def test_backward(self):
X=[[0,0],[0,1],[1,0],[1,1]]
Y=[[0],[1],[1],[0]]
nn=NN([2,2,1],verbose=0)
for i in range(4):
nn.forward(X[i])
nn.backward(Y[i])
self.assertEqual(nn.outputs[0].shape,(3,1))
self.assertEqual(nn.outputs[1].shape,(3,1))
self.assertEqual(nn.outputs[2].shape,(1,1))
self.assertEqual(nn.get_output().shape,(1,))
def test_backward(self):
X = [[0, 0], [0, 1], [1, 0], [1, 1]]
Y = [[0], [1], [1], [0]]
nn = NN([2, 2, 1], verbose=0)
for i in range(4):
nn.forward(X[i])
nn.backward(Y[i])
self.assertEqual(nn.outputs[0].shape, (3, 1))
self.assertEqual(nn.outputs[1].shape, (3, 1))
self.assertEqual(nn.outputs[2].shape, (1, 1))
self.assertEqual(nn.get_output().shape, (1, ))
def test_neural_net_back_forward(self):
n_in, n_out = 3, 2
weights = np.array([[0, -1, 2], [-3, 4, -5]])
bias = np.arange(n_out)[:, np.newaxis]
nn = NeuralNet(MeanSquaredError(),
1e-3,
layers=[Linear(n_in, 2, weights, bias),
ReLU()])
x = np.array([[[0], [1], [2]]])
y = np.array([[[2], [3]]])
assert y.shape[1] == n_out
# |0 -1 2| |0| |0| | 3| |0| | 3| |3|
# |-3 4 -5| |1| + |1| = |-6| + |1| = |-5| -> |0|
# |2|
pred = nn.forward(x)
assert np.array_equal(pred, [[[3], [0]]])
nn.compute_loss(pred, y)
dL_dx = nn.backward()
# |0 -1 2| |0 + dx1| | 3 + 0 - dx2 + 2dx3| | 3 + ...| |3 - dx2 + 2dx3|
# |-3 4 -5| |1 + dx2| = |-6 - 3dx1 + 4dx2 - 5dx3| = |-5 + ...| -> |0|
# |2 + dx3| The second component is ReLU'ed away
# MSE loss results in 2( ... ) so dL = -2dx2 + 4dx3, dL/dx = |0, -2, 4|
assert np.array_equal(dL_dx, [[[0], [-2], [4]]])
def test_neural_net_tends_to_correct(self):
n_in, n_out = 4, 2
np.random.seed(12)
weights = np.random.normal(size=(n_out, n_in))
bias = np.zeros(n_out)[:, np.newaxis]
nn = NeuralNet(MeanSquaredError(),
1e-2,
layers=[Linear(n_in, 2, weights, bias)])
x = np.array([[[-1], [0.5], [-0.33], [0.75]]])
y = np.array([[[-0.5], [0.2]]])
for _ in range(1000):
pred = nn.forward(x)
loss = nn.compute_loss(pred, y)
nn.backward()
assert np.isclose(loss, 0)
def test_neural_net_works_with_batches(self):
n_in, n_out = 2, 2
np.random.seed(12)
weights = np.random.normal(size=(n_out, n_in))
bias = np.zeros(n_out)[:, np.newaxis]
nn = NeuralNet(MeanSquaredError(),
1e-2,
layers=[Linear(n_in, 2, weights, bias)])
# batch of 3
x = np.array([[[-1], [0.5]], [[1], [-0.2]], [[-0.33], [0.75]]])
y = x
# Why does this take so much longer to converge than the previous one?
for _ in range(10000):
pred = nn.forward(x)
loss = nn.compute_loss(pred, y)
nn.backward()
assert np.isclose(loss, 0)
assert np.all(
np.isclose(nn.layers[0].weights, [[1, 0], [0, 1]], atol=1e-3))
import numpy as np
from neural_net import NeuralNet
nn = NeuralNet(learning_rate=1,
layer_dims=[2, 3, 1],
actn_fn=['sigmoid', 'sigmoid'],
initializer='random')
# print(nn)
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]).T
Y = np.array([[0], [1], [1], [0]]).T
nn.layers[0].w = np.array([[0.1, 0.6], [0.2, 0.4], [0.3, 0.7]])
nn.layers[0].b = np.array([[0], [0], [0]])
nn.layers[1].w = np.array([[0.1, 0.4, 0.9]])
nn.layers[1].b = np.array([[0]])
for i in range(5000):
forward_val = nn(X)
# print ("Forward :", forward_val)
print("Error: ", nn.error(Y, nn.layers[-1].activation_fn.prev))
nn.backward(X, Y, forward_val)
y_pred = nn.classify(nn(X).reshape(-1))
print(y_pred)
print(nn.score(Y.reshape(-1), y_pred))
