Reinforcement Learning with TensorFlow
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The LeNet-5 convolutional neural network

Architecture of LeNet-5, from Gradient-based Learning Applied to Document Recognition by LeCunn et al.(http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf)

LeNet-5 is a seven-level convolutional neural network, published by the team comprising of Yann LeCunn, Yoshua Bengio, Leon Bottou and Patrick Haffner in 1998 to classify digits, which was used by banks to recognize handwritten numbers on checks. The layers are ordered as:

  • Input image | Convolutional Layer 1(ReLU) | Pooling 1 |Convolutional Layer 2(ReLU) |Pooling 2 |Fully Connected (ReLU) 1 | Fully Connected 2 | Output
  • LeNet-5 had remarkable results, but the ability to process higher-resolution images required more convolutional layers, such as in AlexNet, VGG-Net, and Inception models.
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