Keras: Load checkpoint weights HDF5 generated by multiple GPUs

Question

Checkpoint snippet:

checkpointer = ModelCheckpoint(filepath=os.path.join(savedir, \"mid/weights.{epoch:02d}.hd5\"), monitor=\'val_loss\', verbose=1, save_bes         


        

Answer 1

Try this function:

def keras_model_reassign_weights(model_cpu,model_gpu):
    weights_temp ={}
    print('_'*5,'Collecting weights from GPU model','_'*5)
    for layer in model_gpu.layers:
        try:
            for layer_unw in layer.layers:
                #print('Weights extracted for: ',layer_unw.name)
                weights_temp[layer_unw.name] = layer_unw.get_weights()
            break
        except:
            print('Skipped: ',layer.name)
    print('_'*5,'Writing weights to CPU model','_'*5)
    for layer in model_cpu.layers:
        try:
            layer.set_weights(weights_temp[layer.name])
            #print(layer.name,'Done!')
        except:
            print(layer.name,'weights does not set for this layer!')
    return model_cpu

But you need to load weights to your gpu model first:

#load or initialize your keras multi-gpu model
model_gpu = None 
#load or initialize your keras model with the same structure, without using keras.multi_gpu function
model_cpu = None 
#load weights into multigpu model
model_gpu.load_weights(r'gpu_model_best_checkpoint.hdf5') 
#execute function
model_cpu = keras_model_reassign_weights(model_cpu,model_gpu)
#save obtained weights for cpu model
model_cpu.save_weights(r'CPU_model.hdf5')

After transferring you can use weights with a single GPU or CPU model.