Is it possible to load tensorflow .pb file into Keras as weight for model?

I have followed the retraining on Tensorflow example for my specific classification task and have a grad-CAM visualization written code in Keras. For instance

Usually, I do load pre-train weights such as vgg16 or inception-v3 in .h5 format and works very well on my grad-CAM work. The problem is the retrained_graph.pb from retraining process by Tensorflow and I have no idea if there are any workaround like

• mapping .pb file to .h5?

• or do Keras have any interface to load .pb file in the same manner with loading .h5 file?

Note: I use Tensorflow as the backend

Please advise

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[guoxiaolu]

You should know the graph defination of your pb and copy all weights to each keras layer. Seems the code format has some problems…

import tensorflow as tf from tensorflow.python.platform import gfile from keras.applications.resnet50 import ResNet50 from keras.layers import Dense, GlobalAveragePooling2D, Convolution2D, BatchNormalization from keras.models import Model from tensorflow.python.framework import tensor_util

GRAPH_PB_PATH = xxx.pb’ #path to your .pb file with tf.Session() as sess: print(“load graph”) with gfile.FastGFile(GRAPH_PB_PATH,‘rb’) as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sess.graph.as_default() tf.import_graph_def(graph_def, name=‘’) graph_nodes=[n for n in graph_def.node]

wts = [n for n in graph_nodes if n.op==‘Const’]

weight_dict = {} for i, n in enumerate(wts): weight_dict[n.name] = i

model = ResNet50(input_shape=(224, 224, 3), include_top=True) model.summary()

for layer in model.layers: layer_weight = layer.get_weights() name = layer.name if len(layer_weight) == 0: continue if isinstance(layer, Convolution2D): kname = name + ‘/kernel’ bname = name + ‘/bias’ if kname not in weight_dict or bname not in weight_dict: print kname, bname else: weights = [] idx = weight_dict[kname] wtensor = wts[idx].attr[‘value’].tensor weight = tensor_util.MakeNdarray(wtensor) weights.append(weight)

        idx = weight_dict[bname]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)
        layer.set_weights(weights)
        continue
if isinstance(layer, BatchNormalization):
    beta_name = name + '/beta'
    gamma_name = name + '/gamma'
    mmean_name = name + '/moving_mean'
    mvar_name = name + '/moving_variance'

    if beta_name not in weight_dict or gamma_name not in weight_dict or\
            mmean_name not in weight_dict or mvar_name not in weight_dict:
        print beta_name, gamma_name, mmean_name, mvar_name
    else:
        weights = []
        idx = weight_dict[gamma_name]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)

        idx = weight_dict[beta_name]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)

        idx = weight_dict[mmean_name]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)

        idx = weight_dict[mvar_name]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)
        layer.set_weights(weights)
        continue
if isinstance(layer, Dense):
    kname = name + '/kernel'
    bname = name + '/bias'
    if kname not in weight_dict or bname not in weight_dict:
        print kname, bname
    else:
        weights = []
        idx = weight_dict[kname]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)

        idx = weight_dict[bname]
        wtensor = wts[idx].attr['value'].tensor
        weight = tensor_util.MakeNdarray(wtensor)
        weights.append(weight)
        layer.set_weights(weights)
        continue
print name

model.save(‘tmp.h5’)