Created
April 6, 2022 14:59
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tmp_saved_model
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MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs: | |
signature_def['__saved_model_init_op']: | |
The given SavedModel SignatureDef contains the following input(s): | |
The given SavedModel SignatureDef contains the following output(s): | |
outputs['__saved_model_init_op'] tensor_info: | |
dtype: DT_INVALID | |
shape: unknown_rank | |
name: NoOp | |
Method name is: | |
signature_def['serving_default']: | |
The given SavedModel SignatureDef contains the following input(s): | |
inputs['image_input'] tensor_info: | |
dtype: DT_FLOAT | |
shape: (-1, 224, 224, 3) | |
name: serving_default_image_input:0 | |
The given SavedModel SignatureDef contains the following output(s): | |
outputs['resnet50'] tensor_info: | |
dtype: DT_FLOAT | |
shape: (-1, 1000) | |
name: StatefulPartitionedCall:0 | |
Method name is: tensorflow/serving/predict | |
Concrete Functions: | |
Function Name: '__call__' | |
Option #1 | |
Callable with: | |
Argument #1 | |
inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs') | |
Argument #2 | |
DType: bool | |
Value: False | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Option #2 | |
Callable with: | |
Argument #1 | |
image_input: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='image_input') | |
Argument #2 | |
DType: bool | |
Value: False | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Option #3 | |
Callable with: | |
Argument #1 | |
inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs') | |
Argument #2 | |
DType: bool | |
Value: True | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Option #4 | |
Callable with: | |
Argument #1 | |
image_input: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='image_input') | |
Argument #2 | |
DType: bool | |
Value: True | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Function Name: '_default_save_signature' | |
Option #1 | |
Callable with: | |
Argument #1 | |
image_input: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='image_input') | |
Function Name: 'call_and_return_all_conditional_losses' | |
Option #1 | |
Callable with: | |
Argument #1 | |
image_input: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='image_input') | |
Argument #2 | |
DType: bool | |
Value: True | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Option #2 | |
Callable with: | |
Argument #1 | |
image_input: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='image_input') | |
Argument #2 | |
DType: bool | |
Value: False | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Option #3 | |
Callable with: | |
Argument #1 | |
inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs') | |
Argument #2 | |
DType: bool | |
Value: True | |
Argument #3 | |
DType: NoneType | |
Value: None | |
Option #4 | |
Callable with: | |
Argument #1 | |
inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs') | |
Argument #2 | |
DType: bool | |
Value: False | |
Argument #3 | |
DType: NoneType | |
Value: None |
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