Created
June 20, 2017 00:17
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Keras Models --> TensorFlow SavedModel format
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# Mostly copied from https://keras.io/applications/#usage-examples-for-image-classification-models | |
# Changing it to use InceptionV3 instead of ResNet50 | |
from keras.applications.inception_v3 import InceptionV3, preprocess_input, decode_predictions | |
from keras.preprocessing import image | |
import numpy as np | |
model = InceptionV3() | |
img_path = 'elephant.jpg' | |
img = image.load_img(img_path, target_size=(299, 299)) | |
x = image.img_to_array(img) | |
x = np.expand_dims(x, axis=0) | |
x = preprocess_input(x) | |
preds = model.predict(x) | |
print('Predicted:', decode_predictions(preds, top=3)[0]) | |
# And now exporting to the TensorFlow SavedModel format. | |
# Documentation for the SavedModel format can be found here: | |
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md | |
from keras import backend as K | |
import tensorflow as tf | |
signature = tf.saved_model.signature_def_utils.predict_signature_def( | |
inputs={'image': model.input}, outputs={'scores': model.output}) | |
builder = tf.saved_model.builder.SavedModelBuilder('/tmp/my_saved_model') | |
builder.add_meta_graph_and_variables( | |
sess=K.get_session(), | |
tags=[tf.saved_model.tag_constants.SERVING], | |
signature_def_map={ | |
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: | |
signature | |
}) | |
builder.save() | |
# This model can be loaded in other langauges using the C API: | |
# TF_SessionOptions* opts = TF_NewSessionOptions(); | |
# const char* tags[] = {"serve"}; // tf.saved_model.tag_constants.SERVING | |
# TF_LoadSessionFromSavedModel(opts, NULL, "/tmp/my_saved_model", tags, 1, graph, NULL, status); | |
# | |
# This is what is used by the: | |
# - Java API: https://www.tensorflow.org/api_docs/java/reference/org/tensorflow/SavedModelBundle | |
# - Go API: https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go#LoadSavedModel | |
# etc. |
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Thanks for sharing. This helped me save my tensorflow.keras models.