Keras + Tensorflow CNN with multiple image inputsCustom layer in keras with multiple input and multiple outputKeras shape error in applications Inception Resnet v2Keras CNN image input and outputMy Keras CNN doesn't learnCan't train input variable with Keras+TensorflowMultiple-input multiple-output CNN with custom loss functionKeras Attention Guided CNN problemwhat happens to the depth channels when convolved by multiple filters in a cnn (keras, tensorflow)Keras/TF: Making sure image training data shape is accurate for Time Distributed CNN+LSTMArchitecture help for multivariate input and output LSTM models
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Keras + Tensorflow CNN with multiple image inputs
Custom layer in keras with multiple input and multiple outputKeras shape error in applications Inception Resnet v2Keras CNN image input and outputMy Keras CNN doesn't learnCan't train input variable with Keras+TensorflowMultiple-input multiple-output CNN with custom loss functionKeras Attention Guided CNN problemwhat happens to the depth channels when convolved by multiple filters in a cnn (keras, tensorflow)Keras/TF: Making sure image training data shape is accurate for Time Distributed CNN+LSTMArchitecture help for multivariate input and output LSTM models
$begingroup$
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
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
$endgroup$
add a comment |
$begingroup$
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
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
$endgroup$
$begingroup$
Isn't this:shape=training_data.shape[1:][1:]the same for each loop?
$endgroup$
– Stephen Rauch♦
Apr 7 at 23:53
add a comment |
$begingroup$
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
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
$endgroup$
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
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
edited Apr 7 at 23:52
Stephen Rauch♦
1,53551330
1,53551330
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
asked Apr 7 at 22:11
Charley PearceCharley Pearce
132
132
$begingroup$
Isn't this:shape=training_data.shape[1:][1:]the same for each loop?
$endgroup$
– Stephen Rauch♦
Apr 7 at 23:53
add a comment |
$begingroup$
Isn't this:shape=training_data.shape[1:][1:]the same for each loop?
$endgroup$
– Stephen Rauch♦
Apr 7 at 23:53
$begingroup$
Isn't this:
shape=training_data.shape[1:][1:] the same for each loop?$endgroup$
– Stephen Rauch♦
Apr 7 at 23:53
$begingroup$
Isn't this:
shape=training_data.shape[1:][1:] the same for each loop?$endgroup$
– Stephen Rauch♦
Apr 7 at 23:53
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Yes it is wrong, each (68, 59, 59) input should go through one model not an array of them.
You can treat each of 68 images as a channel, for this, you need to squeeze your data axes from
(-1, 68, 59, 59, 1)to(-1, 68, 59, 59)to have a 59x59 image with 68 channels corresponding toInput((68, 59, 59)), and setdata_format='channels_first'in conv2D, to let the layer know that channels are in the first dimension (it expects them to be in the last dimension by default). This is similar to an RGB image that has 3 channels corresponding toInput((59, 59, 3)). The rest is the same.If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across neighbor frames too; this is done by 3D kernels instead of 2D kernels. It requires
(-1, 68, 59, 59, 1)data shape corresponding toInput((68, 59, 59, 1)). Also, we should use the defaultdata_format='channels_last'since now there is only one channel as the last dimension. Commonly, temporal axis is placed third, i.e.(-1, 59, 59, 68, 1), which can be accomplished by moving the axes.
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
$endgroup$
add a comment |
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$begingroup$
Yes it is wrong, each (68, 59, 59) input should go through one model not an array of them.
You can treat each of 68 images as a channel, for this, you need to squeeze your data axes from
(-1, 68, 59, 59, 1)to(-1, 68, 59, 59)to have a 59x59 image with 68 channels corresponding toInput((68, 59, 59)), and setdata_format='channels_first'in conv2D, to let the layer know that channels are in the first dimension (it expects them to be in the last dimension by default). This is similar to an RGB image that has 3 channels corresponding toInput((59, 59, 3)). The rest is the same.If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across neighbor frames too; this is done by 3D kernels instead of 2D kernels. It requires
(-1, 68, 59, 59, 1)data shape corresponding toInput((68, 59, 59, 1)). Also, we should use the defaultdata_format='channels_last'since now there is only one channel as the last dimension. Commonly, temporal axis is placed third, i.e.(-1, 59, 59, 68, 1), which can be accomplished by moving the axes.
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
$endgroup$
add a comment |
$begingroup$
Yes it is wrong, each (68, 59, 59) input should go through one model not an array of them.
You can treat each of 68 images as a channel, for this, you need to squeeze your data axes from
(-1, 68, 59, 59, 1)to(-1, 68, 59, 59)to have a 59x59 image with 68 channels corresponding toInput((68, 59, 59)), and setdata_format='channels_first'in conv2D, to let the layer know that channels are in the first dimension (it expects them to be in the last dimension by default). This is similar to an RGB image that has 3 channels corresponding toInput((59, 59, 3)). The rest is the same.If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across neighbor frames too; this is done by 3D kernels instead of 2D kernels. It requires
(-1, 68, 59, 59, 1)data shape corresponding toInput((68, 59, 59, 1)). Also, we should use the defaultdata_format='channels_last'since now there is only one channel as the last dimension. Commonly, temporal axis is placed third, i.e.(-1, 59, 59, 68, 1), which can be accomplished by moving the axes.
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
$endgroup$
add a comment |
$begingroup$
Yes it is wrong, each (68, 59, 59) input should go through one model not an array of them.
You can treat each of 68 images as a channel, for this, you need to squeeze your data axes from
(-1, 68, 59, 59, 1)to(-1, 68, 59, 59)to have a 59x59 image with 68 channels corresponding toInput((68, 59, 59)), and setdata_format='channels_first'in conv2D, to let the layer know that channels are in the first dimension (it expects them to be in the last dimension by default). This is similar to an RGB image that has 3 channels corresponding toInput((59, 59, 3)). The rest is the same.If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across neighbor frames too; this is done by 3D kernels instead of 2D kernels. It requires
(-1, 68, 59, 59, 1)data shape corresponding toInput((68, 59, 59, 1)). Also, we should use the defaultdata_format='channels_last'since now there is only one channel as the last dimension. Commonly, temporal axis is placed third, i.e.(-1, 59, 59, 68, 1), which can be accomplished by moving the axes.
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
$endgroup$
Yes it is wrong, each (68, 59, 59) input should go through one model not an array of them.
You can treat each of 68 images as a channel, for this, you need to squeeze your data axes from
(-1, 68, 59, 59, 1)to(-1, 68, 59, 59)to have a 59x59 image with 68 channels corresponding toInput((68, 59, 59)), and setdata_format='channels_first'in conv2D, to let the layer know that channels are in the first dimension (it expects them to be in the last dimension by default). This is similar to an RGB image that has 3 channels corresponding toInput((59, 59, 3)). The rest is the same.If 68 images are consecutive frames from a movie, you can use conv3D to extract motion patterns across neighbor frames too; this is done by 3D kernels instead of 2D kernels. It requires
(-1, 68, 59, 59, 1)data shape corresponding toInput((68, 59, 59, 1)). Also, we should use the defaultdata_format='channels_last'since now there is only one channel as the last dimension. Commonly, temporal axis is placed third, i.e.(-1, 59, 59, 68, 1), which can be accomplished by moving the axes.
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
edited Apr 8 at 16:28
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
answered Apr 8 at 0:39
EsmailianEsmailian
4,021422
4,021422
add a comment |
add a comment |
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$begingroup$
Isn't this:
shape=training_data.shape[1:][1:]the same for each loop?$endgroup$
– Stephen Rauch♦
Apr 7 at 23:53