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CNNs-with-TensorFlow

Note: The implementations are using tensorflow 1, see tensorflow-2-simple-examples for tensorflow 2 examples.

Implementations of an exemplary convolutional neural network with TensorFlow 1 using APIs at different levels of abstraction.

  • Convolutional layer from scratch: Convolutional neural network implementation with convolutional and pooling layers built from scratch with core TensorFlow.

  • Low-level TensorFlow: Convolutional neural network using tf.nn.conv2d and tf.nn.avg_pool with explicit definitions of weights, biases, and placeholders.

  • Mid-level TensorFlow: Convolutional neural network using tf.keras.layers managing weights and biases for us, whereas placeholders and the session are still explicit.

  • High-level TensorFlow: Convolutional neural network using tf.keras.model.Sequential (everything is managed).

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Different levels of abstraction in convolutional neural network implementations with TensorFlow

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