def main():
# loading training data
data = pd.read_csv('../input/train.csv')
X_tr = data.values[:, 1:].astype(float)
X_tr = normalizeX(X_tr)
y_tr = data.values[:, 0]
# training neural network
num_hidden = 25
lambda_ = 1 # regularization parameter
maxiter = 50 # max number of iterations
print('Training: size of hidden layer={}, lambda={}, maximum iterations={}'
.format(num_hidden, lambda_, maxiter))
recognizer = digit_recognizer(lambda_=lambda_, num_hidden=num_hidden, maxiter=maxiter)
recognizer.fit(X_tr, y_tr)
y_val = recognizer.predict(X_tr)
print('Accuracy={}\n'.format(accuracy_score(y_tr, y_val)))
# training neural network
num_hidden = 25
lambda_ = 1
maxiter = 500
print('Training: size of hidden layer={}, lambda={}, maximum iterations={}'
.format(num_hidden, lambda_, maxiter))
recognizer = digit_recognizer(lambda_=lambda_, num_hidden=num_hidden, maxiter=maxiter)
recognizer.fit(X_tr, y_tr)
y_val = recognizer.predict(X_tr)
print('Accuracy={}'.format(accuracy_score(y_tr, y_val)))
def main():
# loading training data
data = pd.read_csv('../input/train.csv')
X_tr = data.values[:, 1:].astype(float)
X_tr = normalizeX(X_tr)
y_tr = data.values[:, 0]
param_range = np.array(
[0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0])
train_scores, test_scores = validation_curve(
digit_recognizer(maxiter=100), X_tr, y_tr,
param_name='lambda_', param_range=param_range,
cv=3, scoring='accuracy', n_jobs=2)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.title("Validation Curve with Neural Network")
plt.xlabel("$\lambda$")
plt.ylabel("Score")
plt.ylim(0.85, 1.05)
plt.semilogx(param_range, train_scores_mean, label="Training score", color="r")
plt.fill_between(param_range, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.2, color="r")
plt.semilogx(param_range, test_scores_mean, label="Cross-validation score",
color="g")
plt.fill_between(param_range, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.2, color="g")
plt.legend(loc="best")
plt.show()