stengoes

function image_pad_or_crop(image, output_height, output_width) {
  // Pads or crops an image so that the height and width dimensions match output_height and output_width. 
  // This function is compatible with tf.image.resize_with_crop_or_pad(image, target_height, target_width)

  // Validate inputs
  const image_size = image.height * image.width * image.depth;
  if (image.data.length != image_size) {
    console.log("Error: image dimensions do not match rgba buffer size!");
    return null;
  }

stengoes

import tensorflow as tf

# Reset states (to be sure)
tf.keras.backend.clear_session()

# Method 1
x = tf.keras.Input(shape=(224, 224, 3), dtype=tf.uint8)
c = tf.keras.layers.Flatten()(x)
y = tf.keras.layers.Dense(units=2, activation="softmax")(c)
model1 = tf.keras.Model(inputs=x, outputs=y)

stengoes

class CosineDecayWithWarmup(tf.keras.optimizers.schedules.LearningRateSchedule):
    
    def __init__(
        self, 
        warmup_steps,
        total_steps,
        base_lr=0.001,
    ):
        super(CosineDecayWithWarmup, self).__init__()
        self.warmup_steps = warmup_steps