Upgrade TensorFlow version to v2.16.1 (#2282)

* Upgrade TensorFlow version to v2.16.1 Signed-off-by: Yuki Iwai <yuki.iwai.tz@gmail.com> * Replace deprecated ImageDataGenerator with new data augmentation approach Signed-off-by: Yuki Iwai <yuki.iwai.tz@gmail.com> --------- Signed-off-by: Yuki Iwai <yuki.iwai.tz@gmail.com>
kubeflow · Apr 2, 2024 · 9680b8c · 9680b8c
1 parent 8629a3c
commit 9680b8c
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Showing 5 changed files with 14 additions and 11 deletions.
diff --git a/cmd/metricscollector/v1beta1/tfevent-metricscollector/requirements.txt b/cmd/metricscollector/v1beta1/tfevent-metricscollector/requirements.txt
@@ -2,5 +2,5 @@ psutil==5.9.4
 rfc3339>=6.2
 grpcio>=1.41.1
 googleapis-common-protos==1.6.0
-tensorflow==2.13.0
+tensorflow==2.16.1
 protobuf<=3.20.3
diff --git a/cmd/suggestion/nas/enas/v1beta1/requirements.txt b/cmd/suggestion/nas/enas/v1beta1/requirements.txt
@@ -1,5 +1,5 @@
 grpcio>=1.41.1
 googleapis-common-protos==1.6.0
 cython>=0.29.24
-tensorflow==2.13.0
+tensorflow==2.16.1
 protobuf<=3.20.3
diff --git a/examples/v1beta1/trial-images/enas-cnn-cifar10/RunTrial.py b/examples/v1beta1/trial-images/enas-cnn-cifar10/RunTrial.py
@@ -16,7 +16,7 @@
 from keras.datasets import cifar10
 from ModelConstructor import ModelConstructor
 from tensorflow.keras.utils import to_categorical
-from keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.layers import RandomFlip, RandomTranslation, Rescaling
 import tensorflow as tf
 import argparse
 
@@ -76,18 +76,21 @@
     y_train = to_categorical(y_train)
     y_test = to_categorical(y_test)
 
-    augmentation = ImageDataGenerator(
-        width_shift_range=0.1,
-        height_shift_range=0.1,
-        horizontal_flip=True)
+    augmentation = tf.keras.Sequential([
+        Rescaling(1./255),
+        RandomFlip('horizontal'),
+        RandomTranslation(height_factor=0.1, width_factor=0.1),
+    ])
 
+    train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
+    train_dataset = train_dataset.map(lambda x, y: (augmentation(x, training=True), y))
     # TODO: Add batch size to args
-    aug_data_flow = augmentation.flow(x_train, y_train, batch_size=128)
+    train_dataset = train_dataset.batch(128)
 
     print(">>> Data Loaded. Training starts.")
     for e in range(num_epochs):
         print("\nTotal Epoch {}/{}".format(e + 1, num_epochs))
-        history = test_model.fit(aug_data_flow,
+        history = test_model.fit(train_dataset,
                                  steps_per_epoch=int(len(x_train) / 128) + 1,
                                  epochs=1, verbose=1,
                                  validation_data=(x_test, y_test))

diff --git a/examples/v1beta1/trial-images/enas-cnn-cifar10/requirements.txt b/examples/v1beta1/trial-images/enas-cnn-cifar10/requirements.txt
@@ -1,2 +1,2 @@
 scipy>=1.7.2
-tensorflow==2.13.0
+tensorflow==2.16.1
diff --git a/examples/v1beta1/trial-images/tf-mnist-with-summaries/requirements.txt b/examples/v1beta1/trial-images/tf-mnist-with-summaries/requirements.txt
@@ -1 +1 @@
-tensorflow==2.13.0
+tensorflow==2.16.1