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how do I access a specific value in a tensor?
The Next CEO of Stack OverflowHow to merge two dictionaries in a single expression?How do I check if a list is empty?How do I check whether a file exists without exceptions?How can I safely create a nested directory in Python?Accessing the index in 'for' loops?How do I sort a dictionary by value?How to make a chain of function decorators?How do I list all files of a directory?How to access environment variable values?How can I freeze last layer of my own model?
0
I have an input layer wtm=Input(4,4,1) and I want to access each value of this layer during learning. for accessing to wtm[1,1]( the value in row=1 and column=1) I use this code a=Kr.layers.Lambda(lambda x:x[1,1])(wtm) but the output shape is TensorShape([Dimension(4), Dimension(1)]) not (1,1) and I think it gives the first column.is it right? if I only need one value in specific row and column what should I do and how can I change it? I really need your help. I know this could be easy but I am a beginner and do not know how to work with this issue:(
Edit:
suppose
wtm=
1 0 0 1
1 1 1 0
1 0 1 0
1 0 1 1
we know wtm(0,0)=1 now I want to produce new tensor with shape (28,28,1) with value 1 and I want to do this for all values in wtm.
wtm=Input((4,4,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
BN=BatchNormalization()(conv3)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,a])
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
BNd=BatchNormalization()(deconv4)
#DrO2=Dropout(0.25,name='DrO2')(BNd)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
#model=Model(inputs=image,outputs=decoded)
model=Model(inputs=[image,wtm],outputs=decoded)
decoded_noise = GaussianNoise(0.5)(decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (5,5), activation='relu', name='conl1w')(decoded_noise)#24
convw2 = Conv2D(64, (5,5), activation='relu', name='convl2w')(convw1)#20
#Avw1=AveragePooling2D(pool_size=(2,2))(convw2)
convw3 = Conv2D(64, (5,5), activation='relu' ,name='conl3w')(convw2)#16
convw4 = Conv2D(64, (5,5), activation='relu' ,name='conl4w')(convw3)#12
#Avw2=AveragePooling2D(pool_size=(2,2))(convw4)
convw5 = Conv2D(64, (5,5), activation='relu', name='conl5w')(convw4)#8
convw6 = Conv2D(64, (5,5), activation='relu', name='conl6w')(convw5)#4
convw7 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl7w',dilation_rate=(2,2))(convw6)#4
convw8 = Conv2D(64, (5,5), activation='relu', padding='same',name='conl8w',dilation_rate=(2,2))(convw7)#4
convw9 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl9w',dilation_rate=(2,2))(convw8)#4
convw10 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl10w',dilation_rate=(2,2))(convw9)#4
BNed=BatchNormalization()(convw10)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(BNed)
w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
w_extraction.summary()
python keras tensor
asked Mar 21 at 17:23
daviddavid
246
add a comment |
0
I have an input layer wtm=Input(4,4,1) and I want to access each value of this layer during learning. for accessing to wtm[1,1]( the value in row=1 and column=1) I use this code a=Kr.layers.Lambda(lambda x:x[1,1])(wtm) but the output shape is TensorShape([Dimension(4), Dimension(1)]) not (1,1) and I think it gives the first column.is it right? if I only need one value in specific row and column what should I do and how can I change it? I really need your help. I know this could be easy but I am a beginner and do not know how to work with this issue:(
Edit:
suppose
wtm=
1 0 0 1
1 1 1 0
1 0 1 0
1 0 1 1
we know wtm(0,0)=1 now I want to produce new tensor with shape (28,28,1) with value 1 and I want to do this for all values in wtm.
wtm=Input((4,4,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
BN=BatchNormalization()(conv3)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,a])
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
BNd=BatchNormalization()(deconv4)
#DrO2=Dropout(0.25,name='DrO2')(BNd)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
#model=Model(inputs=image,outputs=decoded)
model=Model(inputs=[image,wtm],outputs=decoded)
decoded_noise = GaussianNoise(0.5)(decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (5,5), activation='relu', name='conl1w')(decoded_noise)#24
convw2 = Conv2D(64, (5,5), activation='relu', name='convl2w')(convw1)#20
#Avw1=AveragePooling2D(pool_size=(2,2))(convw2)
convw3 = Conv2D(64, (5,5), activation='relu' ,name='conl3w')(convw2)#16
convw4 = Conv2D(64, (5,5), activation='relu' ,name='conl4w')(convw3)#12
#Avw2=AveragePooling2D(pool_size=(2,2))(convw4)
convw5 = Conv2D(64, (5,5), activation='relu', name='conl5w')(convw4)#8
convw6 = Conv2D(64, (5,5), activation='relu', name='conl6w')(convw5)#4
convw7 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl7w',dilation_rate=(2,2))(convw6)#4
convw8 = Conv2D(64, (5,5), activation='relu', padding='same',name='conl8w',dilation_rate=(2,2))(convw7)#4
convw9 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl9w',dilation_rate=(2,2))(convw8)#4
convw10 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl10w',dilation_rate=(2,2))(convw9)#4
BNed=BatchNormalization()(convw10)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(BNed)
w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
w_extraction.summary()
python keras tensor
asked Mar 21 at 17:23
daviddavid
246
Can you please show us a Minimal, Complete, and Verifiable example of what you have tried so far?
– IonicSolutions
Mar 21 at 17:56
I put the complete code above but my problem is the part I said before and I do not know can I access each value in tensor?
– david
Mar 21 at 18:11
why the output shape of this code Kr.layers.Lambda(lambda x:x[1,1])(wtm) is (4,1)? I think it should be (1,1). what is the problem?
– david
Mar 21 at 21:33
Can you create a smaller example of what you are trying to accomplish, please? It's quite difficult to see from your long code snippet what exactly you're asking. Also, please include all necessary import statements so that we can run the code on our own systems.
– IonicSolutions
Mar 23 at 15:26
I add an example that shows what I need.
– david
Mar 25 at 17:40
add a comment |
0
0
0
I have an input layer wtm=Input(4,4,1) and I want to access each value of this layer during learning. for accessing to wtm[1,1]( the value in row=1 and column=1) I use this code a=Kr.layers.Lambda(lambda x:x[1,1])(wtm) but the output shape is TensorShape([Dimension(4), Dimension(1)]) not (1,1) and I think it gives the first column.is it right? if I only need one value in specific row and column what should I do and how can I change it? I really need your help. I know this could be easy but I am a beginner and do not know how to work with this issue:(
Edit:
suppose
wtm=
1 0 0 1
1 1 1 0
1 0 1 0
1 0 1 1
we know wtm(0,0)=1 now I want to produce new tensor with shape (28,28,1) with value 1 and I want to do this for all values in wtm.
wtm=Input((4,4,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
BN=BatchNormalization()(conv3)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,a])
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
BNd=BatchNormalization()(deconv4)
#DrO2=Dropout(0.25,name='DrO2')(BNd)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
#model=Model(inputs=image,outputs=decoded)
model=Model(inputs=[image,wtm],outputs=decoded)
decoded_noise = GaussianNoise(0.5)(decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (5,5), activation='relu', name='conl1w')(decoded_noise)#24
convw2 = Conv2D(64, (5,5), activation='relu', name='convl2w')(convw1)#20
#Avw1=AveragePooling2D(pool_size=(2,2))(convw2)
convw3 = Conv2D(64, (5,5), activation='relu' ,name='conl3w')(convw2)#16
convw4 = Conv2D(64, (5,5), activation='relu' ,name='conl4w')(convw3)#12
#Avw2=AveragePooling2D(pool_size=(2,2))(convw4)
convw5 = Conv2D(64, (5,5), activation='relu', name='conl5w')(convw4)#8
convw6 = Conv2D(64, (5,5), activation='relu', name='conl6w')(convw5)#4
convw7 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl7w',dilation_rate=(2,2))(convw6)#4
convw8 = Conv2D(64, (5,5), activation='relu', padding='same',name='conl8w',dilation_rate=(2,2))(convw7)#4
convw9 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl9w',dilation_rate=(2,2))(convw8)#4
convw10 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl10w',dilation_rate=(2,2))(convw9)#4
BNed=BatchNormalization()(convw10)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(BNed)
w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
w_extraction.summary()
python keras tensor
asked Mar 21 at 17:23
daviddavid
246
I have an input layer wtm=Input(4,4,1) and I want to access each value of this layer during learning. for accessing to wtm[1,1]( the value in row=1 and column=1) I use this code a=Kr.layers.Lambda(lambda x:x[1,1])(wtm) but the output shape is TensorShape([Dimension(4), Dimension(1)]) not (1,1) and I think it gives the first column.is it right? if I only need one value in specific row and column what should I do and how can I change it? I really need your help. I know this could be easy but I am a beginner and do not know how to work with this issue:(
Edit:
suppose
wtm=
1 0 0 1
1 1 1 0
1 0 1 0
1 0 1 1
we know wtm(0,0)=1 now I want to produce new tensor with shape (28,28,1) with value 1 and I want to do this for all values in wtm.
wtm=Input((4,4,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
BN=BatchNormalization()(conv3)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,a])
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
BNd=BatchNormalization()(deconv4)
#DrO2=Dropout(0.25,name='DrO2')(BNd)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
#model=Model(inputs=image,outputs=decoded)
model=Model(inputs=[image,wtm],outputs=decoded)
decoded_noise = GaussianNoise(0.5)(decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (5,5), activation='relu', name='conl1w')(decoded_noise)#24
convw2 = Conv2D(64, (5,5), activation='relu', name='convl2w')(convw1)#20
#Avw1=AveragePooling2D(pool_size=(2,2))(convw2)
convw3 = Conv2D(64, (5,5), activation='relu' ,name='conl3w')(convw2)#16
convw4 = Conv2D(64, (5,5), activation='relu' ,name='conl4w')(convw3)#12
#Avw2=AveragePooling2D(pool_size=(2,2))(convw4)
convw5 = Conv2D(64, (5,5), activation='relu', name='conl5w')(convw4)#8
convw6 = Conv2D(64, (5,5), activation='relu', name='conl6w')(convw5)#4
convw7 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl7w',dilation_rate=(2,2))(convw6)#4
convw8 = Conv2D(64, (5,5), activation='relu', padding='same',name='conl8w',dilation_rate=(2,2))(convw7)#4
convw9 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl9w',dilation_rate=(2,2))(convw8)#4
convw10 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl10w',dilation_rate=(2,2))(convw9)#4
BNed=BatchNormalization()(convw10)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(BNed)
w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
w_extraction.summary()
python keras tensor
python keras tensor
asked Mar 21 at 17:23
daviddavid
246
asked Mar 21 at 17:23
daviddavid
246
edited Mar 25 at 17:39
david
asked Mar 21 at 17:23
daviddavid
246
asked Mar 21 at 17:23
daviddavid
246
asked Mar 21 at 17:23
daviddavid
246
246
Can you please show us a Minimal, Complete, and Verifiable example of what you have tried so far?
– IonicSolutions
Mar 21 at 17:56
I put the complete code above but my problem is the part I said before and I do not know can I access each value in tensor?
– david
Mar 21 at 18:11
why the output shape of this code Kr.layers.Lambda(lambda x:x[1,1])(wtm) is (4,1)? I think it should be (1,1). what is the problem?
– david
Mar 21 at 21:33
Can you create a smaller example of what you are trying to accomplish, please? It's quite difficult to see from your long code snippet what exactly you're asking. Also, please include all necessary import statements so that we can run the code on our own systems.
– IonicSolutions
Mar 23 at 15:26
I add an example that shows what I need.
– david
Mar 25 at 17:40
add a comment |
Can you please show us a Minimal, Complete, and Verifiable example of what you have tried so far?
– IonicSolutions
Mar 21 at 17:56
I put the complete code above but my problem is the part I said before and I do not know can I access each value in tensor?
– david
Mar 21 at 18:11
why the output shape of this code Kr.layers.Lambda(lambda x:x[1,1])(wtm) is (4,1)? I think it should be (1,1). what is the problem?
– david
Mar 21 at 21:33
Can you create a smaller example of what you are trying to accomplish, please? It's quite difficult to see from your long code snippet what exactly you're asking. Also, please include all necessary import statements so that we can run the code on our own systems.
– IonicSolutions
Mar 23 at 15:26
I add an example that shows what I need.
– david
Mar 25 at 17:40
Can you please show us a Minimal, Complete, and Verifiable example of what you have tried so far?
– IonicSolutions
Mar 21 at 17:56
Can you please show us a Minimal, Complete, and Verifiable example of what you have tried so far?
– IonicSolutions
Mar 21 at 17:56
I put the complete code above but my problem is the part I said before and I do not know can I access each value in tensor?
– david
Mar 21 at 18:11
I put the complete code above but my problem is the part I said before and I do not know can I access each value in tensor?
– david
Mar 21 at 18:11
why the output shape of this code Kr.layers.Lambda(lambda x:x[1,1])(wtm) is (4,1)? I think it should be (1,1). what is the problem?
– david
Mar 21 at 21:33
why the output shape of this code Kr.layers.Lambda(lambda x:x[1,1])(wtm) is (4,1)? I think it should be (1,1). what is the problem?
– david
Mar 21 at 21:33
Can you create a smaller example of what you are trying to accomplish, please? It's quite difficult to see from your long code snippet what exactly you're asking. Also, please include all necessary import statements so that we can run the code on our own systems.
– IonicSolutions
Mar 23 at 15:26
Can you create a smaller example of what you are trying to accomplish, please? It's quite difficult to see from your long code snippet what exactly you're asking. Also, please include all necessary import statements so that we can run the code on our own systems.
– IonicSolutions
Mar 23 at 15:26
I add an example that shows what I need.
– david
Mar 25 at 17:40
I add an example that shows what I need.
– david
Mar 25 at 17:40
add a comment |
1 Answer
1
active
oldest
votes
0
I believe you're not taking into account that the first dimension is the batch dimension.
If you run
from keras.layers import Input, Lambda
def inspector(x):
print(x.shape)
return x
inp = Input((4, 4, 1))
lmb = Lambda(inspector)(inp)
you will see that it prints
(?, 4, 4, 1)
(?, 4, 4, 1)
indicating that x is four-dimensional.
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,x[1]will return the first entry of the batch, hencex[1, 1]will (as you discovered) return a (4,1)-dimensional tensor.
– IonicSolutions
Mar 26 at 17:15
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
add a comment |
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I believe you're not taking into account that the first dimension is the batch dimension.
If you run
from keras.layers import Input, Lambda
def inspector(x):
print(x.shape)
return x
inp = Input((4, 4, 1))
lmb = Lambda(inspector)(inp)
you will see that it prints
(?, 4, 4, 1)
(?, 4, 4, 1)
indicating that x is four-dimensional.
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,x[1]will return the first entry of the batch, hencex[1, 1]will (as you discovered) return a (4,1)-dimensional tensor.
– IonicSolutions
Mar 26 at 17:15
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
add a comment |
0
I believe you're not taking into account that the first dimension is the batch dimension.
If you run
from keras.layers import Input, Lambda
def inspector(x):
print(x.shape)
return x
inp = Input((4, 4, 1))
lmb = Lambda(inspector)(inp)
you will see that it prints
(?, 4, 4, 1)
(?, 4, 4, 1)
indicating that x is four-dimensional.
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,x[1]will return the first entry of the batch, hencex[1, 1]will (as you discovered) return a (4,1)-dimensional tensor.
– IonicSolutions
Mar 26 at 17:15
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
add a comment |
0
0
0
I believe you're not taking into account that the first dimension is the batch dimension.
If you run
from keras.layers import Input, Lambda
def inspector(x):
print(x.shape)
return x
inp = Input((4, 4, 1))
lmb = Lambda(inspector)(inp)
you will see that it prints
(?, 4, 4, 1)
(?, 4, 4, 1)
indicating that x is four-dimensional.
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
I believe you're not taking into account that the first dimension is the batch dimension.
If you run
from keras.layers import Input, Lambda
def inspector(x):
print(x.shape)
return x
inp = Input((4, 4, 1))
lmb = Lambda(inspector)(inp)
you will see that it prints
(?, 4, 4, 1)
(?, 4, 4, 1)
indicating that x is four-dimensional.
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
answered Mar 26 at 10:45
IonicSolutionsIonicSolutions
877718
877718
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,x[1]will return the first entry of the batch, hencex[1, 1]will (as you discovered) return a (4,1)-dimensional tensor.
– IonicSolutions
Mar 26 at 17:15
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
add a comment |
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,x[1]will return the first entry of the batch, hencex[1, 1]will (as you discovered) return a (4,1)-dimensional tensor.
– IonicSolutions
Mar 26 at 17:15
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I know the first dimension is batch and the second and third dimension are the size of the image and the last one is the number of the channel that is here my images are gray and so it will be 1, but I need to access the values of the image for each sample in the batch.
– david
Mar 26 at 14:05
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,
x[1] will return the first entry of the batch, hence x[1, 1] will (as you discovered) return a (4,1)-dimensional tensor.– IonicSolutions
Mar 26 at 17:15
I suggest you try to work your way forward from here, carefully checking the dimensions. If I'm not mistaken,
x[1] will return the first entry of the batch, hence x[1, 1] will (as you discovered) return a (4,1)-dimensional tensor.– IonicSolutions
Mar 26 at 17:15
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
could you answer me this question? suppose wtm is the same as the example I put above. and if we consider wtm as an image with size 4x4 the wtm( 0,0) is 1. now if we consier wtm=Input( 4,4,1), what is the value of wtm[:,0,0,:]? is it 1 or not?
– david
Mar 26 at 19:30
add a comment |
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Can you please show us a Minimal, Complete, and Verifiable example of what you have tried so far?
– IonicSolutions
Mar 21 at 17:56
I put the complete code above but my problem is the part I said before and I do not know can I access each value in tensor?
– david
Mar 21 at 18:11
why the output shape of this code Kr.layers.Lambda(lambda x:x[1,1])(wtm) is (4,1)? I think it should be (1,1). what is the problem?
– david
Mar 21 at 21:33
Can you create a smaller example of what you are trying to accomplish, please? It's quite difficult to see from your long code snippet what exactly you're asking. Also, please include all necessary import statements so that we can run the code on our own systems.
– IonicSolutions
Mar 23 at 15:26
I add an example that shows what I need.
– david
Mar 25 at 17:40