Getting keras predictions as a tensor graph for use in tensorflow
I currently have a custom LSTM model that I have saved as a .h5 file using save(). I am loading this model using load_model() during a tensorflow graph construction, and want to construct a part of the graph using the LSTM model's prediction output (which I therefore need in the form of a tensor). I have established the same session for the tensorflow graph and the keras backend graph, but I am having trouble connecting the output into my tensorflow code graph. Using the standard predict() seems to attempt to run the keras model's session, and I have scoured the internet for something other than hideously converting it to a .pb file and messing with it like that. It seems like it should be easy, considering I am using tensorflow as the Keras backend...Any ideas on how to achieve this?
python tensorflow keras
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
add a comment |
I currently have a custom LSTM model that I have saved as a .h5 file using save(). I am loading this model using load_model() during a tensorflow graph construction, and want to construct a part of the graph using the LSTM model's prediction output (which I therefore need in the form of a tensor). I have established the same session for the tensorflow graph and the keras backend graph, but I am having trouble connecting the output into my tensorflow code graph. Using the standard predict() seems to attempt to run the keras model's session, and I have scoured the internet for something other than hideously converting it to a .pb file and messing with it like that. It seems like it should be easy, considering I am using tensorflow as the Keras backend...Any ideas on how to achieve this?
python tensorflow keras
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
blog.keras.io/…
– Matias Valdenegro
Jan 1 at 13:44
thanks, that was helpful and is pretty much what I ended up doing (the chosen answer demos it some more)
– JCDeveloper
Jan 2 at 11:43
add a comment |
I currently have a custom LSTM model that I have saved as a .h5 file using save(). I am loading this model using load_model() during a tensorflow graph construction, and want to construct a part of the graph using the LSTM model's prediction output (which I therefore need in the form of a tensor). I have established the same session for the tensorflow graph and the keras backend graph, but I am having trouble connecting the output into my tensorflow code graph. Using the standard predict() seems to attempt to run the keras model's session, and I have scoured the internet for something other than hideously converting it to a .pb file and messing with it like that. It seems like it should be easy, considering I am using tensorflow as the Keras backend...Any ideas on how to achieve this?
python tensorflow keras
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
I currently have a custom LSTM model that I have saved as a .h5 file using save(). I am loading this model using load_model() during a tensorflow graph construction, and want to construct a part of the graph using the LSTM model's prediction output (which I therefore need in the form of a tensor). I have established the same session for the tensorflow graph and the keras backend graph, but I am having trouble connecting the output into my tensorflow code graph. Using the standard predict() seems to attempt to run the keras model's session, and I have scoured the internet for something other than hideously converting it to a .pb file and messing with it like that. It seems like it should be easy, considering I am using tensorflow as the Keras backend...Any ideas on how to achieve this?
python tensorflow keras
python tensorflow keras
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
asked Jan 1 at 13:40
JCDeveloperJCDeveloper
273
273
blog.keras.io/…
– Matias Valdenegro
Jan 1 at 13:44
thanks, that was helpful and is pretty much what I ended up doing (the chosen answer demos it some more)
– JCDeveloper
Jan 2 at 11:43
add a comment |
blog.keras.io/…
– Matias Valdenegro
Jan 1 at 13:44
thanks, that was helpful and is pretty much what I ended up doing (the chosen answer demos it some more)
– JCDeveloper
Jan 2 at 11:43
blog.keras.io/…
– Matias Valdenegro
Jan 1 at 13:44
blog.keras.io/…
– Matias Valdenegro
Jan 1 at 13:44
thanks, that was helpful and is pretty much what I ended up doing (the chosen answer demos it some more)
– JCDeveloper
Jan 2 at 11:43
thanks, that was helpful and is pretty much what I ended up doing (the chosen answer demos it some more)
– JCDeveloper
Jan 2 at 11:43
add a comment |
1 Answer
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I will show how to import saved keras model into tensorflow graph. I will show this using simple single layer feed forward model.
inputs = tf.keras.layers.Input(shape=(1,), name="inputs")
outputs = tf.keras.layers.Dense(1, activation="linear", name="outputs")(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(loss="mse", optimizer="adam")
model.save("model.h5")
Now let's load the model using load_model method of keras and use it in tensorflow to multiply the output of the model with new placeholder tensor.
model = tf.keras.models.load_model("model.h5")
model_output = model.output
new_tensor_ph = tf.placeholder(tf.float32, [None, 1])
new_output = tf.multiply(model_output, new_tensor_ph)
sess = tf.keras.backend.get_session()
prediction = sess.run(new_output, feed_dict={model.input:[[3]],new_tensor_ph :[[4]]})
## This works without error
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
add a comment |
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1 Answer
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active
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votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
I will show how to import saved keras model into tensorflow graph. I will show this using simple single layer feed forward model.
inputs = tf.keras.layers.Input(shape=(1,), name="inputs")
outputs = tf.keras.layers.Dense(1, activation="linear", name="outputs")(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(loss="mse", optimizer="adam")
model.save("model.h5")
Now let's load the model using load_model method of keras and use it in tensorflow to multiply the output of the model with new placeholder tensor.
model = tf.keras.models.load_model("model.h5")
model_output = model.output
new_tensor_ph = tf.placeholder(tf.float32, [None, 1])
new_output = tf.multiply(model_output, new_tensor_ph)
sess = tf.keras.backend.get_session()
prediction = sess.run(new_output, feed_dict={model.input:[[3]],new_tensor_ph :[[4]]})
## This works without error
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
add a comment |
I will show how to import saved keras model into tensorflow graph. I will show this using simple single layer feed forward model.
inputs = tf.keras.layers.Input(shape=(1,), name="inputs")
outputs = tf.keras.layers.Dense(1, activation="linear", name="outputs")(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(loss="mse", optimizer="adam")
model.save("model.h5")
Now let's load the model using load_model method of keras and use it in tensorflow to multiply the output of the model with new placeholder tensor.
model = tf.keras.models.load_model("model.h5")
model_output = model.output
new_tensor_ph = tf.placeholder(tf.float32, [None, 1])
new_output = tf.multiply(model_output, new_tensor_ph)
sess = tf.keras.backend.get_session()
prediction = sess.run(new_output, feed_dict={model.input:[[3]],new_tensor_ph :[[4]]})
## This works without error
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
add a comment |
I will show how to import saved keras model into tensorflow graph. I will show this using simple single layer feed forward model.
inputs = tf.keras.layers.Input(shape=(1,), name="inputs")
outputs = tf.keras.layers.Dense(1, activation="linear", name="outputs")(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(loss="mse", optimizer="adam")
model.save("model.h5")
Now let's load the model using load_model method of keras and use it in tensorflow to multiply the output of the model with new placeholder tensor.
model = tf.keras.models.load_model("model.h5")
model_output = model.output
new_tensor_ph = tf.placeholder(tf.float32, [None, 1])
new_output = tf.multiply(model_output, new_tensor_ph)
sess = tf.keras.backend.get_session()
prediction = sess.run(new_output, feed_dict={model.input:[[3]],new_tensor_ph :[[4]]})
## This works without error
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
I will show how to import saved keras model into tensorflow graph. I will show this using simple single layer feed forward model.
inputs = tf.keras.layers.Input(shape=(1,), name="inputs")
outputs = tf.keras.layers.Dense(1, activation="linear", name="outputs")(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(loss="mse", optimizer="adam")
model.save("model.h5")
Now let's load the model using load_model method of keras and use it in tensorflow to multiply the output of the model with new placeholder tensor.
model = tf.keras.models.load_model("model.h5")
model_output = model.output
new_tensor_ph = tf.placeholder(tf.float32, [None, 1])
new_output = tf.multiply(model_output, new_tensor_ph)
sess = tf.keras.backend.get_session()
prediction = sess.run(new_output, feed_dict={model.input:[[3]],new_tensor_ph :[[4]]})
## This works without error
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
answered Jan 1 at 14:06
MitikuMitiku
2,1491417
2,1491417
add a comment |
add a comment |
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blog.keras.io/…
– Matias Valdenegro
Jan 1 at 13:44
thanks, that was helpful and is pretty much what I ended up doing (the chosen answer demos it some more)
– JCDeveloper
Jan 2 at 11:43