def test_predict_02():
np.random.seed(seed=1)
input_dimension = 4
number_of_samples = 7
number_of_layers = 2
number_of_nodes_in_layers_list = list(
np.random.randint(3, high=15, size=(number_of_layers, )))
number_of_nodes_in_layers_list[-1] = 5
multi_nn = MultiNN(input_dimension)
for layer_number in range(len(number_of_nodes_in_layers_list)):
multi_nn.add_layer(number_of_nodes_in_layers_list[layer_number],
activation_function=multi_nn.linear)
for layer_number in range(len(number_of_nodes_in_layers_list)):
W = multi_nn.get_weights_without_biases(layer_number)
np.random.seed(seed=1)
W = np.random.randn(*W.shape)
multi_nn.set_weights_without_biases(W, layer_number)
b = multi_nn.get_biases(layer_number)
b = np.random.randn(*b.shape)
multi_nn.set_biases(b, layer_number)
X = 0.01 * np.random.randn(number_of_samples, input_dimension)
Y = multi_nn.predict(X).numpy()
# print(np.array2string(np.array(Y), separator=","))
assert np.allclose(Y, np.array( \
[[-0.40741104, -3.44566828, -1.76869339, 1.6163245, -2.54072028],
[-0.43079142, -3.33179871, -1.68949031, 1.60929609, -2.56731804],
[-0.40121922, -3.38003556, -1.72446422, 1.62425049, -2.53508997],
[-0.39082312, -3.38185473, -1.70911191, 1.58223456, -2.57304599],
[-0.32408109, -3.351902, -1.66647768, 1.55353972, -2.5740835],
[-0.25208801, -3.46616221, -1.72734675, 1.53356758, -2.55233025],
[-0.54751084, -3.30222443, -1.73142232, 1.72880885, -2.50151569]]), rtol=1e-3, atol=1e-3)
def test_predict():
np.random.seed(seed=1)
input_dimension = 4
number_of_samples = 7
number_of_layers = 3
number_of_nodes_in_layers_list = list(
np.random.randint(3, high=15, size=(number_of_layers, )))
number_of_nodes_in_layers_list[-1] = 5
multi_nn = MultiNN(input_dimension)
for layer_number in range(len(number_of_nodes_in_layers_list)):
multi_nn.add_layer(number_of_nodes_in_layers_list[layer_number],
activation_function=multi_nn.sigmoid)
for layer_number in range(len(number_of_nodes_in_layers_list)):
W = multi_nn.get_weights_without_biases(layer_number)
np.random.seed(seed=1)
W = np.random.randn(*W.shape)
multi_nn.set_weights_without_biases(W, layer_number)
b = multi_nn.get_biases(layer_number)
b = np.random.randn(*b.shape)
multi_nn.set_biases(b, layer_number)
X = np.random.randn(number_of_samples, input_dimension)
Y = multi_nn.predict(X)
assert np.allclose(Y.numpy(),np.array( \
[[0.03266127, 0.50912841, 0.64450596, 0.9950739, 0.85501755],
[0.10064564, 0.33718693, 0.30543574, 0.94555041, 0.85876801],
[0.0723957, 0.37974684, 0.32749328, 0.96334569, 0.86322814],
[0.08510464, 0.36674615, 0.49463606, 0.97151055, 0.79762128],
[0.09168739, 0.3137653, 0.34286721, 0.96533277, 0.87458543],
[0.03880329, 0.41823933, 0.33338152, 0.98297688, 0.86816211],
[0.04737598, 0.4365602, 0.41720532, 0.9806787, 0.79618209]]),rtol=1e-3, atol=1e-3)