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base: robocars:415d39c5bb7860f684c024168edda0269b0ed62f
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robocars:master
robocars:feat/linear_model
robocars:1.8.0-py3
robocars:1.4.1-py3
robocars:v0.2.0
robocars:v0.1.0
..
compare: robocars:7d7d2af62228e774f8c8a2e7f037912580d7a088
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robocars:master
robocars:feat/linear_model
robocars:1.8.0-py3
robocars:1.4.1-py3
robocars:v0.2.0
robocars:v0.1.0

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poetry.lock generated
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1
pyproject.toml
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@ -12,7 +12,6 @@ python = ">=3.10,<3.11"
tensorflow = "^2.10.0" tensorflow = "^2.10.0"
numpy = "^1.23.4" numpy = "^1.23.4"
pillow = "^8.3.2" pillow = "^8.3.2"
flatbuffers = "<2.11"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
mypy = "^0.982" mypy = "^0.982"

2
tf_container/train.py
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@ -187,7 +187,7 @@ def train(model_type: str, record_field: str, batch_size: int, slide_size: int,
# Save model for tensorflow using # Save model for tensorflow using
model.save(f'/opt/ml/model/model_{record_field.replace("_", "")}_{model_type}_{str(img_width)}x{str(img_height)}h{str(horizon)}') model.save(f'/opt/ml/model/model_{record_field.replace("_", "")}_{model_type}_{str(img_width)}x{str(img_height)}h{str(horizon)}')
def representative_dataset() -> typing.Generator[typing.List[float], typing.Any, None]: def representative_dataset() -> typing.Generator[typing.List[tf.float32], typing.Any, None]:
for d in tf.data.Dataset.from_tensor_slices(images).batch(1).take(100): for d in tf.data.Dataset.from_tensor_slices(images).batch(1).take(100):
yield [tf.dtypes.cast(d, tf.float32)] yield [tf.dtypes.cast(d, tf.float32)]