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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计算机视觉简介
|
31
因此它一次训练一批图像。这为你提供了 1875 批图像来组成 60 000 张图像（即 60 000
除以 32 可得 1875）。
当你到达第 20 个轮次时，你会看到准确度现在超过 97%：
Epoch 19/20
1875/1875 [=====================] - 2s 1ms/step - loss: 0.0922 - accuracy: 0.9717
Epoch 20/20
1875/1875 [=====================] - 2s 1ms/step - loss: 0.0905 - accuracy: 0.9722
因此，只需几行代码和不到一分钟的训练，你现在就拥有一个模型，可以以超过 97% 的
准确度识别 Fashion MNIST 图像。
还记得之前你保留了 10 000 张图像作为验证数据集吗？你现在可以将它们传递给模型来
查看模型如何解析它们。请注意，它以前从未见过这些图像，因此这是测试你的模型是
否真正准确的好方法
—
它是否可以对以前从未见过的图像进行高度准确的分类。你可
以通过调用
model.evaluate
将图像和标签集传递给它来完成此操作：
model.evaluate(val_images, val_labels)
313/313 [=====================] - 0s 872us/step - loss: 0.1320 - accuracy: 0.9623
由此可以看到，你的模型在之前从未见过的数据上的准确度为 ...
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