首先建立 编码网络encoder network 它是一个RNN结构,RNN的子结构可以是GRU或者LSTM,每次向网络中输入一个单词,将输入序列接收完毕后,这个RNN会输出一个 RNN条件生成与Attention机制 And Tell 模型引入了更好的CNN和LSTM来做的 图像特征使用更强大的CNN来提取,例如Goolnet,residual net,而且只提取一次 然后利用LSTM...
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TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary support for PyTorch): # ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py import shap import numpy as np # select a set of background examples to...
This makes it easy to switch out any type of model or processor. Perhaps you need a CNN or an RNN or a Regex model to label with--all are possible. A model or processor can be created from the default architecture or loaded from an existing model or processor. Creating your own data ...