Apr 24, 2019 · This week, I implemented a character-level recurrent neural network (or char-rnn for short) in PyTorch , and used it to generate fake book titles. The code, training data, and pre-trained models can be found on my GitHub repo .
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Is training the recurrent network really needed ? View on GitHub. In this blog post, we are delving into World Models (Ha et al., 2018)[1] a recent model based reinforcement learning paper that achieves surprisingly good performance on the challenging CarRacing-v0 environment.

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  • Jun 07, 2019 · A collection of various deep learning architectures, models, and tips . Deep Learning Models. A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
  • Mar 10, 2018 · Recurrent Neural Network(RNN) are a type of Neural Network where the output from previous step are fed as input to the current step.In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it is required to predict the next word of a sentence, the previous words are required and hence there is a need to remember the previous words.

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where is the hidden state of the RNN, is the input from the previous layer, is the weight matrix for the input and is the weight matrix for the recurrent connections.. The hidden weight matrix is necessarily square - the number of hidden units remains the same, so there are the same number of inputs as there are outputs, so M must always equal K.

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Keras documentation. Keras API reference / Layers API / Recurrent layers Recurrent layers. LSTM layer; GRU layer; SimpleRNN layer

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Introduction This project is a PyTorch implementation of C-RNN-GAN, which was originally developed in TensorFlow. In a nutshell, C-RNN-GAN is a GAN variant where both the Generator and the Discriminator are RNNs, with each output at each timestep from the Generator correspondingly fed into each timestep as input to the Discriminator.

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自然语言处理 Python3 TensorFlow PyTorch Keras CNN RNN DNN VGG 语音识别 新手 简单 中等 相似度检测 视觉计算 文本生成 对话机器人 BERT Fast-RCNN FasterRCNN 北京智能工场科技有限公司旗下的FlyAI是为AI开发者提供数据竞赛并支持GPU离线训练的一站式服务平台。

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Sep 27, 2017 · In my previous RNN example, it seems using 0.8 is appropriate. But for VRNN I feel a higher temperature is allowed. Like char-rnn demo, the overall dialogue format is well reserved. Otherwise, no good. For better results, train longer time and use multi-layer RNN modules.

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双向RNN的表现优于单向RNN,而LSTM的表现优于Simple RNN。对于Simple RNN而言,Elman的表现不比Jordan差(甚至更好),而用时更少并且实现更简单,这可能是主流深度学习框架(TensorFlow / Pytorch等)的simple RNN是基于Elman的原因。

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Sep 16, 2016 · Let me introduce a repository that you might find useful during deep learning training especially when you use large batch size in PyTorch. If you ever needed or wished to try out the training of a model with bigger batch size than you could solve with your own GPU memory or with Google Colab you would find our library a useful tool.

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torch.nn.utils.rnn.pack_sequence¶ torch.nn.utils.rnn.pack_sequence (sequences, enforce_sorted=True) [source] ¶ Packs a list of variable length Tensors. sequences should be a list of Tensors of size L x *, where L is the length of a sequence and * is any number of trailing dimensions, including zero.

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