def main():
train_loader = Data(train_path, shuffle=True, split=0.2)
pca = train_loader.dataloading(batch_size=batch_size,
pca=PCA(n_components=pca_components))
test_loader = Data(test_path, shuffle=False, test=True)
test_loader.dataloading(pca=pca)
model = NN(in_dim=pca_components + 1,
n_cls=3,
neurons=[256],
lr=lr,
hidden_activation='sigmoid',
load_weight=False,
save_weight=False)
info = {
'train_loss': [],
'train_acc': [],
'val_loss': [],
'val_acc': [],
'EPOCHS': EPOCHS
}
for epoch in range(EPOCHS):
cache = np.zeros(4)
itr = 0
for train_data in train_loader:
x_train, y_train, x_val, y_val = train_data
y_train = to_onehot(y_train, 3)
y_val = to_onehot(y_val, 3)
train_loss, train_acc = model.train(x_train, y_train)
val_loss, val_acc, pred = model.predict(x_val, y_val)
cache[0] += train_loss
cache[1] += train_acc
cache[2] += val_loss
cache[3] += val_acc
itr += 1
cache /= itr
info['train_loss'].append(cache[0])
info['train_acc'].append(cache[1])
info['val_loss'].append(cache[2])
info['val_acc'].append(cache[3])
print(
'EPOCH:{:05d}/{:05d} train_loss: {:.5f} train_acc: {:.4f} val_loss: {:.5f} val_acc: {:.4f}'
.format(epoch + 1, EPOCHS, *cache.tolist()))
plot_info(**info)
train_loader.set_batch_size(2000)
for train_data in train_loader:
x_train, y_train, x_val, y_val = train_data
y_train = to_onehot(y_train, 3)
plot_decision_region(x_train,
y_train,
model,
train_loader.CLASSES,
title='training')
for test_data in test_loader:
x_test, y_test = test_data
y_test = to_onehot(y_test, 3)
plot_decision_region(x_test,
y_test,
model,
train_loader.CLASSES,
title='testing')
test_loss, test_acc, pred = model.predict(x_test, y_test)
print('test_loss: {:.5f} test_acc: {:.4f}'.format(test_loss, test_acc))
# Plot the training points
plt.scatter(X[:, 0], X[:, 1], c=t.flatten())
plt.xlabel('Petal length')
plt.ylabel('Petal width')
plt.title('Real Data', fontsize=16)
plt.show()
# Build neural network
model = NN()
model.add_layer(units=10, activation=np.tanh, initialization=np.random.normal)
model.add_layer(units=5, activation=np.tanh, initialization=np.random.normal)
model.add_layer(units=2, activation=np.tanh, initialization=np.random.normal)
model.add_layer(1, activation=model.sigmoid)
model.train(X, t, iterations=5000, learn_rate=0.0001) # 5000
prediction = model.predict(X)
# Plot error
plt.plot(model.history['epoch'],
model.history['error_train'],
label='Training error')
plt.xlabel('Epoch')
plt.ylabel('Error')
plt.legend()
plt.show()
# Plot prediction
plt.scatter(X[:, 0], X[:, 1], c=np.round(prediction, 0).flatten())
plt.xlabel('Petal length')
plt.ylabel('Petal width')
plt.title('Predictions', fontsize=16)
