def predict(self, x): W1, W2 = self.params['W1'], self.params['W2'] b1, b2 = self.params['b1'], self.params['b2'] a1 = np.dot(x, W1) + b1 z1 = sigmoid(a1) a2 = np.dot(z1, W2) + b2 y = softmax(a2) return y
def predict(self, x): W1, W2 = self.params["W1"], self.params["W2"] b1, b2 = self.params["b1"], self.params["b2"] a1 = np.dot(x, W1) + b1 z1 = sigmoid(a1) a2 = np.dot(z1, W2) + b2 y = softmax(a2) return y
def predict(network, x): W1, W2, W3 = network["W1"], network["W2"], network["W3"] b1, b2, b3 = network["b1"], network["b2"], network["b3"] a1 = np.dot(x, W1) + b1 z1 = sigmoid(a1) a2 = np.dot(z1, W2) + b2 z2 = sigmoid(a2) a3 = np.dot(z2, W3) + b3 y = softmax(a3) return y
def loss(self, x, t): z = self.predict(x) y = softmax(z) loss = cross_entropy_error(y, t) return loss
def forward(self, x, t): self.t = t self.y = softmax(x) self.loss = cross_entropy_error(self.y, self.t) return self.loss