Keras Loss Value Extremely High + Prediction Result same
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All the Keras Deep Learning tutorials feature an already wrapped image dataset, and there's just one load method to load it all.
I have a set of images and a corresponding csv file for targets of those images. I can't use the Image Data Generator as it's a regression problem and not a labeling one. So I made a custom numpy array of the following:
a numpy array of (448, 448, 3) images
a numpy array of corresponding target numbers
When this is fed into the model, I face no error/ exception. Except the output looks ridiculously bad. The loss encountered is extremely high (in thousands), and the data really does not look so incoherent. Maybe it's the model. Here's the description:
Sequential with 2 Conv2D Layers (64, 32), flattened it to feed to a Dense layer of 16 and 8, and then one final Dense layer with 1 output node with no activation function (because, regression). [Also tried to scale the image values to [0,1], but no luck.]
As a noob in this domain, I have no idea where to begin to know where I could be going wrong. If it's the way I'm loading up the data, can anyone guide me to how to go about with that. Thanks.
Just Observed:
The target test values are in the range 1 - 15 with whole numbers as the target value. The predictions are all the same, with a value around 0.00065. What could be the reason for this behaviour?
neural-network deep-learning keras regression
asked 13 hours ago
thegravitythegravity
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All the Keras Deep Learning tutorials feature an already wrapped image dataset, and there's just one load method to load it all.
I have a set of images and a corresponding csv file for targets of those images. I can't use the Image Data Generator as it's a regression problem and not a labeling one. So I made a custom numpy array of the following:
a numpy array of (448, 448, 3) images
a numpy array of corresponding target numbers
When this is fed into the model, I face no error/ exception. Except the output looks ridiculously bad. The loss encountered is extremely high (in thousands), and the data really does not look so incoherent. Maybe it's the model. Here's the description:
Sequential with 2 Conv2D Layers (64, 32), flattened it to feed to a Dense layer of 16 and 8, and then one final Dense layer with 1 output node with no activation function (because, regression). [Also tried to scale the image values to [0,1], but no luck.]
As a noob in this domain, I have no idea where to begin to know where I could be going wrong. If it's the way I'm loading up the data, can anyone guide me to how to go about with that. Thanks.
Just Observed:
The target test values are in the range 1 - 15 with whole numbers as the target value. The predictions are all the same, with a value around 0.00065. What could be the reason for this behaviour?
neural-network deep-learning keras regression
asked 13 hours ago
thegravitythegravity
11
New contributor
thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
All the Keras Deep Learning tutorials feature an already wrapped image dataset, and there's just one load method to load it all.
I have a set of images and a corresponding csv file for targets of those images. I can't use the Image Data Generator as it's a regression problem and not a labeling one. So I made a custom numpy array of the following:
a numpy array of (448, 448, 3) images
a numpy array of corresponding target numbers
When this is fed into the model, I face no error/ exception. Except the output looks ridiculously bad. The loss encountered is extremely high (in thousands), and the data really does not look so incoherent. Maybe it's the model. Here's the description:
Sequential with 2 Conv2D Layers (64, 32), flattened it to feed to a Dense layer of 16 and 8, and then one final Dense layer with 1 output node with no activation function (because, regression). [Also tried to scale the image values to [0,1], but no luck.]
As a noob in this domain, I have no idea where to begin to know where I could be going wrong. If it's the way I'm loading up the data, can anyone guide me to how to go about with that. Thanks.
Just Observed:
The target test values are in the range 1 - 15 with whole numbers as the target value. The predictions are all the same, with a value around 0.00065. What could be the reason for this behaviour?
neural-network deep-learning keras regression
asked 13 hours ago
thegravitythegravity
11
New contributor
thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
All the Keras Deep Learning tutorials feature an already wrapped image dataset, and there's just one load method to load it all.
I have a set of images and a corresponding csv file for targets of those images. I can't use the Image Data Generator as it's a regression problem and not a labeling one. So I made a custom numpy array of the following:
a numpy array of (448, 448, 3) images
a numpy array of corresponding target numbers
When this is fed into the model, I face no error/ exception. Except the output looks ridiculously bad. The loss encountered is extremely high (in thousands), and the data really does not look so incoherent. Maybe it's the model. Here's the description:
Sequential with 2 Conv2D Layers (64, 32), flattened it to feed to a Dense layer of 16 and 8, and then one final Dense layer with 1 output node with no activation function (because, regression). [Also tried to scale the image values to [0,1], but no luck.]
As a noob in this domain, I have no idea where to begin to know where I could be going wrong. If it's the way I'm loading up the data, can anyone guide me to how to go about with that. Thanks.
Just Observed:
The target test values are in the range 1 - 15 with whole numbers as the target value. The predictions are all the same, with a value around 0.00065. What could be the reason for this behaviour?
neural-network deep-learning keras regression
neural-network deep-learning keras regression
asked 13 hours ago
thegravitythegravity
11
New contributor
thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 13 hours ago
thegravitythegravity
11
New contributor
thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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edited 13 hours ago
thegravity
asked 13 hours ago
thegravitythegravity
11
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thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 13 hours ago
thegravitythegravity
11
asked 13 hours ago
thegravitythegravity
11
11
New contributor
thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
thegravity is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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