Corrupted Graph in Keras models when they are converted to TensorFlow graph
I developed two keras models inside two unit tests. I converted the models into tensorflow graph (using https://github.com/amir-abdi/keras_to_tensorflow) to store on disk. When the tests are run separately, the models are loaded fine and work as they are expected to do. But when I run the test through unittest discover, I got the following error running the second test:
Tensor dense_2_target:0, specified in either feed_devices or fetch_devices was not found in the Graph.
I am wondering if it is a cause of any open resources or dependencies between the generated graphs? Any help is appreciated.
Here is the source code for the two models.
Model 1:
model = Sequential(name="Regressor")
model.add(Dense(10, input_dim=2, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation=None))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph() # as described in https://github.com/amir-abdi/keras_to_tensorflow.
Model 2:
model = Sequential(name="classifier")
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph()
As you see, the structure of the models are not the same.
python tensorflow machine-learning keras deep-learning
asked Nov 19 '18 at 18:16
mehdimehdi
498
add a comment |
I developed two keras models inside two unit tests. I converted the models into tensorflow graph (using https://github.com/amir-abdi/keras_to_tensorflow) to store on disk. When the tests are run separately, the models are loaded fine and work as they are expected to do. But when I run the test through unittest discover, I got the following error running the second test:
Tensor dense_2_target:0, specified in either feed_devices or fetch_devices was not found in the Graph.
I am wondering if it is a cause of any open resources or dependencies between the generated graphs? Any help is appreciated.
Here is the source code for the two models.
Model 1:
model = Sequential(name="Regressor")
model.add(Dense(10, input_dim=2, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation=None))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph() # as described in https://github.com/amir-abdi/keras_to_tensorflow.
Model 2:
model = Sequential(name="classifier")
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph()
As you see, the structure of the models are not the same.
python tensorflow machine-learning keras deep-learning
asked Nov 19 '18 at 18:16
mehdimehdi
498
add a comment |
I developed two keras models inside two unit tests. I converted the models into tensorflow graph (using https://github.com/amir-abdi/keras_to_tensorflow) to store on disk. When the tests are run separately, the models are loaded fine and work as they are expected to do. But when I run the test through unittest discover, I got the following error running the second test:
Tensor dense_2_target:0, specified in either feed_devices or fetch_devices was not found in the Graph.
I am wondering if it is a cause of any open resources or dependencies between the generated graphs? Any help is appreciated.
Here is the source code for the two models.
Model 1:
model = Sequential(name="Regressor")
model.add(Dense(10, input_dim=2, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation=None))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph() # as described in https://github.com/amir-abdi/keras_to_tensorflow.
Model 2:
model = Sequential(name="classifier")
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph()
As you see, the structure of the models are not the same.
python tensorflow machine-learning keras deep-learning
asked Nov 19 '18 at 18:16
mehdimehdi
498
I developed two keras models inside two unit tests. I converted the models into tensorflow graph (using https://github.com/amir-abdi/keras_to_tensorflow) to store on disk. When the tests are run separately, the models are loaded fine and work as they are expected to do. But when I run the test through unittest discover, I got the following error running the second test:
Tensor dense_2_target:0, specified in either feed_devices or fetch_devices was not found in the Graph.
I am wondering if it is a cause of any open resources or dependencies between the generated graphs? Any help is appreciated.
Here is the source code for the two models.
Model 1:
model = Sequential(name="Regressor")
model.add(Dense(10, input_dim=2, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation=None))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph() # as described in https://github.com/amir-abdi/keras_to_tensorflow.
Model 2:
model = Sequential(name="classifier")
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X, y, verbose=0)
convert_to_tensorflow_graph()
As you see, the structure of the models are not the same.
python tensorflow machine-learning keras deep-learning
python tensorflow machine-learning keras deep-learning
asked Nov 19 '18 at 18:16
mehdimehdi
498
asked Nov 19 '18 at 18:16
mehdimehdi
498
edited Nov 20 '18 at 15:59
mehdi
asked Nov 19 '18 at 18:16
mehdimehdi
498
asked Nov 19 '18 at 18:16
mehdimehdi
498
asked Nov 19 '18 at 18:16
mehdimehdi
498
498
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53380472%2fcorrupted-graph-in-keras-models-when-they-are-converted-to-tensorflow-graph%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53380472%2fcorrupted-graph-in-keras-models-when-they-are-converted-to-tensorflow-graph%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
