by Tuyen Vo Quang
Last Updated March 13, 2018 07:19 AM - source

I know that loss function denotes the error for a single training example, and cost is for the entire training set. Also, people want to vectorize computation as much as possible for computational efficieny. And in the tensorflow docs they use loss notation: https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer

```
minimize(
loss,
global_step=None,
var_list=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None
)
```

My confusion is that loss in the minimize function is the same as loss for a single training example or not? If so how do they make use of vectorization for example, the case of a convolutional neural network.

Thanks,

- Serverfault Help
- Superuser Help
- Ubuntu Help
- Webapps Help
- Webmasters Help
- Programmers Help
- Dba Help
- Drupal Help
- Wordpress Help
- Magento Help
- Joomla Help
- Android Help
- Apple Help
- Game Help
- Gaming Help
- Blender Help
- Ux Help
- Cooking Help
- Photo Help
- Stats Help
- Math Help
- Diy Help
- Gis Help
- Tex Help
- Meta Help
- Electronics Help
- Stackoverflow Help
- Bitcoin Help
- Ethereum Help