How to pass sequence of image through Conv2D in Keras?
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I have a sequence of 5 images that I want to pass through a CNN sequentially. A single input will have size: (5, width, height, channels) and I want to pass each image in the sequence in order to a 2D CNN, concatenate all 5 outputs at some layer and then feed to an LSTM. My model looks something like this:
from keras.models import Model
from keras.layers import Dense, Input, LSTM, Flatten, Conv2D, MaxPooling2D
# Feed images in sequential order here
inputs = Input(shape=(128, 128, 3))
x = Conv2D(16, 3, activation='relu')(inputs)
x = MaxPooling2D((2, 2))(x)
...
# Concatenate sequence outputs here
x = LSTM(8)(x)
x = Flatten()(x)
outputs = Dense(5, activation='sigmoid')
model = Model(inputs=inputs, outputs=outputs)
Eventually I want to concatenate all 5 outputs together at some point in the network and feed them to an LSTM but I am having trouble figuring out how to feed sequence of images in order to a 2D convolutional layer. I have looked into 3D convolutional layers and the ConvLSTM2D layer but I want to figure out how I can do it this way instead.
python keras
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
add a comment |
I have a sequence of 5 images that I want to pass through a CNN sequentially. A single input will have size: (5, width, height, channels) and I want to pass each image in the sequence in order to a 2D CNN, concatenate all 5 outputs at some layer and then feed to an LSTM. My model looks something like this:
from keras.models import Model
from keras.layers import Dense, Input, LSTM, Flatten, Conv2D, MaxPooling2D
# Feed images in sequential order here
inputs = Input(shape=(128, 128, 3))
x = Conv2D(16, 3, activation='relu')(inputs)
x = MaxPooling2D((2, 2))(x)
...
# Concatenate sequence outputs here
x = LSTM(8)(x)
x = Flatten()(x)
outputs = Dense(5, activation='sigmoid')
model = Model(inputs=inputs, outputs=outputs)
Eventually I want to concatenate all 5 outputs together at some point in the network and feed them to an LSTM but I am having trouble figuring out how to feed sequence of images in order to a 2D convolutional layer. I have looked into 3D convolutional layers and the ConvLSTM2D layer but I want to figure out how I can do it this way instead.
python keras
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
Coincidentally i had the same question today. See my answer here: stackoverflow.com/questions/53488768/…
– deKeijzer
Nov 26 '18 at 22:33
add a comment |
I have a sequence of 5 images that I want to pass through a CNN sequentially. A single input will have size: (5, width, height, channels) and I want to pass each image in the sequence in order to a 2D CNN, concatenate all 5 outputs at some layer and then feed to an LSTM. My model looks something like this:
from keras.models import Model
from keras.layers import Dense, Input, LSTM, Flatten, Conv2D, MaxPooling2D
# Feed images in sequential order here
inputs = Input(shape=(128, 128, 3))
x = Conv2D(16, 3, activation='relu')(inputs)
x = MaxPooling2D((2, 2))(x)
...
# Concatenate sequence outputs here
x = LSTM(8)(x)
x = Flatten()(x)
outputs = Dense(5, activation='sigmoid')
model = Model(inputs=inputs, outputs=outputs)
Eventually I want to concatenate all 5 outputs together at some point in the network and feed them to an LSTM but I am having trouble figuring out how to feed sequence of images in order to a 2D convolutional layer. I have looked into 3D convolutional layers and the ConvLSTM2D layer but I want to figure out how I can do it this way instead.
python keras
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
I have a sequence of 5 images that I want to pass through a CNN sequentially. A single input will have size: (5, width, height, channels) and I want to pass each image in the sequence in order to a 2D CNN, concatenate all 5 outputs at some layer and then feed to an LSTM. My model looks something like this:
from keras.models import Model
from keras.layers import Dense, Input, LSTM, Flatten, Conv2D, MaxPooling2D
# Feed images in sequential order here
inputs = Input(shape=(128, 128, 3))
x = Conv2D(16, 3, activation='relu')(inputs)
x = MaxPooling2D((2, 2))(x)
...
# Concatenate sequence outputs here
x = LSTM(8)(x)
x = Flatten()(x)
outputs = Dense(5, activation='sigmoid')
model = Model(inputs=inputs, outputs=outputs)
Eventually I want to concatenate all 5 outputs together at some point in the network and feed them to an LSTM but I am having trouble figuring out how to feed sequence of images in order to a 2D convolutional layer. I have looked into 3D convolutional layers and the ConvLSTM2D layer but I want to figure out how I can do it this way instead.
python keras
python keras
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
asked Nov 26 '18 at 20:24
CrashingWaterCrashingWater
616
616
Coincidentally i had the same question today. See my answer here: stackoverflow.com/questions/53488768/…
– deKeijzer
Nov 26 '18 at 22:33
add a comment |
Coincidentally i had the same question today. See my answer here: stackoverflow.com/questions/53488768/…
– deKeijzer
Nov 26 '18 at 22:33
Coincidentally i had the same question today. See my answer here: stackoverflow.com/questions/53488768/…
– deKeijzer
Nov 26 '18 at 22:33
Coincidentally i had the same question today. See my answer here: stackoverflow.com/questions/53488768/…
– deKeijzer
Nov 26 '18 at 22:33
add a comment |
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Coincidentally i had the same question today. See my answer here: stackoverflow.com/questions/53488768/…
– deKeijzer
Nov 26 '18 at 22:33