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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
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# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license.
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import os
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import numpy as np
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import tensorflow as tf
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from PIL import Image
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from azureml.core import Model
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def init():
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global g_tf_sess
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# Disable eager execution
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tf.compat.v1.disable_eager_execution()
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# pull down model from workspace
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model_path = Model.get_model_path("mnist-prs")
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# contruct graph to execute
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tf.compat.v1.reset_default_graph()
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saver = tf.compat.v1.train.import_meta_graph(os.path.join(model_path, 'mnist-tf.model.meta'))
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g_tf_sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(device_count={'GPU': 0}))
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saver.restore(g_tf_sess, os.path.join(model_path, 'mnist-tf.model'))
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def run(mini_batch):
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print(f'run method start: {__file__}, run({mini_batch})')
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resultList = []
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in_tensor = g_tf_sess.graph.get_tensor_by_name("network/X:0")
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output = g_tf_sess.graph.get_tensor_by_name("network/output/MatMul:0")
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for image in mini_batch:
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# prepare each image
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data = Image.open(image)
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np_im = np.array(data).reshape((1, 784))
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# perform inference
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inference_result = g_tf_sess.run(output, feed_dict={in_tensor: np_im})
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# find best probability, and add to result list
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best_result = np.argmax(inference_result)
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resultList.append("{}: {}".format(os.path.basename(image), best_result))
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return resultList
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