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Upgrade TensorFlow version to v2.16.1 #2282

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Apr 2, 2024
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Upgrade TensorFlow version to v2.16.1 #2282

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e18011d
Upgrade TensorFlow version to v2.16.1
tenzen-y Mar 10, 2024
328a562
Replace deprecated ImageDataGenerator with new data augmentation appr…
tenzen-y Mar 27, 2024
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2 changes: 1 addition & 1 deletion cmd/metricscollector/v1beta1/tfevent-metricscollector/requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -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
2 changes: 1 addition & 1 deletion cmd/suggestion/nas/enas/v1beta1/requirements.txt
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@@ -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
17 changes: 10 additions & 7 deletions examples/v1beta1/trial-images/enas-cnn-cifar10/RunTrial.py
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Expand Up @@ -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

Expand Down Expand Up @@ -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),
])
Comment on lines -79 to +83
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This is a deprecated approach and moved to `tensorflow.keras.preprocessing.image.

Traceback (most recent call last):
  File "/opt/enas-cnn-cifar10/RunTrial.py", line 19, in <module>
    from keras.preprocessing.image import ImageDataGenerator
ImportError: cannot import name 'ImageDataGenerator' from 'keras.preprocessing.image' (/usr/local/lib/python3.11/site-packages/keras/preprocessing/image/__init__.py)

https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

So, I introduced the recommended approach.


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))
Expand Down
2 changes: 1 addition & 1 deletion examples/v1beta1/trial-images/enas-cnn-cifar10/requirements.txt
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@@ -1,2 +1,2 @@
scipy>=1.7.2
tensorflow==2.13.0
tensorflow==2.16.1
2 changes: 1 addition & 1 deletion examples/v1beta1/trial-images/tf-mnist-with-summaries/requirements.txt
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@@ -1 +1 @@
tensorflow==2.13.0
tensorflow==2.16.1
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