我有一個用于預處理資料的特定順序模型，如下所示：
data_transformation = tf.keras.Sequential([layers.experimental.preprocessing.RandomContrast(factor=(0.7,0.9)),layers.GaussianNoise(stddev=tf.random.uniform(shape=(),minval=0, maxval=1)), layers.experimental.preprocessing.RandomRotation(factor=0.1, fill_mode='reflect', interpolation='bilinear', seed=None, name=None, fill_value=0.0), layers.experimental.preprocessing.RandomZoom(height_factor=(0.1,0.2), width_factor=(0.1,0.2), fill_mode='reflect', interpolation='bilinear', seed=None, name=None, fill_value=0.0),])
但是，我想添加我自己的預處理層，它由下面的 Python 函式定義：
import tensorflow as tf
import random
def my_random_contrast(image_to_be_transformed, contrast_factor):
#build the contrast factor
selected_contrast_factor=random.uniform(1-contrast_factor, 1 contrast_factor)
selected_contrast_factor_c1=selected_contrast_factor
selected_contrast_factor_c2=selected_contrast_factor-0.01
selected_contrast_factor_c3=selected_contrast_factor-0.02
image_to_be_transformed=image_to_be_transformed.numpy()
image_to_be_transformed[0,:,:]=((image_to_be_transformed[0,:,:]-tf.reduce_mean(image_to_be_transformed[0,:,:]))*selected_contrast_factor_c1) tf.reduce_mean(image_to_be_transformed[0,:,:])
image_to_be_transformed[1,:,:]=((image_to_be_transformed[1,:,:]-tf.reduce_mean(image_to_be_transformed[1,:,:]))*selected_contrast_factor_c2) tf.reduce_mean(image_to_be_transformed[1,:,:])
image_to_be_transformed[2,:,:]=((image_to_be_transformed[2,:,:]-tf.reduce_mean(image_to_be_transformed[2,:,:]))*selected_contrast_factor_c3) tf.reduce_mean(image_to_be_transformed[2,:,:])
image_to_be_transformed=tf.convert_to_tensor(image_to_be_transformed)
return image_to_be_transformed
x=tf.random.uniform(shape=[3,224,224], minval=0, maxval=1, dtype=tf.float32)
y=my_random_contrast(x, 0.5)
我如何使用 TensorFlow 做到這一點？作為一個新的預處理層，它將接收來自其他層的輸入和輸出，我是否必須保證輸入和輸出是給定的型別？
uj5u.com熱心網友回復：
如果您計劃一次預處理多個影像，則需要確保您的方法適用于批量維度。此外，它應該能夠在沒有任何 numpy 操作的情況下作業，以便在圖形模式下運行。然后，您可以撰寫自己的自定義層或簡單lambda層：
import tensorflow as tf
import matplotlib.pyplot as plt
import pathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
image_size=(180, 180),
batch_size=32, shuffle=False)
def my_random_contrast(image_to_be_transformed, contrast_factor):
#build the contrast factor
selected_contrast_factor=tf.random.uniform((), minval=1 - contrast_factor, maxval=1 contrast_factor)
selected_contrast_factor_c1=selected_contrast_factor
selected_contrast_factor_c2=selected_contrast_factor-0.01
selected_contrast_factor_c3=selected_contrast_factor-0.02
c0 = ((image_to_be_transformed[:,:,:, 0]-tf.reduce_mean(image_to_be_transformed[:,:,:, 0]))*selected_contrast_factor_c1) tf.reduce_mean(image_to_be_transformed[:,:,:, 0])
c1 = ((image_to_be_transformed[:,:,:, 1]-tf.reduce_mean(image_to_be_transformed[:,:,:, 1]))*selected_contrast_factor_c2) tf.reduce_mean(image_to_be_transformed[:,:,:, 1])
c2 = ((image_to_be_transformed[:,:,:, 2]-tf.reduce_mean(image_to_be_transformed[:,:,:, 2]))*selected_contrast_factor_c3) tf.reduce_mean(image_to_be_transformed[:,:,:, 2])
image_to_be_transformed = tf.concat([c0[..., tf.newaxis], image_to_be_transformed[:,:,:, 1:]], axis=-1)
image_to_be_transformed = tf.concat([image_to_be_transformed[:,:,:, 0][..., tf.newaxis], c1[..., tf.newaxis], image_to_be_transformed[:,:,:, 2][..., tf.newaxis]], axis=-1)
image_to_be_transformed = tf.concat([image_to_be_transformed[:,:,:, :2], c2[..., tf.newaxis]], axis=-1)
return image_to_be_transformed
images, _ = next(iter(ds.take(1)))
image = images[2]
plt.figure()
f, axarr = plt.subplots(1,2)
axarr[0].imshow(image / 255)
# After preprocessing dataset:
images, _ = next(iter(ds.map(lambda x, y: (my_random_contrast(x, 0.8), y)).take(1)))
image = images[2]
axarr[1].imshow(image / 255)
示例用法：
model = tf.keras.Sequential()
model.add(tf.keras.layers.Lambda(lambda x: my_random_contrast(x, 0.8)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit(tf.random.normal((50, 64, 64, 3)), tf.random.normal((50, 1)))
uj5u.com熱心網友回復：
您需要通過子類化 Layer 類來創建自定義層，可以在此處通過子類化制作新的層和模型找到一個示例。許多例子可以在網上找到。
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