add(InputLayer(17))
my_model.add(DenseLayer(10, fanin=17, activation="sigmoid"))
my_model.add(OutputLayer(1, fanin=10, activation="sigmoid"))
my_model.compile(122, 600, 0.075, None, 0.0001, 0, "mean_squared_error")
(loss, test_loss, accuracy, test_accuracy) = my_model.fit_monks(
data.train_data_patterns, data.train_data_targets, data.test_data_patterns,
data.test_data_targets)
print("Loss: {}".format(loss[-1]))
print("Test Loss: {}".format(test_loss[-1]))
print("Accuracy: {}".format(accuracy[-1]))
print("Test accuracy: {}".format(test_accuracy[-1]))
plot1 = plt.figure(1)
plt.plot(loss)
plt.plot(test_loss, "--")
plot2 = plt.figure(2)