Keras Plugins
Project description
# keras-plugins
## Callbacks
### Telegram Callback
Notify levels available:
1) on_train_begin,
2) on_train_end,
3) on_batch_begin,
4) on_batch_end,
5) on_epoch_begin,
6) on_epoch_end
##### How to use
##### Installation
```
pip install kerasplugins
```
```python
from kerasplugins import callbacks
#Notify can either be a list, dict or set
notify = {
'on_batch_end', # sends BATCH END: Loss 0.50 Accuracy: 0.75
'on_epoch_end' # sends EPOCH END: Loss 0.43 Accuracy: 0.81
}
# msg is the initial message
msg = "Predicting Bitcoin Price"
telegram = callbacks.TelegramNotify(<token>, <chat_id>, msg=msg, notify=notify)
# channel is "#general" by default
slack = callbacks.SlackNotify(<slack_token>, <channel>, msg=msg, notify=notify)
model.fit(X_train, Y_train, validation_data=[X_test, Y_test], batch_size=256, epochs=10, callbacks=[telegram, slack])
```
## Coming Soon
1) Ability to stop training remotely
## Callbacks
### Telegram Callback
Notify levels available:
1) on_train_begin,
2) on_train_end,
3) on_batch_begin,
4) on_batch_end,
5) on_epoch_begin,
6) on_epoch_end
##### How to use
##### Installation
```
pip install kerasplugins
```
```python
from kerasplugins import callbacks
#Notify can either be a list, dict or set
notify = {
'on_batch_end', # sends BATCH END: Loss 0.50 Accuracy: 0.75
'on_epoch_end' # sends EPOCH END: Loss 0.43 Accuracy: 0.81
}
# msg is the initial message
msg = "Predicting Bitcoin Price"
telegram = callbacks.TelegramNotify(<token>, <chat_id>, msg=msg, notify=notify)
# channel is "#general" by default
slack = callbacks.SlackNotify(<slack_token>, <channel>, msg=msg, notify=notify)
model.fit(X_train, Y_train, validation_data=[X_test, Y_test], batch_size=256, epochs=10, callbacks=[telegram, slack])
```
## Coming Soon
1) Ability to stop training remotely
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