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author | Nathan Bailey <nathan.bailey@arm.com> | 2024-03-20 08:13:39 +0000 |
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committer | Nathan Bailey <nathan.bailey@arm.com> | 2024-03-28 07:17:32 +0000 |
commit | f3f3ab451968350b8f6df2de7c60b2c2b9320b59 (patch) | |
tree | 05d56c8e41de9b32f8054019a21b78628151310d /tests/utils/common.py | |
parent | 5f063ae1cfbfa2568d2858af0a0ccaf192bb1e8d (diff) | |
download | mlia-f3f3ab451968350b8f6df2de7c60b2c2b9320b59.tar.gz |
feat: Update Vela version
Updates Vela Version to 3.11.0 and TensorFlow version to 2.15.1
Required keras import to change:
from keras.api._v2 import keras needed instead of calling tf.keras
Subsequently tf.keras.X needed to change to keras.X
Resolves: MLIA-1107
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I53bcaa9cdad58b0e6c311c8c6490393d33cb18bc
Diffstat (limited to 'tests/utils/common.py')
-rw-r--r-- | tests/utils/common.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tests/utils/common.py b/tests/utils/common.py index c29b47c..eafa31b 100644 --- a/tests/utils/common.py +++ b/tests/utils/common.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates. +# SPDX-FileCopyrightText: Copyright 2022-2024, Arm Limited and/or its affiliates. # SPDX-License-Identifier: Apache-2.0 """Common test utils module.""" from __future__ import annotations @@ -6,12 +6,12 @@ from __future__ import annotations from pathlib import Path import numpy as np -import tensorflow as tf +from keras.api._v2 import keras # Temporary workaround for now: MLIA-1107 def get_dataset() -> tuple[np.ndarray, np.ndarray]: """Return sample dataset.""" - mnist = tf.keras.datasets.mnist + mnist = keras.datasets.mnist (x_train, y_train), _ = mnist.load_data() x_train = x_train / 255.0 @@ -22,11 +22,11 @@ def get_dataset() -> tuple[np.ndarray, np.ndarray]: return x_train, y_train -def train_model(model: tf.keras.Model) -> None: +def train_model(model: keras.Model) -> None: """Train model using sample dataset.""" num_epochs = 1 - loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) + loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) x_train, y_train = get_dataset() |