解释代码 def partial_fit(self, X,Y, lr): cost, summary, _, Coef = self.sess.run( (self.reconst_cost_x, self.merged_summary_op, self.optimizer, self.Coef_test), feed_dict={self.learning_rate:lr,self.train:Y,self.test:X}) self.summary_writer.add_summary(summary, self.iter) self.iter = self.iter + 1 return cost, Coef def pretrain_step(self, X,Y, lr): cost, summary, _ = self.sess.run( (self.reconst_cost_x, self.merged_summary_op, self.optimizer_pretrain), feed_dict={self.learning_rate:lr,self.train:Y,self.test:X}) self.summary_writer.add_summary(summary, self.iter) self.iter = self.iter + 1 return cost

时间: 2023-06-18 19:05:56 浏览: 56
这是一个基于 TensorFlow 的深度学习模型的类中的两个方法： 1. `partial_fit(self, X,Y, lr)`：该方法用于训练模型，通过传入训练数据 X 和对应的标签数据 Y，以及学习率 lr，来更新模型参数。具体实现中，先使用 TensorFlow 的 `run` 方法执行 `self.reconst_cost_x`、`self.merged_summary_op`、`self.optimizer` 和 `self.Coef_test` 四个操作，其中 `self.reconst_cost_x` 是模型的重构误差，`self.merged_summary_op` 是 summary，`self.optimizer` 是优化器，`self.Coef_test` 是模型的参数。最后将 summary 写入 summary_writer 中，同时更新迭代次数 self.iter，并返回 cost 和 Coef。 2. `pretrain_step(self, X,Y, lr)`：该方法用于预训练模型，通过传入训练数据 X 和对应的标签数据 Y，以及学习率 lr，来更新模型参数。具体实现中，先使用 TensorFlow 的 `run` 方法执行 `self.reconst_cost_x`、`self.merged_summary_op` 和 `self.optimizer_pretrain` 三个操作，其中 `self.reconst_cost_x` 是模型的重构误差，`self.merged_summary_op` 是 summary，`self.optimizer_pretrain` 是预训练的优化器。最后将 summary 写入 summary_writer 中，同时更新迭代次数 self.iter，并返回 cost。

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class Partial_conv3(nn.Module): def __init__(self, dim, n_div, forward): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x 在这段代码中插入全局平均池化做通道增强的模块

x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) x[:, :self.dim_conv3, :, :] = self.global_avg_pool(x[:, :self.dim_conv3, :, :]) # 添加全局平均池化 x = torch.cat((x, x...

class Partial_conv3(nn.Module): def init(self, dim, n_div, forward): super().init() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.global_pool(x1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x这段代码有什么错误

x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, inputs: Tensor) -> Tensor: x1, x2 = torch.split(inputs, [self.dim_conv3, self....

帮我分析以下代码：class PConv(nn.Module): def __init__(self, dim, ouc, n_div=4, forward='split_cat'): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.conv = Conv(dim, ouc, k=1) if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x): # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) x = self.conv(x) return x def forward_split_cat(self, x): # for training/inference x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) x = self.conv(x) return x

这段代码定义了一个名为 PConv 的类，该类继承自 nn.Module 类。该类的初始化函数接受三个参数：dim，ouc 和 n_div。其中，dim 表示输入特征图的通道数，ouc 表示输出特征图的通道数，n_div 表示将输入特征图的通道...

def partial_fit(self,train_main,train_appliances,pretrain = False, do_preprocessing=True, **load_kwargs):

这是一个Python类中的一个方法，用于对模型进行增量训练。具体来说，它接受训练数据集train_main和相应的电器数据集train_appliances，并在当前模型参数的基础上，使用这些新数据进行训练以更新模型参数。...

if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d请解释这段代码

这段代码是用来判断输入的归一化层（norm_layer）是否为实例归一化（nn.InstanceNorm2d）。首先通过type()函数判断norm_layer的类型，如果是functools.partial类型，则说明这是一个函数的部分应用...

逐句翻译代码def load_trained_modules(model: torch.nn.Module, args: None): enc_model_path = args.enc_init enc_modules = args.enc_init_mods main_state_dict = model.state_dict() logging.warning("model(s) found for pre-initialization") if os.path.isfile(enc_model_path): logging.info('Checkpoint: loading from checkpoint %s for CPU' % enc_model_path) model_state_dict = torch.load(enc_model_path, map_location='cpu') modules = filter_modules(model_state_dict, enc_modules) partial_state_dict = OrderedDict() for key, value in model_state_dict.items(): if any(key.startswith(m) for m in modules): partial_state_dict[key] = value main_state_dict.update(partial_state_dict) else: logging.warning("model was not found : %s", enc_model_path)

partial_state_dict = OrderedDict()创建一个有序字典partial_state_dict，用于存储部分状态字典。 for key, value in model_state_dict.items():迭代model_state_dict中的每个元素。 if any(key.starts...

解释代码 while epoch < pretrain_max_step: #当迭代次数小于预训练步数的while循环 epoch = epoch + 1 cost = CAE.pretrain_step(Img_test,Img_train, lr) #cost为卷积自动编码器的预训练步数 if epoch % display_step == 0: print ("pretrtain epoch: %.1d" % epoch, "cost: %.8f" % (cost / float(batch_size))) #当迭代次数为展示步数的整数倍时，输出预训练迭代次数和cost while epoch < max_step: #当迭代次数小于最大步数的while循环 epoch = epoch + 1 cost, Coef = CAE.partial_fit(Img_test,Img_train, lr) if epoch % display_step == 0: print ("epoch: %.1d" % epoch, "cost: %.8f" % (cost / float(batch_size))) Coef = thrC(Coef) Coef= np.abs(Coef) for test_sample in range(0,len(test_labels)): x = Coef[test_sample,:] for l in range(1,np.max(test_labels)+1): l_idx = np.array([j for j in range(0,len(train_labels)) if train_labels[j]==l]) l_idx= l_idx.astype(int) class_[int(l-1)] = sum(np.abs(x[l_idx])) prediction[test_sample] = np.argmax(class_) +1 prediction = np.array(prediction) missrate_x = err_rate(test_labels, prediction) acc_x = 1 - missrate_x print("accuracy: %.4f" % acc_x) ACC.append(acc_x) Cost.append(cost / float(batch_size)) if False: # change to ture to save values in a mat file sio.savemat('./coef.mat', dict(ACC=ACC,Coef=Coef,Cost=Cost)) return acc_x, Coef

这段代码的作用是使用卷积自动编码器(CAE)进行图像分类。首先，使用CAE进行预训练，其中参数包括Img_test和Img_train(分别是测试集和训练集的图像数据)， lr表示学习率。然后在每个展示步数(display_step)的整数倍时...

class DTIDataset(data.Dataset): def __init__(self, list_IDs, df, max_drug_nodes=290): self.list_IDs = list_IDs self.df = df self.max_drug_nodes = max_drug_nodes self.atom_featurizer = CanonicalAtomFeaturizer() self.bond_featurizer = CanonicalBondFeaturizer(self_loop=True) self.fc = partial(smiles_to_bigraph, add_self_loop=True)是什么意思

这段代码定义了一个名为DTIDataset的类，它继承自data.Dataset。在初始化函数中，它接受三个参数：list_IDs、df和max_drug_nodes。它还定义了一个atom_featurizer和一个bond_featurizer，它们分别使用...

history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

这段代码是使用Keras中的模型训练方法fit()对模型进行训练。其中，partial_x_train和partial_y_train是训练数据的输入特征和输出标签，epochs是训练轮数，batch_size是每个批次的数据量，validation_data是验证数据...

class DistributedSampler(_DistributedSampler): def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): super().__init__(dataset, num_replicas=num_replicas, rank=rank) self.shuffle = shuffle def __iter__(self): if self.shuffle: g = torch.Generator() g.manual_seed(self.epoch) indices = torch.randperm(len(self.dataset), generator=g).tolist() else: indices = torch.arange(len(self.dataset)).tolist() indices += indices[:(self.total_size - len(indices))] assert len(indices) == self.total_size indices = indices[self.rank:self.total_size:self.num_replicas] assert len(indices) == self.num_samples return iter(indices) def build_dataloader(dataset_cfg, class_names, batch_size, dist, root_path=None, workers=4, seed=None, logger=None, training=True, merge_all_iters_to_one_epoch=False, total_epochs=0): dataset = __all__[dataset_cfg.DATASET]( dataset_cfg=dataset_cfg, class_names=class_names, root_path=root_path, training=training, logger=logger, ) if merge_all_iters_to_one_epoch: assert hasattr(dataset, 'merge_all_iters_to_one_epoch') dataset.merge_all_iters_to_one_epoch(merge=True, epochs=total_epochs) if dist: if training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: rank, world_size = common_utils.get_dist_info() sampler = DistributedSampler(dataset, world_size, rank, shuffle=False) else: sampler = None dataloader = DataLoader( dataset, batch_size=batch_size, pin_memory=True, num_workers=workers, shuffle=(sampler is None) and training, collate_fn=dataset.collate_batch, drop_last=False, sampler=sampler, timeout=0, worker_init_fn=partial(common_utils.worker_init_fn, seed=seed) ) return dataset, dataloader, sampler

这段代码是一个用于构建数据加载器的函数。它接受一些参数，包括数据集的配置、类别名称、批次大小、分布式训练标志、数据集的根路径等。 首先，根据数据集的配置和其他参数，创建一个数据集对象。 如果设置了 ...

def show_img(self): img_path = self.comboBox.currentText() pix = QPixmap(self.path + img_path) self.detect_image.setPixmap(pix) ui.pushButton2.clicked.connect(partial(ui.show_img, ui)) TypeError: show_img() takes 1 positional argument but 3 were given

具体来说，partial(ui.show_img, ui) 将 ui.show_img 函数作为函数对象进行了传递，并传递了 ui 对象作为第二个参数。然而，ui.show_img 函数是一个类方法，它的第一个参数应该是 self，而不是 ui。 ...

def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*sequence2) nn.LeakyReLU(0.2, True) return self.model(input)，上述代码出现问题：RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 64, 512, 512] to have 512 channels, but got 64 channels instead，如何修改

def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset...

class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)，逐行解释上述代码

下面逐行解释代码： class NLayerDiscriminator(nn.Module): 定义了一个名为 NLayerDiscriminator 的类，它继承自 nn.Module。 def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=...

def cost_derivative(self, output_activations, y): return (output_activations - y) 把这个函数改为使用交叉熵误差损失函数的导数

交叉熵误差损失函数的导数公式为： $$\frac{\partial C}{\partial z_j} = \...def cost_derivative(self, output_activations, y): return (output_activations - y) / len(y) # 使用交叉熵误差损失函数的导数

解释： def inv_objectfun_gradient(self, detector, receiver_locations, true_mag_data, x): """ The gradient of the objective function with respect to x. Parameters ---------- detector : class Detector receiver_locations : numpy.ndarray, shape=(N*3) See inv_objective_function receiver_locations. true_mag_data : numpy.ndarray, shape=(N*3) See inv_objective_function true_mag_data. x : numpy.array, size=9 See inv_objective_function x. Returns ------- grad : numpy.array, size=9 The partial derivative of the objective function with respect to nine parameters. """ rx = self.inv_residual_vector(detector, receiver_locations, true_mag_data, x) jx = self.inv_residual_vector_grad(detector, receiver_locations, x) grad = rx.T * jx grad = np.array(grad)[0] return grad

这是一个Python函数，名为inv_objectfun_gradient，它接受四个参数：detector，receiver_locations，true_mag_data和x。该函数返回一个大小为9的numpy数组，表示目标函数相对于九个参数的偏导数。在函数中...

如何将self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 与torchvision.ops.deform_conv2d，加入到：class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)中，请给出修改后的代码

可以将self.conv1、self.conv_offset1和self.conv_mask1加入到NLayerDiscriminator中的nn.Sequential序列中，如下所示： import torchvision from torchvision.ops.deform_conv import DeformConv2d class ...

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