Basic training functionality (original)

lib/gui/analysis/event_reader.py

@@ -9,10 +9,10 @@
 from dataclasses import dataclass, field
 
 import numpy as np
-import tensorflow as tf
-from tensorflow.core.util import event_pb2  # pylint:disable=no-name-in-module
-from tensorflow.python.framework import (  # pylint:disable=no-name-in-module
-    errors_impl as tf_errors)
+#import tensorflow as tf
+#from tensorflow.core.util import event_pb2  # pylint:disable=no-name-in-module
+#from tensorflow.python.framework import (  # pylint:disable=no-name-in-module
+#    errors_impl as tf_errors)
 
 from lib.serializer import get_serializer
 

lib/model/autoclip.py

@@ -1,6 +1,6 @@
 """ Auto clipper for clipping gradients. """
 import numpy as np
-import tensorflow as tf
+import torch
 
 
 class AutoClipper():  # pylint:disable=too-few-public-methods
@@ -25,7 +25,7 @@ def __init__(self, clip_percentile: int, history_size: int = 10000):
         self._index = tf.Variable(0, trainable=False)
         self._history_size = history_size
 
-    def _percentile(self, grad_history: tf.Tensor) -> tf.Tensor:
+    def _percentile(self, grad_history: torch.Tensor) -> torch.Tensor:
         """ Compute the clip percentile of the gradient history
 
         Parameters
@@ -69,7 +69,7 @@ def _percentile(self, grad_history: tf.Tensor) -> tf.Tensor:
 
             return gathered_hist
 
-    def __call__(self, grads_and_vars: list[tf.Tensor]) -> list[tf.Tensor]:
+    def __call__(self, grads_and_vars: list[torch.Tensor]) -> list[torch.Tensor]:
         """ Call the AutoClip function.
 
         Parameters
@@ -86,7 +86,7 @@ def __call__(self, grads_and_vars: list[tf.Tensor]) -> list[tf.Tensor]:
         return [(tf.clip_by_norm(g, clip_value), v) for g, v in grads_and_vars]
 
     @classmethod
-    def _get_grad_norm(cls, gradients: tf.Tensor) -> tf.Tensor:
+    def _get_grad_norm(cls, gradients: torch.Tensor) -> torch.Tensor:
         """ Obtain the L2 Norm for the gradients
 
         Parameters

lib/model/initializers.py

@@ -6,10 +6,10 @@
 import inspect
 
 import keras
+from keras.saving import get_custom_objects
 import keras.backend as K
 
 import numpy as np
-import tensorflow as tf
 
 
 
@@ -307,4 +307,4 @@ def get_config(self):
 # Update initializers into Keras custom objects
 for name, obj in inspect.getmembers(sys.modules[__name__]):
     if inspect.isclass(obj) and obj.__module__ == __name__:
-        keras.utils.get_custom_objects().update({name: obj})
+        get_custom_objects().update({name: obj})