Keras + Tensorflow CNN with multiple image inputs












0












$begingroup$


I have a CNN that needs to take in 68 images that are all 59x59 pixels. The CNN should output 136 values on the output layer



My training data has shape (-1, 68, 59, 59, 1).



My current approach is to use concatenate to join multiple networks like so:



input_layer = [None] * 68

x = [None] * 68

for i in range(68):

    input_layer[i] = tf.keras.layers.Input(shape=training_data.shape[1:][1:])

    x[i] = Conv2D(64, (5,5))(input_layer[i])

    x[i] = LeakyReLU(alpha=0.3)(x[i])

    x[i] = MaxPooling2D(pool_size=(2,2))(x[i])

    x[i] = Model(inputs=input_layer[i], outputs=x[i])



combined = concatenate(x)


However, this always gives the error:



ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs


Is this approach a suitable approach or am I doing this completely wrong?






keras tensorflow cnn







share|improve this question









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Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • $begingroup$
    Isn't this: shape=training_data.shape[1:][1:] the same for each loop?
    $endgroup$
    – Stephen Rauch
    2 hours ago
















0












$begingroup$


I have a CNN that needs to take in 68 images that are all 59x59 pixels. The CNN should output 136 values on the output layer



My training data has shape (-1, 68, 59, 59, 1).



My current approach is to use concatenate to join multiple networks like so:



input_layer = [None] * 68

x = [None] * 68

for i in range(68):

    input_layer[i] = tf.keras.layers.Input(shape=training_data.shape[1:][1:])

    x[i] = Conv2D(64, (5,5))(input_layer[i])

    x[i] = LeakyReLU(alpha=0.3)(x[i])

    x[i] = MaxPooling2D(pool_size=(2,2))(x[i])

    x[i] = Model(inputs=input_layer[i], outputs=x[i])



combined = concatenate(x)


However, this always gives the error:



ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs


Is this approach a suitable approach or am I doing this completely wrong?






keras tensorflow cnn







share|improve this question









New contributor




Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$












  • $begingroup$
    Isn't this: shape=training_data.shape[1:][1:] the same for each loop?
    $endgroup$
    – Stephen Rauch
    2 hours ago














0












0








0





$begingroup$


I have a CNN that needs to take in 68 images that are all 59x59 pixels. The CNN should output 136 values on the output layer



My training data has shape (-1, 68, 59, 59, 1).



My current approach is to use concatenate to join multiple networks like so:



input_layer = [None] * 68

x = [None] * 68

for i in range(68):

    input_layer[i] = tf.keras.layers.Input(shape=training_data.shape[1:][1:])

    x[i] = Conv2D(64, (5,5))(input_layer[i])

    x[i] = LeakyReLU(alpha=0.3)(x[i])

    x[i] = MaxPooling2D(pool_size=(2,2))(x[i])

    x[i] = Model(inputs=input_layer[i], outputs=x[i])



combined = concatenate(x)


However, this always gives the error:



ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs


Is this approach a suitable approach or am I doing this completely wrong?






keras tensorflow cnn







share|improve this question









New contributor




Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$




I have a CNN that needs to take in 68 images that are all 59x59 pixels. The CNN should output 136 values on the output layer



My training data has shape (-1, 68, 59, 59, 1).



My current approach is to use concatenate to join multiple networks like so:



input_layer = [None] * 68

x = [None] * 68

for i in range(68):

    input_layer[i] = tf.keras.layers.Input(shape=training_data.shape[1:][1:])

    x[i] = Conv2D(64, (5,5))(input_layer[i])

    x[i] = LeakyReLU(alpha=0.3)(x[i])

    x[i] = MaxPooling2D(pool_size=(2,2))(x[i])

    x[i] = Model(inputs=input_layer[i], outputs=x[i])



combined = concatenate(x)


However, this always gives the error:



ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs


Is this approach a suitable approach or am I doing this completely wrong?






keras tensorflow cnn




keras tensorflow cnn






share|improve this question









New contributor




Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|improve this question









New contributor




Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|improve this question




share|improve this question








edited 2 hours ago









Stephen Rauch

1,52551330




1,52551330






New contributor




Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









asked 4 hours ago









Charley PearceCharley Pearce

1




1




New contributor




Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.





New contributor





Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






Charley Pearce is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.












  • $begingroup$
    Isn't this: shape=training_data.shape[1:][1:] the same for each loop?
    $endgroup$
    – Stephen Rauch
    2 hours ago


















  • $begingroup$
    Isn't this: shape=training_data.shape[1:][1:] the same for each loop?
    $endgroup$
    – Stephen Rauch
    2 hours ago
















$begingroup$
Isn't this: shape=training_data.shape[1:][1:] the same for each loop?
$endgroup$
– Stephen Rauch
2 hours ago




$begingroup$
Isn't this: shape=training_data.shape[1:][1:] the same for each loop?
$endgroup$
– Stephen Rauch
2 hours ago










1 Answer
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$begingroup$

No loop is required. You can treat each of 68 images as a channel, for this, you need to move, and squeeze your data axes to (-1, 59, 59, 68) to have 59x59 images with 68 channels, i.e. Input((59, 59, 68)), similar to an RGB image that has 3 channels, the rest is the same.



If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across multiple frames too; this is done by 3D kernels instead of 2D kernels. It requires (-1, 59, 59, 68, 1) data shape and Input((59, 59, 68, 1)).






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    $begingroup$

    No loop is required. You can treat each of 68 images as a channel, for this, you need to move, and squeeze your data axes to (-1, 59, 59, 68) to have 59x59 images with 68 channels, i.e. Input((59, 59, 68)), similar to an RGB image that has 3 channels, the rest is the same.



    If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across multiple frames too; this is done by 3D kernels instead of 2D kernels. It requires (-1, 59, 59, 68, 1) data shape and Input((59, 59, 68, 1)).






    share|improve this answer









    $endgroup$


















      0












      $begingroup$

      No loop is required. You can treat each of 68 images as a channel, for this, you need to move, and squeeze your data axes to (-1, 59, 59, 68) to have 59x59 images with 68 channels, i.e. Input((59, 59, 68)), similar to an RGB image that has 3 channels, the rest is the same.



      If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across multiple frames too; this is done by 3D kernels instead of 2D kernels. It requires (-1, 59, 59, 68, 1) data shape and Input((59, 59, 68, 1)).






      share|improve this answer









      $endgroup$
















        0












        0








        0





        $begingroup$

        No loop is required. You can treat each of 68 images as a channel, for this, you need to move, and squeeze your data axes to (-1, 59, 59, 68) to have 59x59 images with 68 channels, i.e. Input((59, 59, 68)), similar to an RGB image that has 3 channels, the rest is the same.



        If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across multiple frames too; this is done by 3D kernels instead of 2D kernels. It requires (-1, 59, 59, 68, 1) data shape and Input((59, 59, 68, 1)).






        share|improve this answer









        $endgroup$



        No loop is required. You can treat each of 68 images as a channel, for this, you need to move, and squeeze your data axes to (-1, 59, 59, 68) to have 59x59 images with 68 channels, i.e. Input((59, 59, 68)), similar to an RGB image that has 3 channels, the rest is the same.



        If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across multiple frames too; this is done by 3D kernels instead of 2D kernels. It requires (-1, 59, 59, 68, 1) data shape and Input((59, 59, 68, 1)).







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 1 hour ago









        EsmailianEsmailian

        2,670318




        2,670318






















            Charley Pearce is a new contributor. Be nice, and check out our Code of Conduct.










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