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Merged
pull merged 2 commits into from
Sep 13, 2022
Merged

[pull] master from tensorflow:master #127

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32b0807
Code changes to get ready for an incoming Keras optimizer migration.
chenmoneygithub Sep 12, 2022
7746c45
Clean up:
tensorflower-gardener Sep 12, 2022
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Binary file removed lite/examples/posenet/ios/PoseNet/Assets.xcassets/AppIcon.appiconset/114.png
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Binary file removed lite/examples/posenet/ios/PoseNet/Assets.xcassets/AppIcon.appiconset/57.png
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181 changes: 180 additions & 1 deletion lite/examples/posenet/ios/PoseNet/Assets.xcassets/AppIcon.appiconset/Contents.json
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@@ -1 +1,180 @@
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{
"images": [
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"expected-size": "180",
"filename": "180.png",
"folder": "Assets.xcassets/AppIcon.appiconset/",
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},
{
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"expected-size": "29",
"filename": "29.png",
"folder": "Assets.xcassets/AppIcon.appiconset/",
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},
{
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"folder": "Assets.xcassets/AppIcon.appiconset/",
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]
}
21 changes: 13 additions & 8 deletions tensorflow_examples/lite/model_maker/core/task/train_image_classifier_lib.py
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Expand Up @@ -87,9 +87,12 @@ def create_optimizer(init_lr, num_decay_steps, num_warmup_steps):
initial_learning_rate=init_lr,
decay_schedule_fn=learning_rate_fn,
warmup_steps=num_warmup_steps)
optimizer = tf.keras.optimizers.RMSprop(
learning_rate=learning_rate_fn, rho=0.9, momentum=0.9, epsilon=0.001)

if hasattr(tf.keras.optimizers, "legacy"):
optimizer = tf.keras.optimizers.legacy.RMSprop(
learning_rate=learning_rate_fn, rho=0.9, momentum=0.9, epsilon=0.001)
else:
optimizer = tf.keras.optimizers.RMSprop(
learning_rate=learning_rate_fn, rho=0.9, momentum=0.9, epsilon=0.001)
return optimizer


Expand Down Expand Up @@ -131,11 +134,13 @@ def hub_train_model(model, hparams, train_ds, validation_ds, steps_per_epoch):
"""
loss = tf.keras.losses.CategoricalCrossentropy(
label_smoothing=hparams.label_smoothing)
model.compile(
optimizer=tf.keras.optimizers.SGD(
learning_rate=hparams.learning_rate, momentum=hparams.momentum),
loss=loss,
metrics=["accuracy"])
if hasattr(tf.keras.optimizers, "legacy"):
optimizer = tf.keras.optimizers.legacy.SGD(
learning_rate=hparams.learning_rate, momentum=hparams.momentum)
else:
optimizer = tf.keras.optimizers.SGD(
learning_rate=hparams.learning_rate, momentum=hparams.momentum)
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])

return model.fit(
train_ds,
Expand Down