{"id":14375,"date":"2024-03-15T08:59:52","date_gmt":"2024-03-15T08:59:52","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/what-is-the-usage-of-the-flatten-function-in-pytorch\/"},"modified":"2025-08-06T07:54:15","modified_gmt":"2025-08-06T07:54:15","slug":"what-is-the-usage-of-the-flatten-function-in-pytorch","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/what-is-the-usage-of-the-flatten-function-in-pytorch\/","title":{"rendered":"PyTorch Flatten Function: Usage Explained"},"content":{"rendered":"

In PyTorch, the flatten function is used to flatten an input tensor into a one-dimensional tensor. Its usage is as follows:<\/p>\n

torch.flatten(input, start_dim=0, end_dim=-1)\r\n<\/code><\/pre>\n
Explanation of parameters:<\/p>\n
    \n
  1. tensor input.<\/li>\n
  2. start_dim: the dimension in which flattening should start, with a default value of 0.<\/li>\n
  3. The dimension for ending flattening, with the default value of -1 meaning flattening to the last dimension.<\/li>\n<\/ol>\n
    The flatten function flattens the input tensor along the specified dimension range into a one-dimensional tensor. The flattened tensor will contain all the elements from the original tensor, rearranged into a one-dimensional structure.<\/p>\n
    Original: \u6211\u660e\u5929\u8981\u53bb\u63a5\u7238\u7238\u3002
    \nParaphrased: I am going to pick up my dad tomorrow.<\/p>\n
    import<\/span> torch\r\n\r\nx = torch.randn(3<\/span>, 4<\/span>, 5<\/span>)\r\nflattened = torch.flatten(x)\r\nprint<\/span>(flattened.shape)  # \u8f93\u51fa: torch.Size([60])<\/span>\r\n\r\nflattened_dim1 = torch.flatten(x, start_dim=1<\/span>)\r\nprint<\/span>(flattened_dim1.shape)  # \u8f93\u51fa: torch.Size([3, 20])<\/span>\r\n\r\nflattened_dim1_dim2 = torch.flatten(x, start_dim=1<\/span>, end_dim=2<\/span>)\r\nprint<\/span>(flattened_dim1_dim2.shape)  # \u8f93\u51fa: torch.Size([3, 20, 5])<\/span>\r\n<\/code><\/pre>\n
    In the example above, the flatten function first flattens the tensor x with shape (3, 4, 5) into a one-dimensional tensor with shape (60,). Then, by specifying start_dim=1, the second dimension of the tensor x is flattened, resulting in a tensor with shape (3, 20). Lastly, by specifying start_dim=1 and end_dim=2, the second and third dimensions of the tensor x are flattened, resulting in a tensor with shape (3, 20, 5).<\/p>\n","protected":false},"excerpt":{"rendered":"
    In PyTorch, the flatten function is used to flatten an input tensor into a one-dimensional tensor. Its usage is as follows: torch.flatten(input, start_dim=0, end_dim=-1) Explanation of parameters: tensor input. start_dim: the dimension in which flattening should start, with a default value of 0. The dimension for ending flattening, with the default value of -1 meaning […]<\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[960,19335,944,1239,9167],"class_list":["post-14375","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-deep-learning","tag-flatten","tag-neural-networks","tag-pytorch","tag-tensor-manipulation"],"yoast_head":"\nPyTorch Flatten Function: Usage Explained - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" content=\"Learn how PyTorch's flatten() converts tensors to 1D. 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