Mixing
tensorflow-serving signature for an XOR
1
I am trying to export my first xor NN using tensorflow serving but I am not getting any result when I call the gRPC.
Here the code I use to predict the XOR
import tensorflow as tf
sess = tf.Session()
from keras import backend as K
K.set_session(sess)
K.set_learning_phase(0) # all new operations will be in test mode from now on
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
model_version = "2" #Change this to export different model versions, i.e. 2, ..., 7
epoch = 100 ## the higher this number is the more accurate the prediction will be 10000 is a good number to s
et it at just takes a while to train
#Exhaustion of Different Possibilities
X = np.array([
[0,0],
[0,1],
[1,0],
[1,1]
])
#Return values of the different inputs
Y = np.array([[0],[1],[1],[0]])
#Create Model
model = Sequential()
model.add(Dense(8, input_dim=2))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)
model.fit(X, Y, batch_size=1, nb_epoch=epoch)
test = np.array([[0.0,0.0]])
#setting values for the sake of saving the model in the proper format
x = model.input
y = model.output
print('Results of Model', model.predict_proba(X))
prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def({"inputs": x}, {"prediction":
y})
valid_prediction_signature = tf.saved_model.signature_def_utils.is_valid_signature(prediction_signature)
if(valid_prediction_signature == False):
raise ValueError("Error: Prediction signature not valid!")
builder = saved_model_builder.SavedModelBuilder('./'+model_version)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
# Add the meta_graph and the variables to the builder
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature,
},
legacy_init_op=legacy_init_op)
# save the graph
builder.save()
Implement in docker
docker run -p 8501:8501 --mount type=bind,source=/root/tensorflow3/projects/example/xor_keras_tensorflow_serving,target=/models/xor -e MODEL_NAME=xor -t tensorflow/serving &
then I request the prediction by the following:
curl -d '{"inputs": [1,1]}' -X POST http://localhost:8501/v2/models/xor
the result is always this
<HTML><HEAD>
<TITLE>404 Not Found</TITLE>
</HEAD><BODY>
<H1>Not Found</H1>
</BODY></HTML>
Can you help me to find where I wrong?
I have tried to change "inputs" in the curl with "instances", but nothing
Thanks,
Manuel
tensorflow tensorflow-serving
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
add a comment |
1
I am trying to export my first xor NN using tensorflow serving but I am not getting any result when I call the gRPC.
Here the code I use to predict the XOR
import tensorflow as tf
sess = tf.Session()
from keras import backend as K
K.set_session(sess)
K.set_learning_phase(0) # all new operations will be in test mode from now on
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
model_version = "2" #Change this to export different model versions, i.e. 2, ..., 7
epoch = 100 ## the higher this number is the more accurate the prediction will be 10000 is a good number to s
et it at just takes a while to train
#Exhaustion of Different Possibilities
X = np.array([
[0,0],
[0,1],
[1,0],
[1,1]
])
#Return values of the different inputs
Y = np.array([[0],[1],[1],[0]])
#Create Model
model = Sequential()
model.add(Dense(8, input_dim=2))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)
model.fit(X, Y, batch_size=1, nb_epoch=epoch)
test = np.array([[0.0,0.0]])
#setting values for the sake of saving the model in the proper format
x = model.input
y = model.output
print('Results of Model', model.predict_proba(X))
prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def({"inputs": x}, {"prediction":
y})
valid_prediction_signature = tf.saved_model.signature_def_utils.is_valid_signature(prediction_signature)
if(valid_prediction_signature == False):
raise ValueError("Error: Prediction signature not valid!")
builder = saved_model_builder.SavedModelBuilder('./'+model_version)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
# Add the meta_graph and the variables to the builder
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature,
},
legacy_init_op=legacy_init_op)
# save the graph
builder.save()
Implement in docker
docker run -p 8501:8501 --mount type=bind,source=/root/tensorflow3/projects/example/xor_keras_tensorflow_serving,target=/models/xor -e MODEL_NAME=xor -t tensorflow/serving &
then I request the prediction by the following:
curl -d '{"inputs": [1,1]}' -X POST http://localhost:8501/v2/models/xor
the result is always this
<HTML><HEAD>
<TITLE>404 Not Found</TITLE>
</HEAD><BODY>
<H1>Not Found</H1>
</BODY></HTML>
Can you help me to find where I wrong?
I have tried to change "inputs" in the curl with "instances", but nothing
Thanks,
Manuel
tensorflow tensorflow-serving
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
add a comment |
1
1
1
1
I am trying to export my first xor NN using tensorflow serving but I am not getting any result when I call the gRPC.
Here the code I use to predict the XOR
import tensorflow as tf
sess = tf.Session()
from keras import backend as K
K.set_session(sess)
K.set_learning_phase(0) # all new operations will be in test mode from now on
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
model_version = "2" #Change this to export different model versions, i.e. 2, ..., 7
epoch = 100 ## the higher this number is the more accurate the prediction will be 10000 is a good number to s
et it at just takes a while to train
#Exhaustion of Different Possibilities
X = np.array([
[0,0],
[0,1],
[1,0],
[1,1]
])
#Return values of the different inputs
Y = np.array([[0],[1],[1],[0]])
#Create Model
model = Sequential()
model.add(Dense(8, input_dim=2))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)
model.fit(X, Y, batch_size=1, nb_epoch=epoch)
test = np.array([[0.0,0.0]])
#setting values for the sake of saving the model in the proper format
x = model.input
y = model.output
print('Results of Model', model.predict_proba(X))
prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def({"inputs": x}, {"prediction":
y})
valid_prediction_signature = tf.saved_model.signature_def_utils.is_valid_signature(prediction_signature)
if(valid_prediction_signature == False):
raise ValueError("Error: Prediction signature not valid!")
builder = saved_model_builder.SavedModelBuilder('./'+model_version)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
# Add the meta_graph and the variables to the builder
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature,
},
legacy_init_op=legacy_init_op)
# save the graph
builder.save()
Implement in docker
docker run -p 8501:8501 --mount type=bind,source=/root/tensorflow3/projects/example/xor_keras_tensorflow_serving,target=/models/xor -e MODEL_NAME=xor -t tensorflow/serving &
then I request the prediction by the following:
curl -d '{"inputs": [1,1]}' -X POST http://localhost:8501/v2/models/xor
the result is always this
<HTML><HEAD>
<TITLE>404 Not Found</TITLE>
</HEAD><BODY>
<H1>Not Found</H1>
</BODY></HTML>
Can you help me to find where I wrong?
I have tried to change "inputs" in the curl with "instances", but nothing
Thanks,
Manuel
tensorflow tensorflow-serving
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
I am trying to export my first xor NN using tensorflow serving but I am not getting any result when I call the gRPC.
Here the code I use to predict the XOR
import tensorflow as tf
sess = tf.Session()
from keras import backend as K
K.set_session(sess)
K.set_learning_phase(0) # all new operations will be in test mode from now on
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
model_version = "2" #Change this to export different model versions, i.e. 2, ..., 7
epoch = 100 ## the higher this number is the more accurate the prediction will be 10000 is a good number to s
et it at just takes a while to train
#Exhaustion of Different Possibilities
X = np.array([
[0,0],
[0,1],
[1,0],
[1,1]
])
#Return values of the different inputs
Y = np.array([[0],[1],[1],[0]])
#Create Model
model = Sequential()
model.add(Dense(8, input_dim=2))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)
model.fit(X, Y, batch_size=1, nb_epoch=epoch)
test = np.array([[0.0,0.0]])
#setting values for the sake of saving the model in the proper format
x = model.input
y = model.output
print('Results of Model', model.predict_proba(X))
prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def({"inputs": x}, {"prediction":
y})
valid_prediction_signature = tf.saved_model.signature_def_utils.is_valid_signature(prediction_signature)
if(valid_prediction_signature == False):
raise ValueError("Error: Prediction signature not valid!")
builder = saved_model_builder.SavedModelBuilder('./'+model_version)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
# Add the meta_graph and the variables to the builder
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature,
},
legacy_init_op=legacy_init_op)
# save the graph
builder.save()
Implement in docker
docker run -p 8501:8501 --mount type=bind,source=/root/tensorflow3/projects/example/xor_keras_tensorflow_serving,target=/models/xor -e MODEL_NAME=xor -t tensorflow/serving &
then I request the prediction by the following:
curl -d '{"inputs": [1,1]}' -X POST http://localhost:8501/v2/models/xor
the result is always this
<HTML><HEAD>
<TITLE>404 Not Found</TITLE>
</HEAD><BODY>
<H1>Not Found</H1>
</BODY></HTML>
Can you help me to find where I wrong?
I have tried to change "inputs" in the curl with "instances", but nothing
Thanks,
Manuel
tensorflow tensorflow-serving
tensorflow tensorflow-serving
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
asked Nov 19 '18 at 18:09
Manuel JanManuel Jan
84
84
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
0
Can you first try
curl http://localhost:8501/v1/models/xor
to check if the model is running? This should return the status of your model.
From RESTful API doc, the format is GET http://host:port/v1/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
answered Nov 21 '18 at 4:41
YidingYiding
766
add a comment |
0
Thanks! you got the point! so I solved.
Actually there were 2 errors to the curl command:
- localhost:8501/v1/models/xor I put v2 thinking to use the version #2, but if you put v2 it does not work. It seems v# it is not the versiono of your saved model
- Also I need to specify the predict so the exact request is: http://localhost:8501/v1/models/xor:predict
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
Can you first try
curl http://localhost:8501/v1/models/xor
to check if the model is running? This should return the status of your model.
From RESTful API doc, the format is GET http://host:port/v1/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
answered Nov 21 '18 at 4:41
YidingYiding
766
add a comment |
0
Can you first try
curl http://localhost:8501/v1/models/xor
to check if the model is running? This should return the status of your model.
From RESTful API doc, the format is GET http://host:port/v1/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
answered Nov 21 '18 at 4:41
YidingYiding
766
add a comment |
0
0
0
Can you first try
curl http://localhost:8501/v1/models/xor
to check if the model is running? This should return the status of your model.
From RESTful API doc, the format is GET http://host:port/v1/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
answered Nov 21 '18 at 4:41
YidingYiding
766
Can you first try
curl http://localhost:8501/v1/models/xor
to check if the model is running? This should return the status of your model.
From RESTful API doc, the format is GET http://host:port/v1/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
answered Nov 21 '18 at 4:41
YidingYiding
766
edited Nov 22 '18 at 16:08
answered Nov 21 '18 at 4:41
YidingYiding
766
answered Nov 21 '18 at 4:41
YidingYiding
766
answered Nov 21 '18 at 4:41
YidingYiding
766
766
add a comment |
add a comment |
0
Thanks! you got the point! so I solved.
Actually there were 2 errors to the curl command:
- localhost:8501/v1/models/xor I put v2 thinking to use the version #2, but if you put v2 it does not work. It seems v# it is not the versiono of your saved model
- Also I need to specify the predict so the exact request is: http://localhost:8501/v1/models/xor:predict
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
add a comment |
0
Thanks! you got the point! so I solved.
Actually there were 2 errors to the curl command:
- localhost:8501/v1/models/xor I put v2 thinking to use the version #2, but if you put v2 it does not work. It seems v# it is not the versiono of your saved model
- Also I need to specify the predict so the exact request is: http://localhost:8501/v1/models/xor:predict
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
add a comment |
0
0
0
Thanks! you got the point! so I solved.
Actually there were 2 errors to the curl command:
- localhost:8501/v1/models/xor I put v2 thinking to use the version #2, but if you put v2 it does not work. It seems v# it is not the versiono of your saved model
- Also I need to specify the predict so the exact request is: http://localhost:8501/v1/models/xor:predict
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
Thanks! you got the point! so I solved.
Actually there were 2 errors to the curl command:
- localhost:8501/v1/models/xor I put v2 thinking to use the version #2, but if you put v2 it does not work. It seems v# it is not the versiono of your saved model
- Also I need to specify the predict so the exact request is: http://localhost:8501/v1/models/xor:predict
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
answered Nov 22 '18 at 14:34
Manuel JanManuel Jan
84
84
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
add a comment |
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
glad to help. just edited my answer about model version.
– Yiding
Nov 22 '18 at 16:09
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
Thanks Yiding, very appreciated!
– Manuel Jan
Nov 22 '18 at 17:16
add a comment |
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