mlp.train(x_data, t_data,
epochs=2000, batch_size=10,
epsilon=0.1,
print_cost=True)
time_end = time.time()
print('Time used in training %f' % (time_end - time_begin))
# check if circles nearby the middle one are
# correctly classified
mlp.get_activations_and_units(x_black)
wrong_black = (mlp.y > 1/2).squeeze()
print('Points misclassified as black: {}'.format(np.sum(wrong_black)))
mlp.get_activations_and_units(x_red)
wrong_red = (mlp.y < 1/2).squeeze()
print('Points misclassified as red: {}'.format(np.sum(wrong_red)))
# plot the probability mapping and the data
delta = 0.01
x = np.arange(-3, 3, delta)
y = np.arange(-3, 3, delta)
y = np.arange(-3, 3, delta)
X, Y = np.meshgrid(x, y)
x_pts = np.vstack((X.flatten(), Y.flatten())).T
x_middle = (x_black + x_red) / 2
mlp = MLP(K_list, activation_functions, diff_activation_functions)
for k in range(1000):
mlp.train(x_data, t_data,
epochs=10, batch_size=10,
epsilon=0.1,
print_cost=True)
mlp.get_activations_and_units(x_pts)
grid_size = X.shape[0]
Z = mlp.y.reshape(grid_size, grid_size)
plt.axis('equal')
plot_contour = plt.contourf(X, Y, Z, 10)
plt.scatter(x_black[:, 0], x_black[:, 1], marker='o', color='black')
plt.scatter(x_red[:, 0], x_red[:, 1], marker='x', color='red')
plt.scatter(x_middle[:, 0], x_middle[:, 1], marker='.', color='white')
plt.pause(0.01)
plt.draw()
plt.show()
K_list = [D, i, j, K] #list of dimensions of layers
activation_functions = [np.tanh, np.tanh, MLP.sigmoid]
diff_activation_functions = [MLP.dtanh, MLP.dtanh, MLP.dsigmoid]
#%%
x_middle = (x_black + x_red) / 2
nb_middle = x_middle.shape[0]
t_middle = np.asarray([0.5] * nb_middle).reshape(nb_middle, 1)
mlp = MLP(K_list, activation_functions, diff_activation_functions)
for k in range(20):
mlp.train(x_data,
t_data,
epochs=10,
batch_size=10,
epsilon=0.1,
print_cost=True)
mlp.get_activations_and_units(x_data)
data_cost = mlp.binary_cross_entropy(mlp.y, t_data)
if data_cost < data_min:
data_min = data_cost
i_min = i
j_min = j
print(i, " ", j, " ", data_min, "\n")
#mlp.get_activations_and_units(x_middle)
#print(mlp.binary_cross_entropy(mlp.y, t_middle))
