create list and drop down list in Excel 2003

本文介绍了在Microsoft Excel中创建列表和下拉列表的方法。创建列表需先选中数据范围,通过数据菜单操作,创建后列表有蓝色边框等特征;创建下拉列表要先选单元格,在数据验证中设置,还可指定引用、输入提示及对无效数据的响应。
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create list 1. Highlight the range of data that you want make into a list (list: A series of rows that contains related data or a series of rows that you designate to function as a datasheet by using the Create List command.). Note You can also select the range of cells to be specified as a list by selecting the range of cells from the Create List dialog box. 2. On the Data menu, point to List, and then click Create List. 3. If the selected data has headers, select the My list has headers check box and click OK. The selected range of data is highlighted by the list indicator, and the most common list related functionality is made available on the List toolbar. Note If you don't see the List toolbar, on the View menu point to Toolbars, and then click List. After the list has been created, it will be identified by a blue border. In addition, AutoFilter drop-downs will be automatically enabled for each column in the list and the insert row will be added as the last row or the list. If you choose to add a total row by clicking Toggle Total Row Button image on the List toolbar, a total row will be displayed under the insert row. When you select a cell, row, or column outside of the list, the list becomes inactive. An inactive list is surrounded by a blue border and does not display the insert row or AutoFilter drop-downs. Note The border will not be displayed if you clicked Hide Border of Inactive Lists on the List menu. create dropdown list 1.Select the cell where you want the drop-down list. 2. On the Data menu, click Validation, and then click the Settings tab. 3. In the Allow box, click List. 4. If the list is in the same worksheet, enter a reference to your list in the Source box. 5.If the list is elsewhere, enter the name you defined for your list in the Source box. 6.Make sure the reference or name is preceded with an equal sign (=). 7. Make sure the In-cell drop-down check box is selected. 8. Specify whether the cell can be left blank: Select or clear the Ignore blank check box. 9. To display optional input instructions when the cell is clicked, click the Input Message tab, make sure the Show input message when cell is selected check box is selected, and then fill in the title and text for the message. 10.Specify how you want Microsoft Excel to respond when invalid data is entered.

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# New module: utils.pyimport torchfrom torch import nnclass ConvBlock(nn.Module): """A convolutional block consisting of a convolution layer, batch normalization layer, and ReLU activation.""" def __init__(self, in_chans, out_chans, drop_prob): super().__init__() self.conv = nn.Conv2d(in_chans, out_chans, kernel_size=3, padding=1) self.bn = nn.BatchNorm2d(out_chans) self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout2d(p=drop_prob) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) x = self.dropout(x) return x# Refactored U-Net modelfrom torch import nnfrom utils import ConvBlockclass UnetModel(nn.Module): """PyTorch implementation of a U-Net model.""" def __init__(self, in_chans, out_chans, chans, num_pool_layers, drop_prob, pu_args=None): super().__init__() PUPS.__init__(self, *pu_args) self.in_chans = in_chans self.out_chans = out_chans self.chans = chans self.num_pool_layers = num_pool_layers self.drop_prob = drop_prob # Calculate input and output channels for each ConvBlock ch_list = [chans] + [chans * 2 ** i for i in range(num_pool_layers - 1)] in_chans_list = [in_chans] + [ch_list[i] for i in range(num_pool_layers - 1)] out_chans_list = ch_list[::-1] # Create down-sampling layers self.down_sample_layers = nn.ModuleList() for i in range(num_pool_layers): self.down_sample_layers.append(ConvBlock(in_chans_list[i], out_chans_list[i], drop_prob)) # Create up-sampling layers self.up_sample_layers = nn.ModuleList() for i in range(num_pool_layers - 1): self.up_sample_layers.append(ConvBlock(out_chans_list[i], out_chans_list[i + 1] // 2, drop_prob)) self.up_sample_layers.append(ConvBlock(out_chans_list[-1], out_chans_list[-1], drop_prob)) # Create final convolution layer self.conv2 = nn.Sequential( nn.Conv2d(out_chans_list[-1], out_chans_list[-1] // 2, kernel_size=1), nn.Conv2d(out_chans_list[-1] // 2, out_chans, kernel_size=1), nn.Conv2d(out_chans, out_chans, kernel_size=1), ) def forward(self, x): # Down-sampling path encoder_outs = [] for layer in self.down_sample_layers: x = layer(x) encoder_outs.append(x) x = nn.MaxPool2d(kernel_size=2)(x) # Bottom layer x = self.conv(x) # Up-sampling path for i, layer in enumerate(self.up_sample_layers): x = nn.functional.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) x = torch.cat([x, encoder_outs[-(i + 1)]], dim=1) x = layer(x) # Final convolution layer x = self.conv2(x) return x
06-09
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