Error when call prediction with base 64 input
I am using Tensorflow hub's example to export a saved_model to be serve with Tensorflow serving using Docker. (https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py)
I just followed some instruction on the internet and modified the export_model like below
def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
"""
# The SavedModel should hold the eval graph.
sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
# Shape of [None] means we can have a batch of images.
image = tf.placeholder(shape=[None], dtype=tf.string)
with sess.graph.as_default() as graph:
tf.saved_model.simple_save(
sess,
saved_model_dir,
#inputs={'image': in_image},
inputs = {'image_bytes': image},
outputs={'prediction': graph.get_tensor_by_name('final_result:0')},
legacy_init_op=tf.group(tf.tables_initializer(), name='legacy_init_op')
)
The problem is when i try to call the api using postman it came with this error
{
"error": "Tensor Placeholder_1:0, specified in either feed_devices or fetch_devices was not found in the Graph"
}
Do I need to modify the retraining process so it can accept base64 input?
tensorflow tensorflow-serving tensorflow-hub
asked Nov 20 '18 at 1:08
naoteenaotee
316
add a comment |
I am using Tensorflow hub's example to export a saved_model to be serve with Tensorflow serving using Docker. (https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py)
I just followed some instruction on the internet and modified the export_model like below
def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
"""
# The SavedModel should hold the eval graph.
sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
# Shape of [None] means we can have a batch of images.
image = tf.placeholder(shape=[None], dtype=tf.string)
with sess.graph.as_default() as graph:
tf.saved_model.simple_save(
sess,
saved_model_dir,
#inputs={'image': in_image},
inputs = {'image_bytes': image},
outputs={'prediction': graph.get_tensor_by_name('final_result:0')},
legacy_init_op=tf.group(tf.tables_initializer(), name='legacy_init_op')
)
The problem is when i try to call the api using postman it came with this error
{
"error": "Tensor Placeholder_1:0, specified in either feed_devices or fetch_devices was not found in the Graph"
}
Do I need to modify the retraining process so it can accept base64 input?
tensorflow tensorflow-serving tensorflow-hub
asked Nov 20 '18 at 1:08
naoteenaotee
316
add a comment |
I am using Tensorflow hub's example to export a saved_model to be serve with Tensorflow serving using Docker. (https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py)
I just followed some instruction on the internet and modified the export_model like below
def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
"""
# The SavedModel should hold the eval graph.
sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
# Shape of [None] means we can have a batch of images.
image = tf.placeholder(shape=[None], dtype=tf.string)
with sess.graph.as_default() as graph:
tf.saved_model.simple_save(
sess,
saved_model_dir,
#inputs={'image': in_image},
inputs = {'image_bytes': image},
outputs={'prediction': graph.get_tensor_by_name('final_result:0')},
legacy_init_op=tf.group(tf.tables_initializer(), name='legacy_init_op')
)
The problem is when i try to call the api using postman it came with this error
{
"error": "Tensor Placeholder_1:0, specified in either feed_devices or fetch_devices was not found in the Graph"
}
Do I need to modify the retraining process so it can accept base64 input?
tensorflow tensorflow-serving tensorflow-hub
asked Nov 20 '18 at 1:08
naoteenaotee
316
I am using Tensorflow hub's example to export a saved_model to be serve with Tensorflow serving using Docker. (https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py)
I just followed some instruction on the internet and modified the export_model like below
def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
"""
# The SavedModel should hold the eval graph.
sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
# Shape of [None] means we can have a batch of images.
image = tf.placeholder(shape=[None], dtype=tf.string)
with sess.graph.as_default() as graph:
tf.saved_model.simple_save(
sess,
saved_model_dir,
#inputs={'image': in_image},
inputs = {'image_bytes': image},
outputs={'prediction': graph.get_tensor_by_name('final_result:0')},
legacy_init_op=tf.group(tf.tables_initializer(), name='legacy_init_op')
)
The problem is when i try to call the api using postman it came with this error
{
"error": "Tensor Placeholder_1:0, specified in either feed_devices or fetch_devices was not found in the Graph"
}
Do I need to modify the retraining process so it can accept base64 input?
tensorflow tensorflow-serving tensorflow-hub
tensorflow tensorflow-serving tensorflow-hub
asked Nov 20 '18 at 1:08
naoteenaotee
316
asked Nov 20 '18 at 1:08
naoteenaotee
316
asked Nov 20 '18 at 1:08
naoteenaotee
316
asked Nov 20 '18 at 1:08
naoteenaotee
316
asked Nov 20 '18 at 1:08
naoteenaotee
316
316
add a comment |
add a comment |
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