def test_matmul(self):
W = np.random.randn(7, 3)
x = np.random.randn(10, 7)
matmul = MatMul(W)
dout = matmul.forward(x)
dx = matmul.backward(dout)
np.testing.assert_array_almost_equal(dout.shape, (10, 3))
np.testing.assert_array_almost_equal(dx.shape, (10, 7))
class SimpleCBOW:
def __init__(self, vocab_size, hidden_size):
V, H = vocab_size, hidden_size
W_in = 0.01 * np.random.randn(V, H).astype('f')
W_out = 0.01 * np.random.randn(H, V).astype('f')
self.in_layer0 = MatMul(W_in)
self.in_layer1 = MatMul(W_in)
self.out_layer = MatMul(W_out)
self.loss_layer = SoftmaxWithLoss()
layers = [self.in_layer0, self.in_layer1, self.out_layer]
self.params, self.grads = [], []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
self.word_vecs = W_in
def forward(self, contexts, target):
h0 = self.in_layer0.forward(contexts[:, 0])
h1 = self.in_layer1.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)
# distribute diff to h0/h1 equally
da *= 0.5
self.in_layer1.backward(da)
self.in_layer0.backward(da)
return None
class SimpleCBOW:
def __init__(self, vocab_size, hidden_size):
V, H = vocab_size, hidden_size
# 重みの初期化
W_in = 0.01 * np.random.randn(V, H).astype('f')
W_out = 0.01 * np.random.randn(H, V).astype('f')
# レイヤの生成
self.in_layer0 = MatMul(W_in)
self.in_layer1 = MatMul(W_in)
self.out_layer = MatMul(W_out)
self.loss_layer = SoftmaxWithLoss()
# すべての重みと勾配をリストにまとめる
layers = [self.in_layer0, self.in_layer1, self.out_layer]
self.params, self.grads = [], []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
# メンバ変数に単語の分散表現を設定
self.word_vecs = W_in
def forward(self, contexts, target):
h0 = self.in_layer0.forward(contexts[:, 0])
h1 = self.in_layer1.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_layer1.backward(da)
self.in_layer0.backward(da)
return None
class SimpleSkipGram:
def __init__(self, vocab_size, hidden_size):
V, H = vocab_size, hidden_size
W_in = 0.01 * np.random.randn(V, H).astype('f')
W_out = 0.01 * np.random.randn(H, V).astype('f')
self.in_layer = MatMul(W_in)
self.out_layer = MatMul(W_out)
self.loss_layer0 = SoftmaxWithLoss()
self.loss_layer1 = SoftmaxWithLoss()
layers = [self.in_layer, self.out_layer]
self.params, self.grads = [], []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
self.word_vecs = W_in
def forward(self, contexts, target):
h = self.in_layer.forward(target)
s = self.out_layer.forward(h)
l1 = self.loss_layer0.forward(s, contexts[:, 0])
l2 = self.loss_layer1.forward(s, contexts[:, 1])
loss = l1 + l2
return loss
def backward(self, dout=1):
dl0 = self.loss_layer1.backward(dout)
dl1 = self.loss_layer0.backward(dout)
ds = dl0 + dl1
dh = self.out_layer.backward(ds)
self.in_layer.backward(dh)
return None