Row 3581

Row ID: 3581 | Dataset Entry | Axioma AXP Content Repository

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I am trying to implement a Self-Organizing Map where for a given input sample, the best matching unit/winning unit is chosen based on (say) L2-norm distance between the SOM and the input. The winning unit/BMU (som\[x, y\]) has the smallest L2 distance from the given input (z):

`# Input batch: batch-size = 512, input-dim = 84-`

`z = torch.randn(512, 84)`

`# SOM shape: (height, width, input-dim)-`

`som = torch.randn(40, 40, 84)`

`print(f"BMU row, col shapes; row = {row.shape} & col = {col.shape}")`

`# BMU row, col shapes; row = torch.Size([512]) & col = torch.Size([512])`

For clarity, for the first input sample in the batch "z\[0\]", the winning unit is "som\[row\[0\], col\[0\]\]"-

`z[0].shape, som[row[0], col[0]].shape`

`# (torch.Size([84]), torch.Size([84]))`

`torch.norm((z[0] - som[row[0], col[0]]))` is the smallest L2 distance between z\[0\] and all other som units except row\[0\] and col\[0\].

`# Define initial neighborhood radius and learning rate-`

`neighb_rad = torch.tensor(2.0)`

`lr = 0.5`

`# To update weights for the first input "z[0]" and its corresponding BMU "som[row[0], col[0]]"-`

`for r in range(som.shape[0]):`

`for c in range(som.shape[1]):`

`neigh_dist = torch.exp(-torch.norm(input = (som[r, c] - som[row[0], col[0]])) / (2.0 * torch.pow(neighb_rad, 2)))`

`som[r, c] = som[r, c] + (lr * neigh_dist * (z[0] - som[r, c]))`

How can I implement the code for:

1. updating weights for all units around each BMU without the 2 for loops (and) 2. do it for all of the inputs "z" (here, z has 512 samples)

FieldValue
text I am trying to implement a Self-Organizing Map where for a given input sample, the best matching unit/winning unit is chosen based on (say) L2-norm distance between the SOM and the input. The winning unit/BMU (som\[x, y\]) has the smallest L2 distance from the given input (z): `# Input batch: batch-size = 512, input-dim = 84-` `z = torch.randn(512, 84)` `# SOM shape: (height, width, input-dim)-` `som = torch.randn(40, 40, 84)` `print(f"BMU row, col shapes; row = {row.shape} & col = {col.sha…
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-03-14
username_encoded Z0FBQUFBQm5LakwxQlhBelYteEZYaXdnT1F5ejZxM3Nob2taY2J4Z2VmNzA3RnNMRVJZZFZ4QVl5Q3AxQjhvOVk1bS1keVZfUl9JNEhiT1UxRENNZFByTVVCLXh4V3phakE9PQ==
url_encoded Z0FBQUFBQm5Lak9Fai10bnFBbUd1MXc3SVFyZTBBWUl0ZEVuYWxkQ0VfTWJUc29ETVdYSnE1THNkN2JXdXZwNVRCSDFvVUphaF9ZWklpTENVSkZObHJsbWxId1BwM0lqaFU4UGUtcWg1X2xINk16VDRycGtMNjB3Ykt0MEZBWmhScnZmTG1BOEpkTGFRSjlIWl90MndleHFmcEozTVRMcTVjc1FsU1BXYkNIMEo2MXFKRTEwMU02bW9LOWR0ODRUTHE1Zkhfa05FV0lfUVlEeGVQTHdUMEtMcE11MjhOV0hwdz09

Raw Record

{
  "text": "I am trying to implement a Self-Organizing Map where for a given input sample, the best matching unit/winning unit is chosen based on (say) L2-norm distance between the SOM and the input. The winning unit/BMU (som\\[x, y\\]) has the smallest L2 distance from the given input (z):\n\n`# Input batch: batch-size = 512, input-dim = 84-`\n\n`z = torch.randn(512, 84)`\n\n`# SOM shape: (height, width, input-dim)-`\n\n`som = torch.randn(40, 40, 84)`\n\n`print(f\"BMU row, col shapes; row = {row.shape} & col = {col.shape}\")`\n\n`# BMU row, col shapes; row = torch.Size([512]) & col = torch.Size([512])`\n\nFor clarity, for the first input sample in the batch \"z\\[0\\]\", the winning unit is \"som\\[row\\[0\\], col\\[0\\]\\]\"-\n\n`z[0].shape, som[row[0], col[0]].shape`\n\n`# (torch.Size([84]), torch.Size([84]))`\n\n`torch.norm((z[0] - som[row[0], col[0]]))` is the smallest L2 distance between z\\[0\\] and all other som units except row\\[0\\] and col\\[0\\].\n\n`# Define initial neighborhood radius and learning rate-`\n\n`neighb_rad = torch.tensor(2.0)`\n\n`lr = 0.5`\n\n`# To update weights for the first input \"z[0]\" and its corresponding BMU \"som[row[0], col[0]]\"-`\n\n`for r in range(som.shape[0]):`\n\n`for c in range(som.shape[1]):`\n\n`neigh_dist = torch.exp(-torch.norm(input = (som[r, c] - som[row[0], col[0]])) / (2.0 * torch.pow(neighb_rad, 2)))`\n\n`som[r, c] = som[r, c] + (lr * neigh_dist * (z[0] - som[r, c]))`\n\nHow can I implement the code for:\n\n1. updating weights for all units around each BMU without the 2 for loops (and)\n2. do it for all of the inputs \"z\" (here, z has 512 samples)",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-03-14",
  "username_encoded": "Z0FBQUFBQm5LakwxQlhBelYteEZYaXdnT1F5ejZxM3Nob2taY2J4Z2VmNzA3RnNMRVJZZFZ4QVl5Q3AxQjhvOVk1bS1keVZfUl9JNEhiT1UxRENNZFByTVVCLXh4V3phakE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9Fai10bnFBbUd1MXc3SVFyZTBBWUl0ZEVuYWxkQ0VfTWJUc29ETVdYSnE1THNkN2JXdXZwNVRCSDFvVUphaF9ZWklpTENVSkZObHJsbWxId1BwM0lqaFU4UGUtcWg1X2xINk16VDRycGtMNjB3Ykt0MEZBWmhScnZmTG1BOEpkTGFRSjlIWl90MndleHFmcEozTVRMcTVjc1FsU1BXYkNIMEo2MXFKRTEwMU02bW9LOWR0ODRUTHE1Zkhfa05FV0lfUVlEeGVQTHdUMEtMcE11MjhOV0hwdz09"
}
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