Created
December 23, 2016 17:26
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copy chainer's layer weights to Keras' layer
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| 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) |
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This is great!
However, have you tested this on any pretrained model?