forked from xuos/xiuos
38d2cb3c85
1.fix some Kconfig file 2.add tensorflow-lite-for-mcu in knowing file 3.add mnist application,note the application cannot be used with RAM less than 500K. 4.the version need to separate application and OS(rtt),later by using add transform layer to solve it.
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
#!/usr/bin/env python3
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import tensorflow as tf
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print("TensorFlow version %s" % (tf.__version__))
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MODEL_NAME_H5 = 'mnist.h5'
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MODEL_NAME_TFLITE = 'mnist.tflite'
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DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
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FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
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def show(image):
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for i in range(28):
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for j in range(28):
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if image[i][j][0] > 0.3:
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print('#', end = '')
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else:
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print(' ', end = '')
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print()
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if __name__ == '__main__':
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mnist = tf.keras.datasets.mnist
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(_, _), (test_images, test_labels) = mnist.load_data()
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test_images = test_images.reshape(10000, 28, 28, 1)
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index = 0
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input_image = test_images[index].astype('float32')/255
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target_label = test_labels[index]
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interpreter = tf.lite.Interpreter(model_path = DEFAULT_QUAN_MODEL_NAME_TFLITE)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()[0]
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output_details = interpreter.get_output_details()[0]
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interpreter.set_tensor(input_details['index'], [input_image])
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interpreter.invoke()
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output = interpreter.get_tensor(output_details['index'])[0]
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show(input_image)
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print('target label: %d, predict label: %d' % (target_label, output.argmax()))
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