文件名称:rnn-from-scratch-master
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RNN神经网络的应用和概念,RNN源代码和使用方法-You can find that the parameters `(W, U, V)` are shared in different time steps. And the output in each time step can be**softmax**. So you can use**cross entropy** loss as an error function and use some optimizing method (e.g. gradient descent) to calculate the optimized parameters `(W, U, V)`.
Let recap the equations of our RNN:
Let recap the equations of our RNN:
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下载文件列表
rnn-from-scratch-master/
rnn-from-scratch-master/README.md
rnn-from-scratch-master/__pycache__/
rnn-from-scratch-master/__pycache__/activation.cpython-34.pyc
rnn-from-scratch-master/__pycache__/gate.cpython-34.pyc
rnn-from-scratch-master/__pycache__/layer.cpython-34.pyc
rnn-from-scratch-master/__pycache__/output.cpython-34.pyc
rnn-from-scratch-master/__pycache__/preprocessing.cpython-34.pyc
rnn-from-scratch-master/__pycache__/rnn.cpython-34.pyc
rnn-from-scratch-master/activation.py
rnn-from-scratch-master/data/
rnn-from-scratch-master/data/reddit-comments-2015-08.csv
rnn-from-scratch-master/figures/
rnn-from-scratch-master/figures/gradient.png
rnn-from-scratch-master/figures/init.png
rnn-from-scratch-master/figures/rnn-bptt-with-gradients.png
rnn-from-scratch-master/figures/rnn-bptt1.png
rnn-from-scratch-master/figures/rnn-compuattion-graph.png
rnn-from-scratch-master/figures/rnn-compuattion-graph_2.png
rnn-from-scratch-master/figures/rnn.jpg
rnn-from-scratch-master/figures/rnn_equation.png
rnn-from-scratch-master/figures/rnn_eval.png
rnn-from-scratch-master/figures/rnn_loss.png
rnn-from-scratch-master/figures/rnn_loss_2.png
rnn-from-scratch-master/gate.py
rnn-from-scratch-master/layer.py
rnn-from-scratch-master/output.py
rnn-from-scratch-master/preprocessing.py
rnn-from-scratch-master/rnn.py
rnn-from-scratch-master/rnnlm.py
rnn-from-scratch-master/README.md
rnn-from-scratch-master/__pycache__/
rnn-from-scratch-master/__pycache__/activation.cpython-34.pyc
rnn-from-scratch-master/__pycache__/gate.cpython-34.pyc
rnn-from-scratch-master/__pycache__/layer.cpython-34.pyc
rnn-from-scratch-master/__pycache__/output.cpython-34.pyc
rnn-from-scratch-master/__pycache__/preprocessing.cpython-34.pyc
rnn-from-scratch-master/__pycache__/rnn.cpython-34.pyc
rnn-from-scratch-master/activation.py
rnn-from-scratch-master/data/
rnn-from-scratch-master/data/reddit-comments-2015-08.csv
rnn-from-scratch-master/figures/
rnn-from-scratch-master/figures/gradient.png
rnn-from-scratch-master/figures/init.png
rnn-from-scratch-master/figures/rnn-bptt-with-gradients.png
rnn-from-scratch-master/figures/rnn-bptt1.png
rnn-from-scratch-master/figures/rnn-compuattion-graph.png
rnn-from-scratch-master/figures/rnn-compuattion-graph_2.png
rnn-from-scratch-master/figures/rnn.jpg
rnn-from-scratch-master/figures/rnn_equation.png
rnn-from-scratch-master/figures/rnn_eval.png
rnn-from-scratch-master/figures/rnn_loss.png
rnn-from-scratch-master/figures/rnn_loss_2.png
rnn-from-scratch-master/gate.py
rnn-from-scratch-master/layer.py
rnn-from-scratch-master/output.py
rnn-from-scratch-master/preprocessing.py
rnn-from-scratch-master/rnn.py
rnn-from-scratch-master/rnnlm.py
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