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 dataset as ds
from neural_networks import NeuralNetwork
from layers import InputLayer, OutputLayer, DenseLayer
import matplotlib.pyplot as plt
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(1142, 600, 0.03/1142, None, 0.000008, 0.3, "mean_squared_error")
loss = model.fit(data.train_data_patterns, data.train_data_targets)
i = 0
final_loss = []
while i < len(loss):
final_loss.append((loss[i] + loss[i+1])/2)
i += 2
print(final_loss[-1])
print(model.evaluate(data.model_assessment_patterns, data.model_assessment_targets))
plt.plot(final_loss)
plt.show()
model._blind_test(data.test_data_patterns)