Keras Input Layer Misinterpreting Input Shape
I am trying to make a very simple functional neural network in Keras. I input a vector of shape (270000,) to the network, and have entered this as the shape to accept in the input layer, but I receive the error shown below. Given that the shape printed for the input specified to be at fault, is in fact (270000,), I don't know why I am receiving this error.
Model Function
def spectrify(A1, y1, simData, aOrigShape):
print("A1: ", np.shape(A1))
print("y1: ", np.shape(y1))
print("simData", np.shape(simData))
print("aOrigShape:", aOrigShape)
dataIn = Input(shape=np.shape(A1))
dataOut = Dense(np.shape(A1)[0])(dataIn)
outShaper = Reshape((aOrigShape))(dataOut)
model = Model(inputs = dataIn, outputs = outShaper)
model.compile(optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = ['accuracy'])
model.fit(A1, simData)
return model
Execution
Running the function above prints the shapes and raises the following error:
A1: (270000,)
y1: (200, 540)
simData (200, 400)
aOrigShape: (500, 540)
...
<ipython-input-130-88e6c1dfc1c9> in spectrify(A1, y1, simData, aOrigShape)
12 loss = 'categorical_crossentropy',
13 metrics = ['accuracy'])
---> 14 model.fit(A1, simData)
15 return model
...
ValueError: Error when checking input: expected input_50 to have shape (270000,) but got array with shape (1,)
python machine-learning keras neural-network
asked Nov 16 '18 at 3:11
TQMTQM
194
add a comment |
I am trying to make a very simple functional neural network in Keras. I input a vector of shape (270000,) to the network, and have entered this as the shape to accept in the input layer, but I receive the error shown below. Given that the shape printed for the input specified to be at fault, is in fact (270000,), I don't know why I am receiving this error.
Model Function
def spectrify(A1, y1, simData, aOrigShape):
print("A1: ", np.shape(A1))
print("y1: ", np.shape(y1))
print("simData", np.shape(simData))
print("aOrigShape:", aOrigShape)
dataIn = Input(shape=np.shape(A1))
dataOut = Dense(np.shape(A1)[0])(dataIn)
outShaper = Reshape((aOrigShape))(dataOut)
model = Model(inputs = dataIn, outputs = outShaper)
model.compile(optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = ['accuracy'])
model.fit(A1, simData)
return model
Execution
Running the function above prints the shapes and raises the following error:
A1: (270000,)
y1: (200, 540)
simData (200, 400)
aOrigShape: (500, 540)
...
<ipython-input-130-88e6c1dfc1c9> in spectrify(A1, y1, simData, aOrigShape)
12 loss = 'categorical_crossentropy',
13 metrics = ['accuracy'])
---> 14 model.fit(A1, simData)
15 return model
...
ValueError: Error when checking input: expected input_50 to have shape (270000,) but got array with shape (1,)
python machine-learning keras neural-network
asked Nov 16 '18 at 3:11
TQMTQM
194
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
add a comment |
I am trying to make a very simple functional neural network in Keras. I input a vector of shape (270000,) to the network, and have entered this as the shape to accept in the input layer, but I receive the error shown below. Given that the shape printed for the input specified to be at fault, is in fact (270000,), I don't know why I am receiving this error.
Model Function
def spectrify(A1, y1, simData, aOrigShape):
print("A1: ", np.shape(A1))
print("y1: ", np.shape(y1))
print("simData", np.shape(simData))
print("aOrigShape:", aOrigShape)
dataIn = Input(shape=np.shape(A1))
dataOut = Dense(np.shape(A1)[0])(dataIn)
outShaper = Reshape((aOrigShape))(dataOut)
model = Model(inputs = dataIn, outputs = outShaper)
model.compile(optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = ['accuracy'])
model.fit(A1, simData)
return model
Execution
Running the function above prints the shapes and raises the following error:
A1: (270000,)
y1: (200, 540)
simData (200, 400)
aOrigShape: (500, 540)
...
<ipython-input-130-88e6c1dfc1c9> in spectrify(A1, y1, simData, aOrigShape)
12 loss = 'categorical_crossentropy',
13 metrics = ['accuracy'])
---> 14 model.fit(A1, simData)
15 return model
...
ValueError: Error when checking input: expected input_50 to have shape (270000,) but got array with shape (1,)
python machine-learning keras neural-network
asked Nov 16 '18 at 3:11
TQMTQM
194
I am trying to make a very simple functional neural network in Keras. I input a vector of shape (270000,) to the network, and have entered this as the shape to accept in the input layer, but I receive the error shown below. Given that the shape printed for the input specified to be at fault, is in fact (270000,), I don't know why I am receiving this error.
Model Function
def spectrify(A1, y1, simData, aOrigShape):
print("A1: ", np.shape(A1))
print("y1: ", np.shape(y1))
print("simData", np.shape(simData))
print("aOrigShape:", aOrigShape)
dataIn = Input(shape=np.shape(A1))
dataOut = Dense(np.shape(A1)[0])(dataIn)
outShaper = Reshape((aOrigShape))(dataOut)
model = Model(inputs = dataIn, outputs = outShaper)
model.compile(optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = ['accuracy'])
model.fit(A1, simData)
return model
Execution
Running the function above prints the shapes and raises the following error:
A1: (270000,)
y1: (200, 540)
simData (200, 400)
aOrigShape: (500, 540)
...
<ipython-input-130-88e6c1dfc1c9> in spectrify(A1, y1, simData, aOrigShape)
12 loss = 'categorical_crossentropy',
13 metrics = ['accuracy'])
---> 14 model.fit(A1, simData)
15 return model
...
ValueError: Error when checking input: expected input_50 to have shape (270000,) but got array with shape (1,)
python machine-learning keras neural-network
python machine-learning keras neural-network
asked Nov 16 '18 at 3:11
TQMTQM
194
asked Nov 16 '18 at 3:11
TQMTQM
194
edited Nov 16 '18 at 16:07
TQM
asked Nov 16 '18 at 3:11
TQMTQM
194
asked Nov 16 '18 at 3:11
TQMTQM
194
asked Nov 16 '18 at 3:11
TQMTQM
194
194
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
add a comment |
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55
add a comment |
1 Answer
1
active
oldest
votes
shape argument refers to the shape of one single sample in the training data. So if you have 270000 training samples of shape (1,), then the shape argument must be set to (1,). Otherwise, which is unlikely but possible, if you have one sample of shape (270000,) then you need the shape argument must be set to (270000,) and A must have a shape of (1, 270000), which means one sample of shape (270000,), and not (270000,) which means 270000 samples of shape (1,).
Generally, if X_train is the array which contains your training data, then it's a good practice to use X_train.shape[1:] (i.e. the shape of each sample) as the input shape, like this:
Input(shape=X_train.shape[1:])
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
add a comment |
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%2f53330884%2fkeras-input-layer-misinterpreting-input-shape%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
shape argument refers to the shape of one single sample in the training data. So if you have 270000 training samples of shape (1,), then the shape argument must be set to (1,). Otherwise, which is unlikely but possible, if you have one sample of shape (270000,) then you need the shape argument must be set to (270000,) and A must have a shape of (1, 270000), which means one sample of shape (270000,), and not (270000,) which means 270000 samples of shape (1,).
Generally, if X_train is the array which contains your training data, then it's a good practice to use X_train.shape[1:] (i.e. the shape of each sample) as the input shape, like this:
Input(shape=X_train.shape[1:])
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
add a comment |
shape argument refers to the shape of one single sample in the training data. So if you have 270000 training samples of shape (1,), then the shape argument must be set to (1,). Otherwise, which is unlikely but possible, if you have one sample of shape (270000,) then you need the shape argument must be set to (270000,) and A must have a shape of (1, 270000), which means one sample of shape (270000,), and not (270000,) which means 270000 samples of shape (1,).
Generally, if X_train is the array which contains your training data, then it's a good practice to use X_train.shape[1:] (i.e. the shape of each sample) as the input shape, like this:
Input(shape=X_train.shape[1:])
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
add a comment |
shape argument refers to the shape of one single sample in the training data. So if you have 270000 training samples of shape (1,), then the shape argument must be set to (1,). Otherwise, which is unlikely but possible, if you have one sample of shape (270000,) then you need the shape argument must be set to (270000,) and A must have a shape of (1, 270000), which means one sample of shape (270000,), and not (270000,) which means 270000 samples of shape (1,).
Generally, if X_train is the array which contains your training data, then it's a good practice to use X_train.shape[1:] (i.e. the shape of each sample) as the input shape, like this:
Input(shape=X_train.shape[1:])
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
shape argument refers to the shape of one single sample in the training data. So if you have 270000 training samples of shape (1,), then the shape argument must be set to (1,). Otherwise, which is unlikely but possible, if you have one sample of shape (270000,) then you need the shape argument must be set to (270000,) and A must have a shape of (1, 270000), which means one sample of shape (270000,), and not (270000,) which means 270000 samples of shape (1,).
Generally, if X_train is the array which contains your training data, then it's a good practice to use X_train.shape[1:] (i.e. the shape of each sample) as the input shape, like this:
Input(shape=X_train.shape[1:])
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
edited Nov 16 '18 at 10:35
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
answered Nov 16 '18 at 10:29
todaytoday
10.7k21837
10.7k21837
add a comment |
add a comment |
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%2f53330884%2fkeras-input-layer-misinterpreting-input-shape%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
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:55