- FaceNet (Google)
- They use a triplet loss with the goal of keeping the L2 intra-class distances low and inter-class distances high
- DeepID (Hong Kong University)
- They use verification and identification signals to train the network. Afer each convolutional layer there is an identity layer connected to the supervisory signals in order to train each layer closely (on top of normal backprop)
- DeepFace (Facebook)
- Convs followed by locally connected, followed by fully connected
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |