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Keras Implementation Residual Attention Network
Hey! There seems to be some issue with the code here:
if identity_x.shape[-1].value != conv_op_3.shape[-1].value:
filter_n = conv_op_3.shape[-1].value
I tried replacing this with the updated set of code but it shows same error:
if identity_x.shape.as_list()[-1] != conv_op_3.shape.as_list()[-1]:
filter_n = conv_op_3.shape.as_list()[-1]
Downgarding tensorflow again effects the other modules and creates issue while implementation. Please help !
TIA
Thank you for sharing this repository! While recompiling the notebook Residual Attention Network Implementation - Cats vs Dogs Example.ipynb , I got an error in line model = ResidualAttentionNetwork((IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS), 1, activation='sigmoid').build_model() : AttributeError: 'int' object has no attribute 'value'. You have build_model() function in class ResidualAttentionNetwork (Code/ResidualAttentionNetwork.py) and I do not understand why I got this error. Have you ever encountered such an error?
The original Resnet paper states that
We adopt batch-normalization (BN) [16] right after each convolution and
before activation
But in your implementation (in the residual unit function) the ordering seems to be different.
# Layer 1
batch_norm_op_1 = BatchNormalization()(residual_input_data)
activation_op_1 = Activation('relu')(batch_norm_op_1)
conv_op_1 = Conv2D(filters=filter1,
kernel_size=(1,1),
strides=(1,1),
padding='same')(activation_op_1)
Great implementation though. Very readable ๐
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