Updated Keras and TensorFlow Task Runner and related workspaces.

openfl-workspace/keras_cnn_mnist/plan/plan.yaml

@@ -1,13 +1,13 @@
-# Copyright (C) 2020-2021 Intel Corporation
+# Copyright (C) 2020-2024 Intel Corporation
 # Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.
 
 aggregator :
   defaults : plan/defaults/aggregator.yaml
   template : openfl.component.Aggregator
   settings :
-    init_state_path : save/keras_cnn_mnist_init.pbuf
-    best_state_path : save/keras_cnn_mnist_best.pbuf
-    last_state_path : save/keras_cnn_mnist_last.pbuf
+    init_state_path : save/init.pbuf
+    best_state_path : save/best.pbuf
+    last_state_path : save/last.pbuf
     rounds_to_train : 10
 
 collaborator :
@@ -19,15 +19,15 @@ collaborator :
 
 data_loader :
   defaults : plan/defaults/data_loader.yaml
-  template : src.tfmnist_inmemory.TensorFlowMNISTInMemory
+  template : src.dataloader.TensorFlowMNISTInMemory
   settings :
     collaborator_count : 2
     data_group_name    : mnist
     batch_size         : 256
 
 task_runner :
   defaults : plan/defaults/task_runner.yaml
-  template : src.keras_cnn.KerasCNN
+  template : src.taskrunner.KerasCNN
 
 network :
   defaults : plan/defaults/network.yaml
@@ -39,4 +39,4 @@ tasks :
   defaults : plan/defaults/tasks_keras.yaml
 
 compression_pipeline :
-  defaults : plan/defaults/compression_pipeline.yaml
+  defaults : plan/defaults/compression_pipeline.yaml

openfl-workspace/keras_cnn_mnist/requirements.txt

@@ -1,2 +1,3 @@
-numpy==1.23.5
-tensorflow==2.13
+keras==3.6.0
+tensorflow==2.18.0
+

openfl-workspace/keras_cnn_with_compression/src/keras_cnn.py → openfl-workspace/keras_cnn_mnist/src/taskrunner.py

@@ -3,16 +3,15 @@
 
 """You may copy this file as the starting point of your own model."""
 
-import tensorflow.keras as ke
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers import Conv2D
-from tensorflow.keras.layers import Dense
-from tensorflow.keras.layers import Flatten
+from keras.models import Sequential
+from keras.layers import Conv2D
+from keras.layers import Dense
+from keras.layers import Flatten
 
-from openfl.federated import KerasTaskRunner
+from openfl.federated import KerasTensorFlowTaskRunner
 
 
-class KerasCNN(KerasTaskRunner):
+class KerasCNN(KerasTensorFlowTaskRunner):
     """A basic convolutional neural network model."""
 
     def __init__(self, **kwargs):
@@ -50,7 +49,7 @@ def build_model(self,
             num_classes (int): The number of classes of the dataset
 
         Returns:
-            tensorflow.python.keras.engine.sequential.Sequential: The model defined in Keras
+            keras.models.Sequential: The model defined in Keras
 
         """
         model = Sequential()
@@ -72,14 +71,8 @@ def build_model(self,
 
         model.add(Dense(num_classes, activation='softmax'))
 
-        model.compile(loss=ke.losses.categorical_crossentropy,
-                      optimizer=ke.optimizers.legacy.Adam(),
-                      metrics=['accuracy'])
-
-        # initialize the optimizer variables
-        opt_vars = model.optimizer.variables()
-
-        for v in opt_vars:
-            v.initializer.run(session=self.sess)
+        model.compile(loss="categorical_crossentropy",
+                      optimizer="adam",
+                      metrics=["accuracy"])
 
         return model

openfl-workspace/keras_cnn_with_compression/plan/plan.yaml

@@ -5,9 +5,9 @@ aggregator :
   defaults : plan/defaults/aggregator.yaml
   template : openfl.component.Aggregator
   settings :
-    init_state_path : save/keras_cnn_mnist_init.pbuf
-    best_state_path : save/keras_cnn_mnist_best.pbuf
-    last_state_path : save/keras_cnn_mnist_last.pbuf
+    init_state_path : save/init.pbuf
+    best_state_path : save/best.pbuf
+    last_state_path : save/last.pbuf
     db_store_rounds: 2
     rounds_to_train : 10
 
@@ -21,15 +21,15 @@ collaborator :
 
 data_loader :
   defaults : plan/defaults/data_loader.yaml
-  template : src.tfmnist_inmemory.TensorFlowMNISTInMemory
+  template : src.dataloader.TensorFlowMNISTInMemory
   settings :
     collaborator_count : 2
     data_group_name    : mnist
     batch_size         : 256
 
 task_runner :
   defaults : plan/defaults/task_runner.yaml
-  template : src.keras_cnn.KerasCNN
+  template : src.taskrunner.KerasCNN
 
 network :
   defaults : plan/defaults/network.yaml

openfl-workspace/keras_cnn_with_compression/requirements.txt

@@ -1 +1,2 @@
-tensorflow==2.13
+keras==3.6.0
+tensorflow==2.18.0

openfl-workspace/keras_cnn_mnist/src/keras_cnn.py → openfl-workspace/keras_cnn_with_compression/src/taskrunner.py

@@ -3,16 +3,15 @@
 
 """You may copy this file as the starting point of your own model."""
 
-import tensorflow.keras as ke
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers import Conv2D
-from tensorflow.keras.layers import Dense
-from tensorflow.keras.layers import Flatten
+from keras.models import Sequential
+from keras.layers import Conv2D
+from keras.layers import Dense
+from keras.layers import Flatten
 
-from openfl.federated import KerasTaskRunner
+from openfl.federated import KerasTensorFlowTaskRunner
 
 
-class KerasCNN(KerasTaskRunner):
+class KerasCNN(KerasTensorFlowTaskRunner):
     """A basic convolutional neural network model."""
 
     def __init__(self, **kwargs):
@@ -72,14 +71,8 @@ def build_model(self,
 
         model.add(Dense(num_classes, activation='softmax'))
 
-        model.compile(loss=ke.losses.categorical_crossentropy,
-                      optimizer=ke.optimizers.legacy.Adam(),
-                      metrics=['accuracy'])
-
-        # initialize the optimizer variables
-        opt_vars = model.optimizer.variables()
-
-        for v in opt_vars:
-            v.initializer.run(session=self.sess)
+        model.compile(loss="categorical_crossentropy",
+                      optimizer="adam",
+                      metrics=["accuracy"])
 
         return model

openfl-workspace/keras_nlp/plan/plan.yaml

@@ -5,9 +5,9 @@ aggregator :
   defaults : plan/defaults/aggregator.yaml
   template : openfl.component.Aggregator
   settings :
-    init_state_path : save/keras_nlp_init.pbuf
-    best_state_path : save/keras_nlp_best.pbuf
-    last_state_path : save/keras_nlp_last.pbuf
+    init_state_path : save/init.pbuf
+    best_state_path : save/best.pbuf
+    last_state_path : save/last.pbuf
     rounds_to_train : 10
 
 collaborator :
@@ -21,7 +21,7 @@ collaborator :
 
 data_loader :
   defaults : plan/defaults/data_loader.yaml
-  template : src.nlp_dataloader.NLPDataLoader
+  template : src.dataloader.NLPDataLoader
   settings :
     collaborator_count : 2
     batch_size         : 64
@@ -30,7 +30,7 @@ data_loader :
 
 task_runner :
   defaults : plan/defaults/task_runner.yaml
-  template : src.nlp_taskrunner.KerasNLP
+  template : src.taskrunner.KerasNLP
   settings :
     latent_dim : 256
 

openfl-workspace/keras_nlp/requirements.txt

@@ -1 +1,2 @@
-tensorflow==2.13
+keras==3.6.0
+tensorflow==2.18.0

openfl-workspace/keras_nlp/src/nlp_dataloader.py → openfl-workspace/keras_nlp/src/dataloader.py

@@ -5,8 +5,6 @@
 license agreement between Intel Corporation and you.
 """
 from logging import getLogger
-from typing import Iterator
-from typing import List
 from typing import Optional
 from typing import Tuple
 from typing import Union
@@ -57,7 +55,7 @@ def get_feature_shape(self) -> Tuple[int, ...]:
         """Get the shape of an example feature array."""
         return self.X_train[0].shape
 
-    def get_train_loader(self, batch_size: Optional[int] = None) -> Iterator[List[np.ndarray]]:
+    def get_train_loader(self, batch_size: Optional[int] = None):
         """
         Get training data loader.
 
@@ -68,7 +66,7 @@ def get_train_loader(self, batch_size: Optional[int] = None) -> Iterator[List[np
         return self._get_batch_generator(X1=self.X_train[0], X2=self.X_train[1],
                                          y=self.y_train, batch_size=batch_size)
 
-    def get_valid_loader(self, batch_size: Optional[int] = None) -> Iterator[List[np.ndarray]]:
+    def get_valid_loader(self, batch_size: Optional[int] = None):
         """
         Get validation data loader.
 
@@ -100,7 +98,7 @@ def get_valid_data_size(self) -> int:
     def _batch_generator(X1: np.ndarray, X2: np.ndarray,
                          y: np.ndarray, idxs: np.ndarray,
                          batch_size: int,
-                         num_batches: int) -> Iterator[List[np.ndarray]]:
+                         num_batches: int):
         """
         Generate batch of data.
 
@@ -116,11 +114,11 @@ def _batch_generator(X1: np.ndarray, X2: np.ndarray,
         for i in range(num_batches):
             a = i * batch_size
             b = a + batch_size
-            yield [X1[idxs[a:b]], X2[idxs[a:b]]], y[idxs[a:b]]
+            yield (X1[idxs[a:b]], X2[idxs[a:b]]), y[idxs[a:b]]
 
     def _get_batch_generator(self, X1: np.ndarray, X2: np.ndarray,
                              y: np.ndarray,
-                             batch_size: Union[int, None]) -> Iterator[List[np.ndarray]]:
+                             batch_size: Union[int, None]):
         """
         Return the dataset generator.