I have a problem with using Inception for fine-tuning. Actually, there is either an is

Inception preprocessing issue about deep-learning-models HOT 1 OPEN

fchollet commented on August 25, 2024 1

Inception preprocessing issue

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apiszcz commented on August 25, 2024

I am using this pattern with fine tuning and two output classes, observations: random classification output results with a single test image, about 50/50. Training was on 1 image for each class and indicated accuracy of 1.0 with an error less 1e-07. Displaying the image after the img_to_array call is an inverted color image. Is it possible I have a layer mismatch? The top 2 blocks are shown below or layers 172:.

        image = image_utils.load_img(file, target_size=(299, 299))
        image = image_utils.img_to_array(image)
        image = np.expand_dims(image, axis=0)
        image = preprocess_input(image)
        preds = model.predict(image)

[end of 'start history model'
Epoch 20/20
64/64 [==============================] - 5s - loss: 1.1921e-07 - acc: 1.0000 - val_loss: 1.1921e-07 - val_acc: 1.0000

end of 'train new model'
Epoch 20/20
2/1 [============================================================] - 0s - loss: 1.1921e-07 - val_loss: 1.1921e-07
]

Layers 172:

(172, 'mixed8')
(173, 'convolution2d_81')
(174, 'batchnormalization_81')
(175, 'convolution2d_78')
(176, 'convolution2d_82')
(177, 'batchnormalization_78')
(178, 'batchnormalization_82')
(179, 'convolution2d_79')
(180, 'convolution2d_80')
(181, 'convolution2d_83')
(182, 'convolution2d_84')
(183, 'averagepooling2d_9')
(184, 'convolution2d_77')
(185, 'batchnormalization_79')
(186, 'batchnormalization_80')
(187, 'batchnormalization_83')
(188, 'batchnormalization_84')
(189, 'convolution2d_85')
(190, 'batchnormalization_77')
(191, 'mixed9_0')
(192, 'merge_1')
(193, 'batchnormalization_85')
(194, 'mixed9')
(195, 'convolution2d_90')
(196, 'batchnormalization_90')
(197, 'convolution2d_87')
(198, 'convolution2d_91')
(199, 'batchnormalization_87')
(200, 'batchnormalization_91')
(201, 'convolution2d_88')
(202, 'convolution2d_89')
(203, 'convolution2d_92')
(204, 'convolution2d_93')
(205, 'averagepooling2d_10')
(206, 'convolution2d_86')
(207, 'batchnormalization_88')
(208, 'batchnormalization_89')
(209, 'batchnormalization_92')
(210, 'batchnormalization_93')
(211, 'convolution2d_94')
(212, 'batchnormalization_86')
(213, 'mixed9_1')
(214, 'merge_2')
(215, 'batchnormalization_94')
(216, 'mixed10')

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