def cost(nn_params, num_labels, X, y, input_layer_size, hidden_layer_size, reg_lambda):
nn = NeuralNetwork(input_layer_size, hidden_layer_size, num_labels)
return nn.get_nn_params(nn_params, X, y, reg_lambda)[0]
def gradient(nn_params, num_labels, X, y, input_layer_size, hidden_layer_size, reg_lambda):
nn = NeuralNetwork(input_layer_size, hidden_layer_size, num_labels)
return nn.get_nn_params(nn_params, X, y, reg_lambda)[1].flatten()
n = np.shape(X)[1]
print("Number of training examples: ", m)
print("Number of features: ", n, "\n")
nn = NeuralNetwork(input_layer_size, hidden_layer_size, num_labels)
reg_lambda = 1
# Testing cost calculation...
print("Loading Saved Neural Network Parameters ...")
# Load the weights into variables Theta1 and Theta2
theta = scipy.io.loadmat(dir_path + '\\ex4weights.mat')
theta1 = theta['Theta1']
theta2 = theta['Theta2']
nn_params = nn.unroll(theta1, theta2)
result = nn.get_nn_params(nn_params, X, y, reg_lambda)
cost_value = round(result[0],4)
if cost_value != 0.3838:
print("ERROR: Test with saved parameters failed! Cost value: ", cost_value, "\n")
print("Training neural network. Please wait...")
initial_theta1 = nn.random_initialize_weights(input_layer_size, hidden_layer_size)
initial_theta2 = nn.random_initialize_weights(hidden_layer_size, num_labels)
initial_nn_params = nn.unroll(initial_theta1, initial_theta2)
weights = scipy.optimize.fmin_cg(cost, fprime=gradient, x0=initial_nn_params,
args=(num_labels, X, y, input_layer_size, hidden_layer_size, reg_lambda),
maxiter = 50, disp = False)
print("Training finished!\n")
print("Predicting...")
