| def download_file(url): | |
| local_filename = url.split('/')[-1] | |
| # NOTE the stream=True parameter | |
| r = requests.get(url, stream=True) | |
| with open(local_filename, 'wb') as f: | |
| for chunk in r.iter_content(chunk_size=1024): | |
| if chunk: # filter out keep-alive new chunks | |
| f.write(chunk) | |
| f.flush() | |
| return local_filename |
| """ | |
| A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes. | |
| @url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d | |
| @author: wassname | |
| """ | |
| from keras import backend as K | |
| def weighted_categorical_crossentropy(weights): | |
| """ | |
| A weighted version of keras.objectives.categorical_crossentropy | |
This tutorial will guide through the steps to create a simple custom layer for Caffe using python. By the end of it, there are some examples of custom layers.
Usually you would create a custom layer to implement a funcionality that isn't available in Caffe, tuning it for your requirements.
Probably just Python and Caffe installed.