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Different levels of abstraction in convolutional neural network implementations with TensorFlow
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1_CNN_fromscratch.py
2_CNN_lowlevel.py
3_CNN_midlevel.py
4_CNN_highlevel.py
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README.md

README.md

CNNs-with-TensorFlow

Differently abstract implementations of an exemplary convolutional neural network with TensorFlow.

  • 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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