The predicted results are very different between using python and imagej ? #1
Description
Hi, I have trained a network with keras, and exported it to a .h5 file. I used the code in this repo. to generate the .pb model, and try to predict images using imagej with your plug-in. It appears that the python results and imagej predicted results are very different. Can you tell me what the potential mistake is ? Many thanks !
path2network='./model.h5'
K.set_learning_phase(0)
model = keras.models.load_model(path2network)
print(model.summary())
OUTPUT_DIR = "./tf_model"
builder = tf.saved_model.builder.SavedModelBuilder(OUTPUT_DIR)
signature = tf.saved_model.signature_def_utils.predict_signature_def(
inputs = {'input': model.input},
outputs = {'output': model.output})
signature_def_map = { tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature }
builder.add_meta_graph_and_variables(K.get_session(), [tf.saved_model.tag_constants.SERVING],
signature_def_map=signature_def_map)
builder.save()
start preprocessing
end preprocessing 0.02025772
Extract Patch 0.172213ms
Build Tensor 7.995988ms
Session feed 0.026279999999999998ms
Session fetch0 0.031226999999999998ms
Session run 154.684742ms
Convert output 4.818864ms
Create ImageJ object 14.75155ms