问规范化tf.data.Dataset

EN

def remove_mean(image, target):
    image_mean = np.mean(image, axis=0)
    target_mean = np.mean(target, axis=0)
    image = image - image_mean
    target = target - target_mean
    return image, target

def linear_scaling(image, target):
    image_min = np.ndarray.min(image, axis=(1,2), keepdims=True)
    image_max = np.ndarray.max(image, axis=(1,2), keepdims=True)
    image = (image-image_min)/(image_max-image_min)

    target_min = np.ndarray.min(target, axis=(1,2), keepdims=True)
    target_max = np.ndarray.max(target, axis=(1,2), keepdims=True)
    target = (target-target_min)/(target_max-target_min)
    return image, target

a, b = next(iter(train_dataset))
print(tf.math.reduce_min(a[0,:,:,:]))

train_dataset.map(lambda item1, item2: tuple(tf.py_function(remove_mean, [item1, item2], [tf.float32, tf.float32])))
test_dataset.map(lambda item1, item2: tuple(tf.py_function(remove_mean, [item1, item2], [tf.float32, tf.float32])))

a, b = next(iter(train_dataset))
print(tf.math.reduce_min(a[0,:,:,:]))

train_dataset.map(lambda item1, item2: tuple(tf.py_function(linear_scaling, [item1, item2], [tf.float32])))
test_dataset.map(lambda item1, item2: tuple(tf.py_function(linear_scaling, [item1, item2], [tf.float32])))

a, b = next(iter(train_dataset))
print(tf.math.reduce_min(a[0,:,:,:]))


Output -

tf.Tensor(-0.00040511801, shape=(), dtype=float32)
tf.Tensor(-0.00040511801, shape=(), dtype=float32)
tf.Tensor(-0.00040511801, shape=(), dtype=float32)