I am implementing ApesNet in keras. It has an ApesBlock that has skip connections. How do I add this to a sequential model in keras? The ApesBlock has two parallel layers that merge at the end by element-wise addition.
Skip Connections were introduced to solve different problems in different architectures. In the case of ResNets, skip connections solved the degradation problem that we addressed earlier whereas, in the case of DenseNets, it ensured feature reusability.
Skip connections in deep architectures, as the name suggests, skip some layer in the neural network and feeds the output of one layer as the input to the next layers (instead of only the next one). As previously explained, using the chain rule, we must keep multiplying terms with the error gradient as we go backwards.
Skip connections or shortcuts are used to jump over some layers (HighwayNets may also learn the skip weights themselves through an additional weight matrix for their gates). Typical ResNet models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between.
Skip connections allow a deep neural network to pass lower-level semantic information unchanged to the latter layers of the neural network. This helps preserve information that would have been abstracted away if it had been passed through too many layers.
The easy answer is don't use a sequential model for this, use the functional API instead, implementing skip connections (also called residual connections) are then very easy, as shown in this example from the functional API guide:
from keras.layers import merge, Convolution2D, Input
# input tensor for a 3-channel 256x256 image
x = Input(shape=(3, 256, 256))
# 3x3 conv with 3 output channels (same as input channels)
y = Convolution2D(3, 3, 3, border_mode='same')(x)
# this returns x + y.
z = merge([x, y], mode='sum')
I, too, couldn't find merge
in the Keras documentation, as Dr.Snoopy says in their answer. And I get a type error 'module' object is not callable
.
Instead I added an Add
layer.
So the same example as Dr. Snoopy's answer would be:
from keras.layers import Add, Convolution2D, Input
# input tensor for a 3-channel 256x256 image
x = Input(shape=(3, 256, 256))
# 3x3 conv with 3 output channels (same as input channels)
y = Convolution2D(3, 3, 3, border_mode='same')(x)
# this returns x + y.
z = Add()([x, y])
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