January 2023
Intermediate to advanced
288 pages
6h 6m
Chinese
Content preview from 移动端AI与ML应用开发：基于iOS和Android
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更深入：了解
TensorFlow Lite
|
131
息项 0 只有一个索引，索引为 0，它期望的形状为
[1,1]
，如前所述。所以，你把
to_
predict
值放在那里。然后使用
invoke
方法调用解释器。
然后，你可以通过调用
get_tensor
并为其提供你要读取的张量的详细信息来读取预测：
tflite_results = interpreter.get_tensor(output_details[0]['index'])
print(tflite_results)
同样，只有一个输出张量，因此它将是
output_details[0]
，你指定索引以得到其详细
信息，该索引具有输出值。
因此，例如，假设你运行以下代码：
to_predict = np.array([[10.0]], dtype=np.float32)
print(to_predict)
interpreter.set_tensor(input_details[0]['index'], to_predict)
interpreter.invoke()
tflite_results = interpreter.get_tensor(output_details[0]['index'])
print(tflite_results)
你应该看到如下输出：
[[10.]]
[[18.975412]]
其中 10 是输入值，18.97 是预测值，非常接近 19，当
x
= 10 时是 2
x
– 1。关于它不是
19 的原因，请回顾第 1
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