def main(filename='data/iris-virginica.txt'):
# Load data
data = read_data('%s/%s' % (filepath, filename))
X, y = data[:, :-1].astype(float), data[:, -1]
class_vec = list(set(y))
K = len(class_vec)
Y = pd.get_dummies(y).astype(int).as_matrix()
# Define parameters
n = X.shape[0]
d = X.shape[1]
#
# # Define layer sizes
print(n, d, K)
layers = [d, 5, K]
model = NeuralNetwork(layers=layers,
num_epochs=1000,
learning_rate=0.10,
alpha=0.9,
activation_func='sigmoid',
epsilon=0.001,
print_details=True)
model.fit(X, Y)
Y_hat = model.predict(X)
accuracy = compute_acc(Y_hat, Y)
print('Model training accuracy:\t%.2f' % (accuracy))
def my_solution2(X_train, X_test, y_train, y_test, hyperparams=None):
if hyperparams == None:
hyperparams = {
'hidden_layer_sizes': (3, ),
'learning_rate': 0.1,
'epoch': 3852,
'momentum': 0.04,
'tol': 1e-10,
'reg_coef': 0
}
if not hasattr(hyperparams, 'batch_size'):
hyperparams['batch_size'] = X_train.shape[0]
# train with self
nn = NeuralNetwork(**hyperparams)
nn.fit(X_train, y_train)
y_pred = nn.predict(X_test)
y_pred = y_pred.argmax(axis=1)
y_test = y_test.argmax(axis=1)
# evaluate
print('My implements2:')
print(classification_report(y_test, y_pred))
return nn, y_pred, y_test
def main(filename='data/iris-virginica.txt'):
# Load data
data = read_data('%s/%s' % (filepath, filename))
X, y = data[:,:-1].astype(float), data[:,-1]
class_vec = list(set(y))
K = len(class_vec)
Y = pd.get_dummies(y).astype(int).as_matrix()
# Define parameters
n = X.shape[0]
d = X.shape[1]
#
# # Define layer sizes
print(n,d,K)
layers = [d, 5, K]
model = NeuralNetwork(layers=layers, num_epochs=1000, learning_rate=0.10, alpha=0.9,
activation_func='sigmoid', epsilon=0.001, print_details=True)
model.fit(X, Y)
Y_hat = model.predict(X)
accuracy = compute_acc(Y_hat, Y)
print('Model training accuracy:\t%.2f' % (accuracy))
from NN import NeuralNetwork from Normalizer import Normalizer import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split cancer = load_breast_cancer() X = cancer['data'] y = cancer['target'] length = len(cancer['feature_names']) nn = NeuralNetwork([length, 30, 20, 10, 5, 1]) X_train, X_test, y_train, y_test = train_test_split(X, y) normalize = Normalizer() normalize.fit(X_train) X_train = normalize.transform(X_train) X_test = normalize.transform(X_test) nn.fit(X_train, y_train, epochs=1000, verbose=False) predictions = nn.predict(X_test) print(nn.cost(predictions, y_test))