reductionista commented on a change in pull request #524:
URL: https://github.com/apache/madlib/pull/524#discussion_r586813934
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File path: src/ports/postgres/modules/deep_learning/madlib_keras_wrapper.py_in
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@@ -287,27 +298,50 @@ def parse_optimizer(compile_dict):
# Parse the fit parameters into a dictionary.
-def parse_and_validate_fit_params(fit_param_str):
+def parse_and_validate_fit_params(fit_param_str, current_seg_id=0):
if fit_param_str:
- fit_params_dict = convert_string_of_args_to_dict(fit_param_str)
-
- literal_eval_fit_params = ['batch_size','epochs','verbose',
+ fit_params_dict = convert_string_of_args_to_dict(fit_param_str,
strip_quotes=False)
+ literal_eval_fit_params = ['batch_size','epochs','verbose', 'shuffle',
Review comment:
"In recent versions of keras, the optimizer classes are required to be
passed in the normal pythonic way, as class variables."
Correction: in recent versions, you can still pass optimizers in one of two
ways, but I think the options are a bit more limited than in the past.
Currently, you can pass either an optimizer object (instantiation of anything
derived from `keras.optimizers.Optimizer`), or what you get when you call
get_config['name'] on the default instantiation of one of the builtin
optimizers, eg:
`keras.optimizers.Adam().get_config()['name']`
'Adam'
But it looks like for all the ones I've checked, the config name and the
class name do still match. So I guess what I said above is only true for
non-default instantiations of the class.
Any of these are okay:
```
optmizer=Adam(learning_rate=0.01)
optimizer=Adam(learning_rate=0.01, name='My custom Adam')
optimizer='Adam'
```
but this is not:
```
optimizer='Adam(learning_rate=0.01)'
```
It looks like they have the same deal with losses and metrics, but for those
the names of the classes don't necessarily match:
```
keras.losses.CategoricalCrossentropy().get_config()['name']
'categorical_crossentropy'
```
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