Row 8166
Content Data
This page contains data entry 8166 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
https://preview.redd.it/7tcmorm6n81d1.png?width=1114&format=png&auto=webp&s=e799a4ca8462699b8dea7cf9817aab2eab00b19a
Consider a neural network shown below. \[Answer in comment\]\[ Answered\]
Consider we have a cross-entropy loss function for binary classification: L=−\[𝑦 ln(𝑎)+(1−𝑦) ln(1−𝑎)\], where 𝑎 is the probability out from the output layer activation function. We've built a computation graph of the network as shown below. The blue letters below are intermediate variable labels to help you understand the connection between the network architecture graph above and the computation graph.
When 𝑦=1, what is the gradient of the loss function w.r.t. 𝑊11? \*\*Write your answer to three decimal places. Note: Please use the computation graph method. One can calculate the gradient directly using chain rules, but if the computation graph is not used at all, it will not score properly. Try to fill the red boxes above. This question does not need coding and the answer can be easily obtained analytically.
https://preview.redd.it/1sy0qb9sn81d1.png?width=1172&format=png&auto=webp&s=21c0a50e6a5d70d4d6b93d1031ca03edef9672b0
Hint
https://preview.redd.it/9g7lh1n8n81d1.png?width=1182&format=png&auto=webp&s=6e18e922dc7f669681673bc152a4183dbc93ab1d
| Field | Value |
|---|---|
| text | https://preview.redd.it/7tcmorm6n81d1.png?width=1114&format=png&auto=webp&s=e799a4ca8462699b8dea7cf9817aab2eab00b19a Consider a neural network shown below. \[Answer in comment\]\[ Answered\] Consider we have a cross-entropy loss function for binary classification: L=−\[𝑦 ln(𝑎)+(1−𝑦) ln(1−𝑎)\], where 𝑎 is the probability out from the output layer activation function. We've built a computation graph of the network as shown below. The blue letters below are intermediate variable labels to h… |
| label | r/deeplearning |
| dataType | post |
| communityName | r/deeplearning |
| datetime | 2024-05-18 |
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Raw Record
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"text": "https://preview.redd.it/7tcmorm6n81d1.png?width=1114&format=png&auto=webp&s=e799a4ca8462699b8dea7cf9817aab2eab00b19a\n\nConsider a neural network shown below. \\[Answer in comment\\]\\[ Answered\\]\n\nConsider we have a cross-entropy loss function for binary classification: L=−\\[𝑦 ln(𝑎)+(1−𝑦) ln(1−𝑎)\\], where 𝑎 is the probability out from the output layer activation function. We've built a computation graph of the network as shown below. The blue letters below are intermediate variable labels to help you understand the connection between the network architecture graph above and the computation graph. \n\nWhen 𝑦=1, what is the gradient of the loss function w.r.t. 𝑊11? \\*\\*Write your answer to three decimal places. Note: Please use the computation graph method. One can calculate the gradient directly using chain rules, but if the computation graph is not used at all, it will not score properly. Try to fill the red boxes above. This question does not need coding and the answer can be easily obtained analytically.\n\nhttps://preview.redd.it/1sy0qb9sn81d1.png?width=1172&format=png&auto=webp&s=21c0a50e6a5d70d4d6b93d1031ca03edef9672b0\n\nHint\n\nhttps://preview.redd.it/9g7lh1n8n81d1.png?width=1182&format=png&auto=webp&s=6e18e922dc7f669681673bc152a4183dbc93ab1d",
"label": "r/deeplearning",
"dataType": "post",
"communityName": "r/deeplearning",
"datetime": "2024-05-18",
"username_encoded": "Z0FBQUFBQm5Lakw0OFFYWjJ1cndpMnN5YnJZLTlIcU9kSGp3Y0tjYS1UaE45c3BOelVPdUhTOE5SaHZhQVd4SzZ4VVZEZDZZVWM3YlNzT0pfM29DeV8xY3o0NUtLUHJscmc9PQ==",
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}
Entry Information
- Entry ID: 8166
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000