def NN_fit(train_set, val_set, nn=5, epochs=10, width=10, layers=2):
from NN import NeuralNetwork
last_error = 100
last_predicated = None
fnn = None
for x in range(nn):
nn = NeuralNetwork()
nn.train(
train_set,
val_set,
epochs=epochs,
width=width,
layers=layers,
batch_size=20,
learning_rate=0.001,
)
predicted_y, _ = nn.predict(train_set.X)
logger.info(
f"NN train MSE {mean_squared_error(train_set.Y, predicted_y)}")
predicted_y, _ = nn.predict(val_set.X)
error = mean_squared_error(val_set.Y, predicted_y)
logger.info(f"NN dev MSE {error}")
if error < last_error:
last_error = error
fnn = nn
return fnn
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 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 recognize(src):
nn = NeuralNetwork([784, 250, 10], 'logistic')
img_list = GetCutZip(src)
final_result = ''
for img_array in img_list:
img_array = img_array.flatten()
result_list = nn.predict(img_array)
result = np.argmax(result_list)
final_result = final_result + str(result)
return final_result
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))
x = preprocess(data)
train_x = x[:NUM_TRAIN]
train_y = y[:NUM_TRAIN]
test_x = x[-NUM_TEST:]
test_y = y[-NUM_TEST:]
params = {
'train_inputs': train_x,
'train_targets': train_y,
'layer_dimentions': [SIZE**2, 450, 250, 50, 10],
'learning_rate': 1e-3,
'iterations': 250
}
nn = NN(params)
nn.train()
prediction = nn.predict(test_x)
accuracy = accuracy_score(test_y, prediction)
precision = precision_score(test_y, prediction, average='micro')
recall = recall_score(test_y, prediction, average='micro')
f1 = f1_score(test_y, prediction, average='micro')
conf_matrix = confusion_matrix(test_y, prediction)
print("accuracy:", accuracy)
print("precision:", precision)
print("recall:", recall)
print("f1:", f1)
print("confusion matrix:\n", conf_matrix)