Upgrade TensorFlow Version

README.rst

@@ -51,11 +51,11 @@ If you have already installed `OSL <https://github.com/OHBA-analysis/osl>`_ you
 
     conda activate osl
     cd osl-dynamics
-    pip install tensorflow==2.9.1
-    pip install tensorflow-probability==0.17
+    pip install tensorflow==2.11.0
+    pip install tensorflow-probability==0.19.0
     pip install -e .
 
-Note, if you're using a Mac computer you need to install TensorFlow with ``pip install tensorflow-macos==2.9.1`` instead of ``tensorflow==2.9.1``.
+Note, if you're using a Mac computer you need to install TensorFlow with ``pip install tensorflow-macos==2.11.0`` instead of ``tensorflow==2.11.0``.
 
 Removing osl-dynamics
 ---------------------

doc/install.rst

@@ -12,7 +12,7 @@ OSL Dynamics can be installed in three steps. Open a Terminal and execute the fo
 
     ::
 
-        conda create --name osld python=3.10
+        conda create --name osld python=3.10.14
         conda activate osld
 
     Note, this environment must be activated every time you want to use osl-dynamics.
@@ -23,27 +23,27 @@ OSL Dynamics can be installed in three steps. Open a Terminal and execute the fo
 
     ::
 
-        pip install tensorflow==2.9.1
+        pip install tensorflow==2.11.0
 
     If you have GPU resources you may need to install additional libraries (CUDA/cuDNN), see https://www.tensorflow.org/install/pip for detailed instructions.
 
     If you are using an Apple Mac, you will need to use the following instead:
 
     ::
 
-        pip install tensorflow-macos==2.9.1
+        pip install tensorflow-macos==2.11.0
 
     If pip can not find the package, then you can try installing TensorFlow with conda:
 
     ::
 
-        conda install tensorflow=2.9.1
+        conda install tensorflow=2.11.0
 
     After you have installed TensorFlow, install the tensorflow-probability addon with:
 
     ::
 
-        pip install tensorflow-probability==0.17
+        pip install tensorflow-probability==0.19.0
 
 #. Finally, install osl-dynamics:
 

envs/linux.yml

@@ -1,7 +1,7 @@
 name: osld
 dependencies:
-  - python=3.10.13
-  - pip=23.3.1
+  - python=3.10.14
+  - pip=24.0
   - pip:
     - glmtools==0.2.1
     - jupyter==1.0.0
@@ -21,5 +21,5 @@ dependencies:
     - seaborn==0.13.0
     - tabulate==0.9.0
     - tqdm==4.66.1
-    - tensorflow==2.9.1
-    - tensorflow-probability==0.17
+    - tensorflow==2.11.0
+    - tensorflow-probability==0.19.0

envs/mac.yml

@@ -1,7 +1,7 @@
 name: osld
 dependencies:
-  - python=3.10.13
-  - pip=23.3.1
+  - python=3.10.14
+  - pip=24.0
   - pip:
     - glmtools==0.2.1
     - jupyter==1.0.0
@@ -21,5 +21,5 @@ dependencies:
     - seaborn==0.13.0
     - tabulate==0.9.0
     - tqdm==4.66.1
-    - tensorflow-macos==2.9.1
-    - tensorflow-probability==0.17
+    - tensorflow-macos==2.11.0
+    - tensorflow-probability==0.19.0

examples/simulation/hive_hmm-mvn.py

@@ -21,7 +21,6 @@
 # GPU settings
 tf_ops.gpu_growth()
 
-set_random_seed(1234)
 # Settings
 config = Config(
     n_states=5,

osl_dynamics/data/base.py

@@ -1166,13 +1166,11 @@ def dataset(
                 return full_dataset.prefetch(tf.data.AUTOTUNE)
 
             else:
-                # Calculate how many batches should be in the training dataset
-                dataset_size = len(full_dataset)
-                training_dataset_size = round((1.0 - validation_split) * dataset_size)
-
                 # Split the full dataset into a training and validation dataset
-                training_dataset = full_dataset.take(training_dataset_size)
-                validation_dataset = full_dataset.skip(training_dataset_size)
+                training_dataset, validation_dataset = tf.keras.utils.split_dataset(
+                    full_dataset,
+                    right_size=validation_split,
+                )
                 _logger.info(
                     f"{len(training_dataset)} batches in training dataset, "
                     f"{len(validation_dataset)} batches in the validation "
@@ -1209,28 +1207,16 @@ def dataset(
                 training_datasets = []
                 validation_datasets = []
                 for i in range(len(full_datasets)):
-                    # Calculate the number of batches in the training dataset
-                    dataset_size = len(full_datasets[i])
-                    training_dataset_size = round(
-                        (1.0 - validation_split) * dataset_size
-                    )
-
-                    # Split this session's dataset
-                    training_datasets.append(
-                        full_datasets[i]
-                        .take(training_dataset_size)
-                        .prefetch(tf.data.AUTOTUNE)
-                    )
-                    validation_datasets.append(
-                        full_datasets[i]
-                        .skip(training_dataset_size)
-                        .prefetch(tf.data.AUTOTUNE)
+                    tds, vds = tf.keras.utils.split_dataset(
+                        full_datasets[i],
+                        right_size=validation_split,
                     )
+                    training_datasets.append(tds.prefetch(tf.data.AUTOTUNE))
+                    validation_datasets.append(vds.prefetch(tf.data.AUTOTUNE))
                     _logger.info(
                         f"Session {i}: "
-                        f"{len(training_datasets[i])} batches in training dataset, "
-                        f"{len(validation_datasets[i])} batches in the validation "
-                        "dataset."
+                        f"{len(tds)} batches in training dataset, "
+                        f"{len(vds)} batches in the validation dataset."
                     )
                 return training_datasets, validation_datasets
 

osl_dynamics/data/tf.py

@@ -279,17 +279,15 @@ def _parse_example(example):
             return full_dataset.prefetch(tf.data.AUTOTUNE)
 
         else:
-            # Calculate how many batches should be in the training dataset
-            dataset_size = dtf.get_n_batches(full_dataset)
-            training_dataset_size = round((1.0 - validation_split) * dataset_size)
-
             # Split the dataset into training and validation datasets
-            training_dataset = full_dataset.take(training_dataset_size)
-            validation_dataset = full_dataset.skip(training_dataset_size)
+            training_dataset, validation_dataset = tf.keras.utils.split_dataset(
+                full_dataset,
+                right_size=validation_split,
+            )
             _logger.info(
-                f"{training_dataset_size} batches in training dataset, "
-                + f"{dataset_size - training_dataset_size} batches in the validation "
-                + "dataset."
+                f"{len(training_dataset)} batches in training dataset, "
+                f"{len(validation_dataset)} batches in the validation "
+                "dataset."
             )
             return training_dataset.prefetch(
                 tf.data.AUTOTUNE
@@ -326,18 +324,14 @@ def _parse_example(example):
             training_datasets = []
             validation_datasets = []
             for i, ds in enumerate(full_datasets):
-                # Calculate how many batches should be in the training dataset
-                dataset_size = dtf.get_n_batches(ds)
-                training_dataset_size = round((1.0 - validation_split) * dataset_size)
-
-                # Split the dataset into training and validation datasets
-                training_datasets.append(ds.take(training_dataset_size))
-                validation_datasets.append(ds.skip(training_dataset_size))
+                tds, vds = tf.keras.utils.split_dataset(
+                    full_datasets[i],
+                    right_size=validation_split,
+                )
                 _logger.info(
                     f"Session {i}: "
-                    + f"{training_dataset_size} batches in training dataset, "
-                    + f"{dataset_size - training_dataset_size} batches in the validation "
-                    + "dataset."
+                    f"{len(tds)} batches in training dataset, "
+                    f"{len(vds)} batches in the validation dataset."
                 )
 
             return training_datasets, validation_datasets

osl_dynamics/inference/initializers.py

@@ -2,8 +2,6 @@
 
 """
 
-from copy import deepcopy
-
 import numpy as np
 import tensorflow as tf
 import tensorflow_probability as tfp
@@ -211,7 +209,8 @@ def reinitialize_layer_weights(layer):
             #
             # We need to create a new initializer to get new
             # random values
-            new_initializer = deepcopy(initializer)
+            config = initializer.get_config()
+            new_initializer = initializer_type.from_config(config)
 
         # Get the variable (i.e. weights) we want to re-initialize
         if key == "recurrent_initializer":