![]() In this post, you have seen how you can use the tf.data dataset with image augmentation functions from Keras and TensorFlow. ![]() Further Readingīelow is some documentation from TensorFlow that is related to the examples above: However, as you want the network to predict well on a wide variation of image quality and properties, using augmentation can help your resulting network become more powerful. You will see some improvement in accuracy if you remove the RandomFlip and RandomRotation layers because you make the problem easier. This would be significant if the dataset has some other augmentation assigned using the map() function. This is a performance technique to allow the dataset to prepare data asynchronously while the neural network is trained. In the code above, you created the dataset with cache() and prefetch(). Sobel = tf.image.sobel_edges(images)Īx.imshow(sobel.numpy().astype("uint8"))Īx.imshow(tf.image.stateless_random_brightness(images, 0.3, seed).numpy().astype("uint8"))Īx.imshow(tf.image.stateless_random_contrast(images, 0.7, 1.3, seed).numpy().astype("uint8"))Īx.imshow(tf.image.stateless_random_saturation(images, 0.7, 1.3, seed).numpy().astype("uint8"))Īx.imshow(tf.image.stateless_random_hue(images, 0.3, seed).numpy().astype("uint8"))ĭs = ds.cache().prefetch(buffer_size=AUTOTUNE) Seed = tf.random.uniform((2,), minval=0, maxval=65536).numpy().astype("int32")Īx.imshow(tf.image.stateless_random_crop(images,, seed).numpy().astype("uint8"))Īx.imshow(tf.image.stateless_random_flip_left_right(images, seed).numpy().astype("uint8"))Īx.imshow(tf.image.stateless_random_flip_up_down(images, seed).numpy().astype("uint8")) X = tf.random.uniform(, minval=0.4, maxval=1.0)Īx.imshow(tf.image.central_crop(images, x).numpy().astype("uint8")) ![]() Rotate = tf.(0.2)Ĭrop = tf.(out_height, out_width) Height = tf.(0.3)įlip = tf.("horizontal_and_vertical") ![]() Image_size=(256,256), interpolation="mitchellcubic", PATH='./Citrus/Leaves' # modify to your path # use image_dataset_from_directory() to load images, with image size scaled to 256x256 From import image_dataset_from_directory
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