Export tensorflow graph with export_saved_model
I'm trying to train and deploy simplified Quick, Draw! classifier from here on Google Cloud. I've managed to train model in GC, now stuck at deploying it, more precisely, at creating serving input functions.
I'm following instructions from here and having tough times trying to understand what type of input tensor should be.
Error:
TypeError: Failed to convert object of type to Tensor. Contents: SparseTensor(indices=Tensor("ParseExample/ParseExample:0", shape=(?, 2), dtype=int64), values=Tensor("ParseExample/ParseExample:1", shape=(?,), dtype=float32), dense_shape=Tensor("ParseExample/ParseExample:2", shape=(2,), dtype=int64)). Consider casting elements to a supported type.
Serving function:
def serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string, shape=[None], name='input_tensors')
receiver_tensors = {'infer_inputs': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
Feature specification:
feature_spec = {
"ink": tf.VarLenFeature(dtype=tf.float32),
"shape": tf.FixedLenFeature([2], dtype=tf.int64)
}
Input layer:
def _get_input_tensors(features, labels):
shapes = features["shape"]
lengths = tf.squeeze(
tf.slice(shapes, begin=[0, 0], size=[params.batch_size, 1]))
inks = tf.reshape(features["ink"], [params.batch_size, -1, 3])
if labels is not None:
labels = tf.squeeze(labels)
return inks, lengths, labels
Code of model and training data were taken here.
python tensorflow tensorflow-serving google-cloud-ml
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
add a comment |
I'm trying to train and deploy simplified Quick, Draw! classifier from here on Google Cloud. I've managed to train model in GC, now stuck at deploying it, more precisely, at creating serving input functions.
I'm following instructions from here and having tough times trying to understand what type of input tensor should be.
Error:
TypeError: Failed to convert object of type to Tensor. Contents: SparseTensor(indices=Tensor("ParseExample/ParseExample:0", shape=(?, 2), dtype=int64), values=Tensor("ParseExample/ParseExample:1", shape=(?,), dtype=float32), dense_shape=Tensor("ParseExample/ParseExample:2", shape=(2,), dtype=int64)). Consider casting elements to a supported type.
Serving function:
def serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string, shape=[None], name='input_tensors')
receiver_tensors = {'infer_inputs': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
Feature specification:
feature_spec = {
"ink": tf.VarLenFeature(dtype=tf.float32),
"shape": tf.FixedLenFeature([2], dtype=tf.int64)
}
Input layer:
def _get_input_tensors(features, labels):
shapes = features["shape"]
lengths = tf.squeeze(
tf.slice(shapes, begin=[0, 0], size=[params.batch_size, 1]))
inks = tf.reshape(features["ink"], [params.batch_size, -1, 3])
if labels is not None:
labels = tf.squeeze(labels)
return inks, lengths, labels
Code of model and training data were taken here.
python tensorflow tensorflow-serving google-cloud-ml
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
add a comment |
I'm trying to train and deploy simplified Quick, Draw! classifier from here on Google Cloud. I've managed to train model in GC, now stuck at deploying it, more precisely, at creating serving input functions.
I'm following instructions from here and having tough times trying to understand what type of input tensor should be.
Error:
TypeError: Failed to convert object of type to Tensor. Contents: SparseTensor(indices=Tensor("ParseExample/ParseExample:0", shape=(?, 2), dtype=int64), values=Tensor("ParseExample/ParseExample:1", shape=(?,), dtype=float32), dense_shape=Tensor("ParseExample/ParseExample:2", shape=(2,), dtype=int64)). Consider casting elements to a supported type.
Serving function:
def serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string, shape=[None], name='input_tensors')
receiver_tensors = {'infer_inputs': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
Feature specification:
feature_spec = {
"ink": tf.VarLenFeature(dtype=tf.float32),
"shape": tf.FixedLenFeature([2], dtype=tf.int64)
}
Input layer:
def _get_input_tensors(features, labels):
shapes = features["shape"]
lengths = tf.squeeze(
tf.slice(shapes, begin=[0, 0], size=[params.batch_size, 1]))
inks = tf.reshape(features["ink"], [params.batch_size, -1, 3])
if labels is not None:
labels = tf.squeeze(labels)
return inks, lengths, labels
Code of model and training data were taken here.
python tensorflow tensorflow-serving google-cloud-ml
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
I'm trying to train and deploy simplified Quick, Draw! classifier from here on Google Cloud. I've managed to train model in GC, now stuck at deploying it, more precisely, at creating serving input functions.
I'm following instructions from here and having tough times trying to understand what type of input tensor should be.
Error:
TypeError: Failed to convert object of type to Tensor. Contents: SparseTensor(indices=Tensor("ParseExample/ParseExample:0", shape=(?, 2), dtype=int64), values=Tensor("ParseExample/ParseExample:1", shape=(?,), dtype=float32), dense_shape=Tensor("ParseExample/ParseExample:2", shape=(2,), dtype=int64)). Consider casting elements to a supported type.
Serving function:
def serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string, shape=[None], name='input_tensors')
receiver_tensors = {'infer_inputs': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
Feature specification:
feature_spec = {
"ink": tf.VarLenFeature(dtype=tf.float32),
"shape": tf.FixedLenFeature([2], dtype=tf.int64)
}
Input layer:
def _get_input_tensors(features, labels):
shapes = features["shape"]
lengths = tf.squeeze(
tf.slice(shapes, begin=[0, 0], size=[params.batch_size, 1]))
inks = tf.reshape(features["ink"], [params.batch_size, -1, 3])
if labels is not None:
labels = tf.squeeze(labels)
return inks, lengths, labels
Code of model and training data were taken here.
python tensorflow tensorflow-serving google-cloud-ml
python tensorflow tensorflow-serving google-cloud-ml
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
asked Nov 13 '18 at 16:13
constantinopolskayaconstantinopolskaya
205
205
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Try this:
def serving_input_receiver_fn():
ink = tf.placeholder(dtype=tf.float32, shape=[None, None, 3], name='ink')
length = tf.placeholder(dtype=tf.int64, shape=[None, 1])
features = {"ink": inks, "length": lengths}
return tf.estimator.export.ServingInputReceiver(features, features)
An example payload would be:
{"instances": [{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]}
or as input to gcloud predict --json-instances:
{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]
I didn't look into the actual code; if ink is generally going to hold a lot of floats, you may want to consider an alternative encoding system.
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Try this:
def serving_input_receiver_fn():
ink = tf.placeholder(dtype=tf.float32, shape=[None, None, 3], name='ink')
length = tf.placeholder(dtype=tf.int64, shape=[None, 1])
features = {"ink": inks, "length": lengths}
return tf.estimator.export.ServingInputReceiver(features, features)
An example payload would be:
{"instances": [{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]}
or as input to gcloud predict --json-instances:
{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]
I didn't look into the actual code; if ink is generally going to hold a lot of floats, you may want to consider an alternative encoding system.
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
add a comment |
Try this:
def serving_input_receiver_fn():
ink = tf.placeholder(dtype=tf.float32, shape=[None, None, 3], name='ink')
length = tf.placeholder(dtype=tf.int64, shape=[None, 1])
features = {"ink": inks, "length": lengths}
return tf.estimator.export.ServingInputReceiver(features, features)
An example payload would be:
{"instances": [{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]}
or as input to gcloud predict --json-instances:
{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]
I didn't look into the actual code; if ink is generally going to hold a lot of floats, you may want to consider an alternative encoding system.
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
add a comment |
Try this:
def serving_input_receiver_fn():
ink = tf.placeholder(dtype=tf.float32, shape=[None, None, 3], name='ink')
length = tf.placeholder(dtype=tf.int64, shape=[None, 1])
features = {"ink": inks, "length": lengths}
return tf.estimator.export.ServingInputReceiver(features, features)
An example payload would be:
{"instances": [{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]}
or as input to gcloud predict --json-instances:
{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]
I didn't look into the actual code; if ink is generally going to hold a lot of floats, you may want to consider an alternative encoding system.
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
Try this:
def serving_input_receiver_fn():
ink = tf.placeholder(dtype=tf.float32, shape=[None, None, 3], name='ink')
length = tf.placeholder(dtype=tf.int64, shape=[None, 1])
features = {"ink": inks, "length": lengths}
return tf.estimator.export.ServingInputReceiver(features, features)
An example payload would be:
{"instances": [{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]}
or as input to gcloud predict --json-instances:
{"ink": [[0.1, 1.0, 2.0]], "length":[[1]]}]
I didn't look into the actual code; if ink is generally going to hold a lot of floats, you may want to consider an alternative encoding system.
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
answered Nov 13 '18 at 16:49
rhaertel80rhaertel80
6,15611634
6,15611634
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
add a comment |
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
Thanks, added shape to features and disabled adding loss and optimizers for prediction mode and it worked!
– constantinopolskaya
Nov 13 '18 at 19:08
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
happy to hear it!
– rhaertel80
Nov 15 '18 at 21:43
add a comment |
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