def train_text_transform_Model(model, X_train, X_test): print("Projecting the input data on the eigenfaces orthonormal basis") t0 = time() X_train_model = model.transform(X_train) X_test_model = model.transform(X_test) print("done in %0.3fs" % (time() - t0)) return X_train_model, X_test_model
时间: 2023-11-22 21:51:46 浏览: 35
这个函数的作用是将输入数据通过一个预训练的模型进行转换,并返回转换后的数据。具体来说,输入数据是X_train和X_test,它们的形状为(m, n),其中m是样本数,n是每个样本的特征数。模型是一个PCA或者LDA模型,它已经在训练集上训练好了,可以将数据投影到一组特征向量上。在函数中,首先调用model.transform(X_train)将训练集数据投影到模型的特征向量上,然后再将测试集数据投影到同一组特征向量上。最后返回两个投影后的数据,分别是X_train_model和X_test_model。
相关问题
*\n * get projecting point of the point\n * */
"get projecting point of the point" 这个表述似乎是在询问如何找到某个点在某个投影或映射过程中的对应位置。这可能是几何、计算机图形学、摄影或者数学中的概念,具体取决于上下文。在三维空间中,如果涉及到投影,可能是指从三维空间到二维平面上的投影,如透视投影或平行投影。在计算机视觉中,这可能涉及到图像处理中的特征点投影。
简要解释一下,如果我们有一个三维空间中的点(x, y, z),要在另一个平面上找到它的投影,例如在屏幕上,我们通常会遵循一定的投影矩阵或公式,比如在透视投影中使用pinhole camera model。这个过程可能包括将三维坐标乘以一个投影矩阵,然后取结果的前两个分量作为二维平面上的投影点。
We can now use a method to plot the loss surface of the network by projecting the parameter updates into two dimensions. You can find more information on that here. But you can just use the provided code. The contour plot will show how the loss will change if you would follow the two main directions of the past parameter updates. Think about the challenges and the optimization process of this landscape. What could impede the convergence of the net? # project states onto the main directions of the gradient updates using n samples over all steps starting from sample x # the directions are calculated using the last sample as a reference directions, state_ids, loss_coordinates = get_state_directions(states, n_states=10, start_from=0, reference_id=-1) # compute the losses over the main directions of the gradient updates x, y, Z, _ = get_loss_grid(net, data_loader, loss_fn, directions=directions, resolution=(20, 20), scale=loss_coordinates.abs().max().item()) # plot the landscape as a contour plot fig = plot_contour(np.copy(x), np.copy(y), np.copy(Z), scale=True) fig.add_traces(go.Scatter(x=np.copy(loss_coordinates[0].cpu().numpy()), y=np.copy(loss_coordinates[1].cpu().numpy()))) print('loss samples:', np.array(losses)[state_ids]) conf_pltly() init_notebook_mode(connected=False) iplot(fig) --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-62-26d05ea2d790> in <cell line: 3>() 1 # project states onto the main directions of the gradient updates using n samples over all steps starting from sample x 2 # the directions are calculated using the last sample as a reference ----> 3 directions, state_ids, loss_coordinates = get_state_directions(states, n_states=10, start_from=0, reference_id=-1) 4 5 # compute the losses over the main directions of the gradient updates <ipython-input-60-6cc4aad7dcda> in get_state_directions(states, n_states, start_from, reference_id) 15 params.append(param.view(-1)) 16 ---> 17 params = torch.stack(params, dim=0) 18 reference = params[-1] 19 RuntimeError: stack expects each tensor to be equal size, but got [200704] at entry 0 and [256] at entry 1这个错误怎么改
这个错误是由于在进行 torch.stack(params, dim=0) 操作时,params 中的张量维度不一致导致的。可以通过使用 torch.cat() 来代替 torch.stack(),将 params 列表中的张量按照指定维度拼接起来,从而避免这个错误。具体来说,可以将 get_state_directions() 函数中的 params = torch.stack(params, dim=0) 改为 params = torch.cat(params, dim=0)。
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