Hi, I finally ran out of excuses to learn ML and Tensorflow.js, I’m building my first model and came across an error that I don’t quite understand where is coming from.
I have a basic model that takes an image and calculates a certain value, the input layer is a cropping layer that removes a number of pixels from the top and bottom of the image. The model works well, but when I change the settings of the cropping layer, say, remove 25 pixels from the top instead of 75, the browser (chrome) flickers and outputs the following error:

Screen Shot 2021-08-23 at 5.10.14 pm820×309 54 KB

NOTE: Right before the above error, it prints out the following message *Couldn't parse line number in error* followed by what appears to be GLSL code.
The same happens if I remove the cropping layer altogether.
I’m using tfjs v3.8.0 but also tested with v2.0.0 with similar outcomes. This is my model:
const model = tf.sequential();

// Cropping Layer
model.add(
  tf.layers.cropping2D({
    // If I change 75 to anything below 50, it crashes before completing the first epoch,
    // If this layer is removed, it crashes almost immediately after training starts
    cropping: [
      [75, 25],
      [0, 0]
    ],
    // image height, width, depth
    inputShape: [160, 320, 3]
  })
);

model.add(
  tf.layers.conv2d({
    filters: 16,
    kernelSize: [3, 3],
    strides: [2, 2],
    activation: 'relu',
  })
);

model.add(
  tf.layers.maxPool2d({
    poolSize: [2, 2]
  })
);

model.add(
  tf.layers.conv2d({
    filters: 32,
    kernelSize: [3, 3],
    strides: [2, 2],
    activation: 'relu'
  })
);

model.add(
  tf.layers.maxPool2d({
    poolSize: [2, 2]
  })
);

model.add( tf.layers.flatten());
model.add( tf.layers.dense({ units: 1024, activation: 'relu' }));
model.add( tf.layers.dropout({ rate: 0.25 }));
model.add( tf.layers.dense({ units: 128, activation: 'relu' }));
model.add( tf.layers.dense({ units: 1, activation: 'linear' }));

model.compile({
  optimizer: 'adam',
  loss: 'meanSquaredError',
  metrics: [
    'accuracy',
  ],
});

Am I doing something obviously wrong?
Thanks.