def test_forward_prop_small_network():
'''testing forward prop in 4-3-3-1 network'''
x = np.array([1, 1, 1, 1]) #input
y = [0.94533048] #preknown output
node_list = [4, 3, 3, 1]
activations = ['sigmoid', 'sigmoid', 'sigmoid']
nn_test1 = NeuralNetwork(node_list, activations)
weights_init(nn_test1) #Setting weights to constant value = 1
y_test = nn_test1.forward_prop(x)
assert np.isclose(y_test, y)
def test_gradient_checking_big_neural_network_2():
node_list = [70, 15, 7, 1]
activations = ['sigmoid', 'sigmoid', 'sigmoid']
x1 = np.full((1, 70), 5).reshape(1, node_list[0])
y1 = np.array([1000]).reshape(1, node_list[-1])
nn_test_2 = NeuralNetwork(node_list, activations)
e_nn = nn_test_2.forward_prop(x1)
derivative_analytical = nn_test_2.analytical_gradients(x1, e_nn, y1)
derivative_numerical = numerical_gradients(nn_test_2, x1, e_nn, y1)
assert np.allclose(derivative_analytical, derivative_numerical, atol=1e-5)
def test_forward_prop_big_network_3():
'''testing forward prop in 70-2-2-1 network
sigmoid--sigmoid--linear'''
x = np.ones((1, 70)) #input
y = [1.76159416] #preknown output
node_list = [70, 2, 2, 1]
activations = ['sigmoid', 'sigmoid', 'linear']
nn_test4 = NeuralNetwork(node_list, activations)
weights_init(nn_test4) #Setting weights to constant value = 1
y_test = nn_test4.forward_prop(x)
assert np.isclose(y_test, y)
def test_forward_prop_big_network_2():
'''testing forward prop in 70-10-10-1 network
sigmoid--sigmoid--linear'''
x = np.ones((1, 70)) #input
y = [9.99954602] #preknown output
node_list = [70, 10, 10, 1]
activations = ['sigmoid', 'sigmoid', 'linear']
nn_test3 = NeuralNetwork(node_list, activations)
weights_init(nn_test3) #Setting weights to constant value = 1
y_test = nn_test3.forward_prop(x)
assert abs(y_test - y) < 1e-8
def test_gradient_checking_small_neural_network_1():
#declare structure of the neural network(no of nodes,activations of layers)
node_list = [2, 3, 3, 1]
activations = ['ReLU', 'ReLU', 'ReLU']
#set inputs and output
x = np.array([1, 2]).reshape(1, node_list[0])
y = np.array([10]).reshape(1, node_list[-1])
nn_test_1 = NeuralNetwork(node_list, activations)
e_nn = nn_test_1.forward_prop(x)
derivative_analytical = nn_test_1.analytical_gradients(x, e_nn, y)
derivative_numerical = numerical_gradients(nn_test_1, x, e_nn, y)
assert np.isclose(derivative_analytical, derivative_numerical).all()
def test_forward_prop_big_network_1():
'''testing forward prop in 70-10-10-1 network
sigmoid--sigmoid--sigmoid'''
x = np.ones((1, 70)) #input
y = [0.99995458] #preknown output
node_list = [70, 10, 10, 1]
activations = ['sigmoid', 'sigmoid', 'sigmoid']
nn_test2 = NeuralNetwork(node_list, activations)
weights_init(nn_test2) #Setting weights to constant value = 1
y_test = nn_test2.forward_prop(x)
assert np.isclose(y_test, y)
def test_forward_prop_big_network_4():
'''testing forward prop in 70-10-10-1 network
linear--linear--linear'''
x = np.ones((1, 70)) #input
y = [
3500
] #preknown output(when input and weights are array of ones and activation is linear,the output will be the product of number of nodes in each layer)
node_list = [70, 5, 10, 1]
activations = ['linear', 'linear', 'linear']
nn_test5 = NeuralNetwork(node_list, activations)
weights_init(nn_test5) #Setting weights to constant value = 1
y_test = nn_test5.forward_prop(x)
assert np.isclose(y_test, y)