def run():
input_layer_size = 400
hidden_layer_size = 25
num_labels = 10
print('Loading and Visualizing Data...')
X, y = digit_data['X'], digit_data['y']
m = X.shape[0]
# Randomly select 100 data points to display
sel = sp.random.permutation(m)
multiclass.displayData(X[sel, :])
print('Program paused. Press enter to continue.')
raw_input()
Theta1, Theta2 = weights_data['Theta1'], weights_data['Theta2']
neurnet.predict(Theta1, Theta2, X)
print('Program paused. Press enter to continue.')
raw_input()
rp = sp.random.permutation(m)
for i in range(0, m):
pred = neurnet.predict(Theta1, Theta2, sp.asmatrix(X[rp[i], :]))
print("Neural Network Prediction: %d (digit %d)\n" % (pred[0], sp.mod(pred[0], 10)))
def run():
X, y = digit_data['X'], digit_data['y']
m = X.shape[0]
rand_indices = sp.random.permutation(m)
multiclass.displayData(X)
print('Program paused. Press enter to continue.')
raw_input()
print('Training One-vs-All Logistic Regression...')
lamda = 0.1
num_labels = 10
all_theta = multiclass.oneVsAll(X, y, num_labels, lamda)
print('Program paused. Press enter to continue.')
raw_input()
print all_theta.shape
multiclass.predict(all_theta, X, y)
