class TestMatMul(unittest.TestCase):
def setUp(self):
W = np.random.rand(4, 2)
self.matmul = MatMul(W)
self.x = np.random.rand(2, 4)
def test_forward(self):
out = self.matmul.forward(self.x)
self.assertEqual((2, 2), out.shape)
def test_backward(self):
dout = self.matmul.forward(self.x)
dx = self.matmul.backward(dout)
self.assertEqual((2, 4), dx.shape)
class SimpleCBOW:
def __init__(self, vocab_size, hidden_size):
V = vocab_size
H = hidden_size
# Initialise weight
W_in = 0.01 * np.random.randn(V, H).astype('f')
W_out = 0.01 * np.random.randn(H, V).astype('f')
# Generate layers
self.in_layer_0 = MatMul(W_in)
self.in_layer_1 = MatMul(W_in)
self.out_layer = MatMul(W_out)
self.loss_layer = SoftMaxWithLoss()
# Integrate all weight and gradients in a list
layers = [self.in_layer_0, self.in_layer_1, self.out_layer]
self.params = []
self.grads = []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
# Assign a word embedding to an instance variable
self.word_vecs = W_in
def forward(self, contexts, target):
h0 = self.in_layer_0.forward(contexts[:, 0])
h1 = self.in_layer_1.forward(contexts[:, 1])
h = (h0 + h1) * 0.5
score = self.out_layer.forward(h)
loss = self.loss_layer.forward(score, target)
return loss
def backward(self, dout=1):
ds = self.loss_layer.backward(dout)
da = self.out_layer.backward(ds)
da *= 0.5
self.in_layer_1.backward(da)
self.in_layer_0.backward(da)
return None
