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@@ -242,6 +242,10 @@ def inputs(eval_data, data_dir, batch_size):
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# Subtract off the mean and divide by the variance of the pixels.
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# Subtract off the mean and divide by the variance of the pixels.
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float_image = tf.image.per_image_standardization(resized_image)
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float_image = tf.image.per_image_standardization(resized_image)
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+ # Set the shapes of tensors.
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+ float_image.set_shape([height, width, 3])
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+ read_input.label.set_shape([1])
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+
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# Ensure that the random shuffling has good mixing properties.
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# Ensure that the random shuffling has good mixing properties.
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min_fraction_of_examples_in_queue = 0.4
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min_fraction_of_examples_in_queue = 0.4
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min_queue_examples = int(num_examples_per_epoch *
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min_queue_examples = int(num_examples_per_epoch *
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