ModifiedNeuralNet.py 文件源码

def forward(self, data, stable_version=False):
        """input has each row as data vector; output also does so"""
        count = 1
        for bias, weight, pre_w, post_w in zip(self.biases, self.weights, self.pre_w, self.post_w):
            size = pre_w[0].shape[0]
            zeros_pre_w = T.zeros((size + 4, size + 4))
            zeros_post_w = T.zeros((size + 4, size + 4))
            pre_w_padding = T.set_subtensor(zeros_pre_w[2: size + 2, 2: size + 2], pre_w[0])
            post_w_padding_T = T.set_subtensor(zeros_post_w[2: size + 2, 2: size + 2], post_w[0])
            pre, updt = scan(process_pre_post_w, sequences=[pre_w_padding, zeros_pre_w])
            post_T, updt = scan(process_pre_post_w, sequences=[post_w_padding_T, zeros_post_w])
            pre, post_T = pre[2:size + 2, :], post_T[2:size + 2, :]
            ori_shape = data.shape
            data = T.reshape(data, (ori_shape[0], pre_w[0].shape[0], pre_w[0].shape[0]))
            product, updt = scan(lambda x, A, B: T.dot(T.dot(A, x), B), sequences=data, non_sequences=[pre, post_T.T])
            data = T.reshape(product, ori_shape)
            if count < self.num_layers - 1:
                data = T.nnet.relu(T.dot(data, weight) + bias)
            elif not stable_version:
                data = T.nnet.softmax(T.dot(data, weight) + bias)
            else:
                data = log_softmax(T.dot(data, weight) + bias)
            count += 1
        return data