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copy chainer's layer weights to Keras' layer
def copy_weights_deconvolution(chainer_model, keras_model, layer_name):
deconv_chainer = chainer_model[layer_name]
W, b = (deconv_chainer.W.data, deconv_chainer.b.data)
keras_model.get_layer(layer_name).set_weights([numpy.transpose(W, (2, 3, 0, 1)), b])
def copy_weights_convolution(chainer_model, keras_model, layer_name):
conv_chainer = chainer_model[layer_name]
W, b = (conv_chainer.W.data, conv_chainer.b.data)
keras_model.get_layer(layer_name).set_weights([numpy.transpose(W, (2, 3, 1, 0)), b])
def copy_weights_bn(chainer_model, keras_model, layer_name):
bn_chainer = chainer_model[layer_name]
w = [bn_chainer.gamma.data, bn_chainer.beta.data, bn_chainer.avg_mean, bn_chainer.avg_var]
keras_model.get_layer(layer_name).set_weights(w)
@Jannick-v

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@Jannick-v Jannick-v commented Aug 31, 2017

This is great!
However, have you tested this on any pretrained model?

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