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@ -108,17 +108,3 @@ model.evaluate(input_fn)
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# 预测单个图像
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n_images = 6
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# 从数据集得到测试图像
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test_images = mnist.test.images[:n_images]
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# 准备输入数据
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input_fn = tf.estimator.inputs.numpy_input_fn(
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x={'images': test_images}, shuffle=False)
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# 用训练好的模型预测图片类别
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preds = list(model.predict(input_fn))
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# 可视化显示
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for i in range(n_images):
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plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
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plt.show()
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print("Model prediction:", preds[i])
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