TensorFlow Computer Vision & Deep Learning Examples

  • Keras MNIST data: Sequential Model using Dense Layers,
  • Keras MINST data: Custom CNN Model Class trained with GradientTape & Dataset,
  • Custom layer creating new model parameters,
  • Dataset performance,
  • Overfitting,
  • Save and load,
  • Classify flowers with data augmentation layers,
  • Classify flowers with data augmentation using dataset mapping,
  • Transfer learning,
  • Image segmentation,
  • Regression on CSV file: Using Pandas to process data,
  • CSV preprocessing,

Keras MNIST data: Sequential Model using Dense Layers

Keras MINST data: Custom CNN Model Class trained with GradientTape & Dataset

  • train with a Dataset (in particular, if samples cannot fit into the memory),
  • a custom CNN model class, and
  • a custom training with GraidentTape.

Custom layer creating new model parameters

Overfitting

  • Load Higgs CSV dataset using tf.data,
  • Train with a custom learning rate decay for the optimizer,
  • Record data later for TensorBoard,
  • Apply regularization and dropout to avoid overfitting, and
  • Apply early stopping.

Dataset Performance

Save & Load

Classify flowers with data augmentation layers

Classify flowers with data augmentation using dataset mapping

Transfer Learning

Image Segmentation

Source
Modified from source

Regression on CSV file: Using Pandas to process data

CSV preprocessing

Credits and References

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