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@sadimanna
Last active April 11, 2019 05:47
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Inception ResNet B
def incresB(x,scale,name=None):
pad = 'same'
branch0 = conv2d(x,192,1,1,pad,True,name=name+'b0')
branch1 = conv2d(x,128,1,1,pad,True,name=name+'b1_1')
branch1 = conv2d(branch1,160,[1,7],1,pad,True,name=name+'b1_2')
branch1 = conv2d(branch1,192,[7,1],1,pad,True,name=name+'b1_3')
branches = [branch0,branch1]
mixed = Concatenate(axis=3, name=name + '_mixed')(branches)
filt_exp_1x1 = conv2d(mixed,1152,1,1,pad,False,name=name+'filt_exp_1x1')
final_lay = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
output_shape=backend.int_shape(x)[1:],
arguments={'scale': scale},
name=name+'act_scaling')([x, filt_exp_1x1])
return final_lay
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