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 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