getting the weights of intermediate layer in keras The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to Obtain Output of Intermediate Model in KerasHow to Create Shared Weights Layer in KerasKeras: visualizing the output of an intermediate layerDot Product between two Keras intermediate variablesWhat are default keras layer weightsKeras intermediate layer (attention model) outputSimple prediction with KerasValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)Value of loss and accuracy does not change over EpochsImages Score Regression only regresses to the average of the target values

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From jafe to El-Guest



getting the weights of intermediate layer in keras



The Next CEO of Stack Overflow
2019 Community Moderator ElectionHow to Obtain Output of Intermediate Model in KerasHow to Create Shared Weights Layer in KerasKeras: visualizing the output of an intermediate layerDot Product between two Keras intermediate variablesWhat are default keras layer weightsKeras intermediate layer (attention model) outputSimple prediction with KerasValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)Value of loss and accuracy does not change over EpochsImages Score Regression only regresses to the average of the target values










4












$begingroup$


I have an image dataset 376 classes each class has 15 pictures corresponds to a person. I would like to get the feature vector that corresponds to each person.



What I have done is, after I compiled the model I then used this link
as a reference to get the weights of the last convolutional layer. However, when I do this, I get the error:



InvalidArgumentError: You must feed a value for placeholder tensor 'conv_layer' with dtype float and shape [?,19,19,360]


How can I resolve this issue?



Here is the code that I have done so far:



train_data = np.array(train_data, dtype=np.float32)
test_data = np.array(test_data, dtype=np.float32)
train_data = train_data / 180 # to make the array values between 0-1
test_data = test_data / 180
train_label = keras.utils.to_categorical(train_label, 376)
test_label = keras.utils.to_categorical(test_label, 376)
# CNN MODEL
model = Sequential()
model.add(Conv2D(180, (3, 3), padding='same', input_shape=(180, 180, 3),
activation="relu")) #180 is the number of filters
model.add(Conv2D(180, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Conv2D(360, (3, 3), padding='same', activation="relu"))
model.add(Conv2D(360, (3, 3), activation="relu"))
conv_layer = model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
flatten_layer = model.add(Flatten())
model.add(Dense(496, activation="relu"))
model.add(Dropout(0.5))
dense_layer = model.add(Dense(376, activation="softmax"))
#compiling the model
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
model.fit(
train_data,
train_label,
batch_size=32,
epochs=40,
verbose = 2 ,
validation_split=0.1,
shuffle=True)
# getting intermediate layer weights
get_layer_output = K.function([model.layers[0].input],
[model.layers[11].output])
layer_output = get_layer_output([conv_layer])[0]









share|improve this question











$endgroup$











  • $begingroup$
    Which layer's output are expecting to keep as face feature vectors?
    $endgroup$
    – Kiritee Gak
    Mar 24 at 14:09










  • $begingroup$
    @KiriteeGak last convolutional layer in this example 7th
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 14:39















4












$begingroup$


I have an image dataset 376 classes each class has 15 pictures corresponds to a person. I would like to get the feature vector that corresponds to each person.



What I have done is, after I compiled the model I then used this link
as a reference to get the weights of the last convolutional layer. However, when I do this, I get the error:



InvalidArgumentError: You must feed a value for placeholder tensor 'conv_layer' with dtype float and shape [?,19,19,360]


How can I resolve this issue?



Here is the code that I have done so far:



train_data = np.array(train_data, dtype=np.float32)
test_data = np.array(test_data, dtype=np.float32)
train_data = train_data / 180 # to make the array values between 0-1
test_data = test_data / 180
train_label = keras.utils.to_categorical(train_label, 376)
test_label = keras.utils.to_categorical(test_label, 376)
# CNN MODEL
model = Sequential()
model.add(Conv2D(180, (3, 3), padding='same', input_shape=(180, 180, 3),
activation="relu")) #180 is the number of filters
model.add(Conv2D(180, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Conv2D(360, (3, 3), padding='same', activation="relu"))
model.add(Conv2D(360, (3, 3), activation="relu"))
conv_layer = model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
flatten_layer = model.add(Flatten())
model.add(Dense(496, activation="relu"))
model.add(Dropout(0.5))
dense_layer = model.add(Dense(376, activation="softmax"))
#compiling the model
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
model.fit(
train_data,
train_label,
batch_size=32,
epochs=40,
verbose = 2 ,
validation_split=0.1,
shuffle=True)
# getting intermediate layer weights
get_layer_output = K.function([model.layers[0].input],
[model.layers[11].output])
layer_output = get_layer_output([conv_layer])[0]









share|improve this question











$endgroup$











  • $begingroup$
    Which layer's output are expecting to keep as face feature vectors?
    $endgroup$
    – Kiritee Gak
    Mar 24 at 14:09










  • $begingroup$
    @KiriteeGak last convolutional layer in this example 7th
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 14:39













4












4








4





$begingroup$


I have an image dataset 376 classes each class has 15 pictures corresponds to a person. I would like to get the feature vector that corresponds to each person.



What I have done is, after I compiled the model I then used this link
as a reference to get the weights of the last convolutional layer. However, when I do this, I get the error:



InvalidArgumentError: You must feed a value for placeholder tensor 'conv_layer' with dtype float and shape [?,19,19,360]


How can I resolve this issue?



Here is the code that I have done so far:



train_data = np.array(train_data, dtype=np.float32)
test_data = np.array(test_data, dtype=np.float32)
train_data = train_data / 180 # to make the array values between 0-1
test_data = test_data / 180
train_label = keras.utils.to_categorical(train_label, 376)
test_label = keras.utils.to_categorical(test_label, 376)
# CNN MODEL
model = Sequential()
model.add(Conv2D(180, (3, 3), padding='same', input_shape=(180, 180, 3),
activation="relu")) #180 is the number of filters
model.add(Conv2D(180, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Conv2D(360, (3, 3), padding='same', activation="relu"))
model.add(Conv2D(360, (3, 3), activation="relu"))
conv_layer = model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
flatten_layer = model.add(Flatten())
model.add(Dense(496, activation="relu"))
model.add(Dropout(0.5))
dense_layer = model.add(Dense(376, activation="softmax"))
#compiling the model
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
model.fit(
train_data,
train_label,
batch_size=32,
epochs=40,
verbose = 2 ,
validation_split=0.1,
shuffle=True)
# getting intermediate layer weights
get_layer_output = K.function([model.layers[0].input],
[model.layers[11].output])
layer_output = get_layer_output([conv_layer])[0]









share|improve this question











$endgroup$




I have an image dataset 376 classes each class has 15 pictures corresponds to a person. I would like to get the feature vector that corresponds to each person.



What I have done is, after I compiled the model I then used this link
as a reference to get the weights of the last convolutional layer. However, when I do this, I get the error:



InvalidArgumentError: You must feed a value for placeholder tensor 'conv_layer' with dtype float and shape [?,19,19,360]


How can I resolve this issue?



Here is the code that I have done so far:



train_data = np.array(train_data, dtype=np.float32)
test_data = np.array(test_data, dtype=np.float32)
train_data = train_data / 180 # to make the array values between 0-1
test_data = test_data / 180
train_label = keras.utils.to_categorical(train_label, 376)
test_label = keras.utils.to_categorical(test_label, 376)
# CNN MODEL
model = Sequential()
model.add(Conv2D(180, (3, 3), padding='same', input_shape=(180, 180, 3),
activation="relu")) #180 is the number of filters
model.add(Conv2D(180, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Conv2D(360, (3, 3), padding='same', activation="relu"))
model.add(Conv2D(360, (3, 3), activation="relu"))
conv_layer = model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
flatten_layer = model.add(Flatten())
model.add(Dense(496, activation="relu"))
model.add(Dropout(0.5))
dense_layer = model.add(Dense(376, activation="softmax"))
#compiling the model
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
model.fit(
train_data,
train_label,
batch_size=32,
epochs=40,
verbose = 2 ,
validation_split=0.1,
shuffle=True)
# getting intermediate layer weights
get_layer_output = K.function([model.layers[0].input],
[model.layers[11].output])
layer_output = get_layer_output([conv_layer])[0]






machine-learning deep-learning keras cnn image-recognition






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Mar 24 at 16:14









Ethan

602324




602324










asked Mar 24 at 12:47









Alfaisal AlbakriAlfaisal Albakri

235




235











  • $begingroup$
    Which layer's output are expecting to keep as face feature vectors?
    $endgroup$
    – Kiritee Gak
    Mar 24 at 14:09










  • $begingroup$
    @KiriteeGak last convolutional layer in this example 7th
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 14:39
















  • $begingroup$
    Which layer's output are expecting to keep as face feature vectors?
    $endgroup$
    – Kiritee Gak
    Mar 24 at 14:09










  • $begingroup$
    @KiriteeGak last convolutional layer in this example 7th
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 14:39















$begingroup$
Which layer's output are expecting to keep as face feature vectors?
$endgroup$
– Kiritee Gak
Mar 24 at 14:09




$begingroup$
Which layer's output are expecting to keep as face feature vectors?
$endgroup$
– Kiritee Gak
Mar 24 at 14:09












$begingroup$
@KiriteeGak last convolutional layer in this example 7th
$endgroup$
– Alfaisal Albakri
Mar 24 at 14:39




$begingroup$
@KiriteeGak last convolutional layer in this example 7th
$endgroup$
– Alfaisal Albakri
Mar 24 at 14:39










1 Answer
1






active

oldest

votes


















3












$begingroup$

The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out



Network you want to train originally




model = Sequential()
model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model.add(Conv2D(10, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

model.fit(
train_data,
train_label)


Now create a subnetwork from which you want the outputs, like from above example




model_new = Sequential()
model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model_new.add(Conv2D(10, (3, 3), activation="relu"))
model_new.add(MaxPooling2D(pool_size=(3, 3)))
model_new.add(Dropout(0.25))
model_new.add(Flatten())

model_new.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['mse'])

# You need to apply fit on random array's created, just so as to initialise
# weights. Anyways you will replacing them with original ones from above.
model_new.fit(train_data, y=np.random.rand(40, 3610))


Now take weights from the first trained network and replace the weights of the second network like




model_new.set_weights(weights=model.get_weights())


You can check whether the weights are changed in the above step by actually adding these check statements like




print("Are arrays equal before fit - ",
any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

model_new.set_weights(weights=model.get_weights())
print("Are arrays equal after applying weights - ",
all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))


This should yeild




Are arrays equal before fit - False
Are arrays equal after applying weights - True


Hope this helps.






share|improve this answer











$endgroup$












  • $begingroup$
    works perfectly thanks . one more question , how do i know which array corresponds to image class?
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 18:13










  • $begingroup$
    What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
    $endgroup$
    – Kiritee Gak
    Mar 24 at 18:25











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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









3












$begingroup$

The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out



Network you want to train originally




model = Sequential()
model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model.add(Conv2D(10, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

model.fit(
train_data,
train_label)


Now create a subnetwork from which you want the outputs, like from above example




model_new = Sequential()
model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model_new.add(Conv2D(10, (3, 3), activation="relu"))
model_new.add(MaxPooling2D(pool_size=(3, 3)))
model_new.add(Dropout(0.25))
model_new.add(Flatten())

model_new.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['mse'])

# You need to apply fit on random array's created, just so as to initialise
# weights. Anyways you will replacing them with original ones from above.
model_new.fit(train_data, y=np.random.rand(40, 3610))


Now take weights from the first trained network and replace the weights of the second network like




model_new.set_weights(weights=model.get_weights())


You can check whether the weights are changed in the above step by actually adding these check statements like




print("Are arrays equal before fit - ",
any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

model_new.set_weights(weights=model.get_weights())
print("Are arrays equal after applying weights - ",
all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))


This should yeild




Are arrays equal before fit - False
Are arrays equal after applying weights - True


Hope this helps.






share|improve this answer











$endgroup$












  • $begingroup$
    works perfectly thanks . one more question , how do i know which array corresponds to image class?
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 18:13










  • $begingroup$
    What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
    $endgroup$
    – Kiritee Gak
    Mar 24 at 18:25















3












$begingroup$

The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out



Network you want to train originally




model = Sequential()
model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model.add(Conv2D(10, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

model.fit(
train_data,
train_label)


Now create a subnetwork from which you want the outputs, like from above example




model_new = Sequential()
model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model_new.add(Conv2D(10, (3, 3), activation="relu"))
model_new.add(MaxPooling2D(pool_size=(3, 3)))
model_new.add(Dropout(0.25))
model_new.add(Flatten())

model_new.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['mse'])

# You need to apply fit on random array's created, just so as to initialise
# weights. Anyways you will replacing them with original ones from above.
model_new.fit(train_data, y=np.random.rand(40, 3610))


Now take weights from the first trained network and replace the weights of the second network like




model_new.set_weights(weights=model.get_weights())


You can check whether the weights are changed in the above step by actually adding these check statements like




print("Are arrays equal before fit - ",
any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

model_new.set_weights(weights=model.get_weights())
print("Are arrays equal after applying weights - ",
all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))


This should yeild




Are arrays equal before fit - False
Are arrays equal after applying weights - True


Hope this helps.






share|improve this answer











$endgroup$












  • $begingroup$
    works perfectly thanks . one more question , how do i know which array corresponds to image class?
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 18:13










  • $begingroup$
    What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
    $endgroup$
    – Kiritee Gak
    Mar 24 at 18:25













3












3








3





$begingroup$

The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out



Network you want to train originally




model = Sequential()
model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model.add(Conv2D(10, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

model.fit(
train_data,
train_label)


Now create a subnetwork from which you want the outputs, like from above example




model_new = Sequential()
model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model_new.add(Conv2D(10, (3, 3), activation="relu"))
model_new.add(MaxPooling2D(pool_size=(3, 3)))
model_new.add(Dropout(0.25))
model_new.add(Flatten())

model_new.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['mse'])

# You need to apply fit on random array's created, just so as to initialise
# weights. Anyways you will replacing them with original ones from above.
model_new.fit(train_data, y=np.random.rand(40, 3610))


Now take weights from the first trained network and replace the weights of the second network like




model_new.set_weights(weights=model.get_weights())


You can check whether the weights are changed in the above step by actually adding these check statements like




print("Are arrays equal before fit - ",
any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

model_new.set_weights(weights=model.get_weights())
print("Are arrays equal after applying weights - ",
all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))


This should yeild




Are arrays equal before fit - False
Are arrays equal after applying weights - True


Hope this helps.






share|improve this answer











$endgroup$



The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out



Network you want to train originally




model = Sequential()
model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model.add(Conv2D(10, (3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])

model.fit(
train_data,
train_label)


Now create a subnetwork from which you want the outputs, like from above example




model_new = Sequential()
model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),
activation="relu"))
model_new.add(Conv2D(10, (3, 3), activation="relu"))
model_new.add(MaxPooling2D(pool_size=(3, 3)))
model_new.add(Dropout(0.25))
model_new.add(Flatten())

model_new.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['mse'])

# You need to apply fit on random array's created, just so as to initialise
# weights. Anyways you will replacing them with original ones from above.
model_new.fit(train_data, y=np.random.rand(40, 3610))


Now take weights from the first trained network and replace the weights of the second network like




model_new.set_weights(weights=model.get_weights())


You can check whether the weights are changed in the above step by actually adding these check statements like




print("Are arrays equal before fit - ",
any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

model_new.set_weights(weights=model.get_weights())
print("Are arrays equal after applying weights - ",
all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))


This should yeild




Are arrays equal before fit - False
Are arrays equal after applying weights - True


Hope this helps.







share|improve this answer














share|improve this answer



share|improve this answer








edited Mar 24 at 18:26

























answered Mar 24 at 16:34









Kiritee GakKiritee Gak

1,3591421




1,3591421











  • $begingroup$
    works perfectly thanks . one more question , how do i know which array corresponds to image class?
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 18:13










  • $begingroup$
    What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
    $endgroup$
    – Kiritee Gak
    Mar 24 at 18:25
















  • $begingroup$
    works perfectly thanks . one more question , how do i know which array corresponds to image class?
    $endgroup$
    – Alfaisal Albakri
    Mar 24 at 18:13










  • $begingroup$
    What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
    $endgroup$
    – Kiritee Gak
    Mar 24 at 18:25















$begingroup$
works perfectly thanks . one more question , how do i know which array corresponds to image class?
$endgroup$
– Alfaisal Albakri
Mar 24 at 18:13




$begingroup$
works perfectly thanks . one more question , how do i know which array corresponds to image class?
$endgroup$
– Alfaisal Albakri
Mar 24 at 18:13












$begingroup$
What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
$endgroup$
– Kiritee Gak
Mar 24 at 18:25




$begingroup$
What do you mean by array? Are you saying output of a filter? You accurately cannot find it. Remember after flattening you have a huge vector and you mapped all of them with some weight onto low dim. using dense layers. So any of the values from the filters would have contributed to the class weight.
$endgroup$
– Kiritee Gak
Mar 24 at 18:25

















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