class Perceptron(object):
def __init__(self, input_size, output_size):
W1 = 0.01 * np.random.randn(input_size, output_size)
b1 = np.zeros(output_size)
self.loss_layer = SigmoidWithLoss()
self.layers = [Affine(W1, b1)]
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
return
def predict(self, X):
for layer in self.layers:
X = layer.forward(X)
return X
def forward(self, X, y):
score = self.predict(X)
loss = self.loss_layer.forward(score, y)
return loss
def backward(self, dout=1):
dout = self.loss_layer.backward(dout)
for layer in reversed(self.layers):
dout = layer.backward(dout)
return dout
def __init__(self, input_size, output_size):
W1 = 0.01 * np.random.randn(input_size, output_size)
b1 = np.zeros(output_size)
self.loss_layer = SigmoidWithLoss()
self.layers = [Affine(W1, b1)]
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
return
def forward(self, xs, ts):
_, T = xs.shape
self.xs_shape = xs.shape
self.layers = []
loss = 0
for t in range(T):
layer = SigmoidWithLoss()
loss += layer.forward(xs[:, t], ts[:, t])
self.layers.append(layer)
return loss / T
def __init__(self, W, corpus, power=0.75, sample_size=5):
"""
Parameters
----------
W :
出力層の重み
corpus :
コーパス（単語ID）のリスト
power :
確率分布の累乗の値
sample_size :
負例のサンプリング数
"""
self.sample_size = sample_size
# UnigramSamplerを使用する
self.sampler = UnigramSampler(corpus, power, sample_size)
# 正例用のレイヤをひとつ、負例用のレイヤをsample_size個作成する
# リストの最初のレイヤが正例を扱うものとする
self.loss_layers = [SigmoidWithLoss() for _ in range(sample_size + 1)]
# 4.2.4で作成したEmbeddingDotレイヤ
self.embed_dot_layers = [
EmbeddingDot(W) for _ in range(sample_size + 1)
]
self.params, self.grads = [], []
for layer in self.embed_dot_layers:
self.params += layer.params
self.grads += layer.grads
def __init__(self, W, corpus, power=0.75, sample_size=5):
self.sample_size = sample_size # 상위 5개만
self.sampler = UnigramSampler(corpus, power, sample_size)
self.loss_layers = [SigmoidWithLoss() for _ in range(sample_size + 1)]
self.embed_dot_layers = [EmbeddingDot(W) for _ in range(sample_size + 1)]
#print(len(self.embed_dot_layers));exit(1)
self.params, self.grads = [], []
for layer in self.embed_dot_layers:
self.params += layer.params # array형식으로 params를 묶어서 나열. [np.array(w), np.array(w), ...]
self.grads += layer.grads
def __init__(self, W, corpus, power=0.75, sample_size=5):
self.sample_size = sample_size
self.sampler = UnigramSampler(corpus, power, sample_size)
self.loss_layers = [SigmoidWithLoss() for _ in range(sample_size + 1)]
self.embed_dot_layers = [EmbeddingDot(W) for _ in range(sample_size + 1)]
self.params, self.grads = [], []
for layer in self.embed_dot_layers:
self.params += layer.params
self.grads += layer.grads
