import neural_network as network
if __name__ == "__main__":
batch, train_percent = 32, 80
X, y = network.extract("mnist", "MNIST")
Y = [element.item() for element in y]
output = len(list(set(Y)))
dimension = len(X[0].flatten())
trainer, tester, validater = network.partition(X, y, output, batch,
train_percent)
episodes = int(1e2)
learning_rate = 5e-3
neurons = [dimension, 40, 50, 50, 20, output]
activity = ["relu", "relu", "relu", "relu", ""]
cost = "crossentropy"
optimizer = "adam"
model, error, accuracy = network.learn(trainer, neurons, activity,
learning_rate, episodes, cost,
optimizer)
network.plot(error, "forestgreen",
"accumulated_error_over_each_epoch_mnist_classification",
"Episode", "Error")
network.plot(accuracy, "mediumvioletred",
"accuracy_over_each_epoch_mnist_classification", "Episode",
"Learning accuracy")
network.test(model, tester, output)
import neural_network as network
if __name__ == "__main__":
batch, output = 10, 3
train_percent = 80
episodes = int(1e3)
learning_rate = 1e-2
encoding = [("setosa", 0), ("versicolor", 1), ("virginica", 2)]
X, y = network.extract("iris.csv",
encoding=encoding,
output=output,
label="species")
dimension = len(X[0])
neurons = [dimension, 10, 20, 20, 5, output]
trainer, tester, validater = network.partition(X, y, output, batch,
train_percent)
activity = ["relu", "relu", "relu", "relu", ""]
cost = "crossentropy"
optimizer = "adam"
model, error, accuracy = network.learn(trainer, neurons, activity,
learning_rate, episodes, cost,
optimizer)
network.plot(
error, "forestgreen",
"accumulated_errors_over_each_epoch_multilabel_classification",
"Episode", "Error")
network.plot(accuracy, "mediumvioletred",
"accuracy_over_each_epoch_multilabel_classification",
"Episode", "Learning accuracy")
network.test(model, tester, output)
import neural_network as network
if __name__ == "__main__":
batch, output = 50, 1
train_percent = 80
episodes = int(1e3)
learning_rate = 1e-1
encoding = [("b", 0), ("g", 1)]
X, y = network.extract("ionosphere.txt", encoding = encoding, output = output)
trainer, tester, validater = network.partition(X, y, output, batch, train_percent)
dimension = len(X[0])
neurons = [dimension, output]
activity = ["sigmoid"]
cost = "mse"
optimizer = "adam"
model, error, accuracy = network.learn(trainer, neurons, activity, learning_rate, episodes, cost, optimizer)
network.plot(error, "forestgreen", "accumulated_error_over_each_epoch_binary_classification", "Episode", "Error")
network.plot(accuracy, "mediumvioletred", "accuracy_over_each_epoch_binary_classification", "Episode", "Learning accuracy")
network.test(model, tester, output)