def gradient(self, x):
return 1 / (1 + torch.exp(-x))
if __name__ == '__main__':
data = load_digits()
X = normalization(torch.tensor(data.data, dtype=torch.double))
y = torch.tensor(data.target)
# Convert the nominal y values to binary
y = to_categorical(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
random_state=1)
# MLP
clf = MultiLayerPerceptron(n_hidden=16,
n_iterations=1000,
learning_rate=0.01,
activation_function_hidden_layer=Sigmoid(),
activation_function_output_layer=Softmax())
clf.fit(X_train, y_train)
y_pred = torch.argmax(clf.predict(X_test), dim=1)
y_test = torch.argmax(y_test, dim=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
from MLP import MultiLayerPerceptron
from fileLoader import load_file
import numpy as np
df = load_file('mnist_data.npz')
X_train, y_train, X_test, y_test = df.get_data()
mlp = MultiLayerPerceptron(hidden_units=100,
L2=.01,
epochs=200,
lr=.0005,
shuffle=True,
mini_batch_size=100,
seed=1)
mlp.fit(X_train[:55000], y_train[:55000])