Keras: How to convert a caffe model to Keras type?

I also hope to see a tutorial to illustrate this.

We are working on it. This is definitely among the features we want to add soon. https://github.com/fchollet/keras/issues/100

https://github.com/fchollet/keras/pull/368

If anybody is still interested you can use this fork of Keras which has a conversion module:
https://github.com/MarcBS/keras

Best,
Marc

Hi,
I was trying caffe to keras conversion module that you mentioned but im getting this error when i run the caffe2keras.py

global name 'network_input' is not defined

Any help?

@dhruvjain if you are referring to this fork then open a new issue here, please.

Furthermore, it would be very helpful if you could include the model files that you are trying to convert, or at least the .prototxt.

I used it to convert caffenet model, the prototxt is this one:
name: "CaffeNet"

force_backward: true
input: "data"
input_dim: 1
input_dim: 3
input_dim: 227
input_dim: 227

input: "label"
input_dim: 1
input_dim: 1
input_dim: 1
input_dim: 1

layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}

layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}

layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}

layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}

layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 1000
}
}

Really an important feature...

This Caffe-to-Keras weight converter is what you are looking for:

https://github.com/pierluigiferrari/caffe_weight_converter

It converts .caffemodel files to .h5 weight files. It converts weights only, not the model definition, but the weights are really all you need anyway.

For any given model, the model definition either requires only Keras core library layers, in which case it's super easy to write in Keras manually, or the model definition is complex and has custom layer types, in which case a model definition converter would probably fail anyway.

what shape input is expected by converted new keras model? (3,224,224) or (224,224,3)? because caffe works with (3,224,224) and keras with (224,224,3)...