def _get_model_weight_metrics(model: keras.models.Model,
obs_horizon,
n_channels,
n_noise_channels):
weight_matrix = model.get_weights()[0]
W_variance = np.var(weight_matrix)
weight_tensor = food_search_env._unflatten_weight_matrix(weight_matrix,
obs_horizon,
n_channels,
n_actions=4)
noise_part = weight_tensor[:, :, -n_noise_channels:, :]
W_noise_part_variance = np.var(noise_part)
outside_part = np.r_[
weight_tensor[:, 0, :].ravel(),
weight_tensor[:, -1, :].ravel(),
weight_tensor[0, 1:-1, :].ravel(),
weight_tensor[-1, 1:-1, :].ravel()
]
W_outside_part_variance = np.var(outside_part)
inside_part = weight_tensor[1:-1, 1:-1, :]
W_inside_part_variance = np.var(inside_part)
# n_inside = inside_part.size
# n_outside = weight_matrix.size
# assert n_outside == weight_tensor.size
# n_total = n_inside + n_outside
# W_outside_part_variance = (n_total / n_inside)*W_variance - (n_outside/n_inside) * W_inside_part_variance
return {
'W_variance': W_variance,
'W_noise_part_variance': W_noise_part_variance,
'W_outside_part_variance': W_outside_part_variance,
'W_inside_part_variance': W_inside_part_variance
}
def check_model_precision(self,
model: keras.models.Model,
state: "State") -> keras.models.Model:
""" Check the model's precision.
If this is a new model, then
Rewrite an existing model's training precsion mode from mixed-float16 to float32 or
vice versa.
This is not easy to do in keras, so we edit the model's config to change the dtype policy
for compatible layers. Create a new model from this config, then port the weights from the
old model to the new model.
Parameters
----------
model: :class:`keras.models.Model`
The original saved keras model to rewrite the dtype
state: ~:class:`plugins.train.model._base.model.State`
The State information for the model
Returns
-------
:class:`keras.models.Model`
The original model with the datatype updated
"""
if get_backend() == "amd": # Mixed precision not supported on amd
return model
if self.use_mixed_precision and not state.mixed_precision_layers:
# Switching to mixed precision on a model which was started in FP32 prior to the
# ability to switch between precisions on a saved model is not supported as we
# do not have the compatible layer names
logger.warning("Switching from Full Precision to Mixed Precision is not supported on "
"older model files. Reverting to Full Precision.")
return model
config = model.get_config()
if not self.use_mixed_precision and not state.mixed_precision_layers:
# Switched to Full Precision, get compatible layers from model if not already stored
state.add_mixed_precision_layers(self._get_mixed_precision_layers(config["layers"]))
self._switch_precision(config["layers"], state.mixed_precision_layers)
new_model = keras.models.Model().from_config(config)
new_model.set_weights(model.get_weights())
logger.info("Mixed precision has been updated from '%s' to '%s'",
not self.use_mixed_precision, self.use_mixed_precision)
del model
return new_model
