rnn

问题 I am trying to train an LSTM recurrent neural network, for sequence classification. My data has the following formart: Input: [1,5,2,3,6,2, ...] -> Output: 1 Input: [2,10,4,6,12,4, ...] -> Output: 1 Input: [4,1,7,1,9,2, ...] -> Output: 2 Input: [1,3,5,9,10,20, ...] -> Output: 3 . . . So basically I want to provide a sequence as an input and get an integer as an output. Each input sequence has length = 2000 float numbers, and I have around 1485 samples for training The output is just an

问题 I am experimenting with LSTMs in Keras with little to no luck. At some moment I decided to scale back to the most basic problems in order finally achieve some positive result. However, even with simplest problems I find that Keras is unable to converge while the implementation of the same problem in Tensorflow gives stable result. I am unwilling to just switch to Tensorflow without understanding why Keras keeps diverging on any problem I attempt. My problem is a many-to-many sequence

问题 When I train the model using the .fit() layer there is the argument shuffle preset to True. Let's say that my dataset has 100 samples and that the batch size is 10. When I set shuffle = True then keras first randomly selects randomly the samples (now the 100 samples have a different order) and on the new order it will start creating the batches: batch 1: 1-10, batch 2: 11-20 etc. If I set shuffle = 'batch' how is it supposed to work in the background? Intuitively and using the previous

问题 I have been trying to build RNN with Seq2Seq model from Udemy course called DeepLearning_NLP_Chatbot, and I followed him step by step, but I face when training an error: InvalidArgumentError : Received a label value of 8825 which is outside the valid range of [0, 8825). The dataset here. here is the data procesing dataset # Building a Chatbot with Deep NLP. # Importing the libraries. import numpy as np import tensorflow as tf import re import time # ---Data Processing---# #-------------------

来源： https://stackoverflow.com/questions/52145992/how-do-we-analyse-a-loss-vs-epochs-graph

来源： https://stackoverflow.com/questions/52145992/how-do-we-analyse-a-loss-vs-epochs-graph

来源： https://stackoverflow.com/questions/46518041/what-evaluation-metric-should-i-use-to-compare-knowledge-based-approach-with-gen

来源： https://stackoverflow.com/questions/46518041/what-evaluation-metric-should-i-use-to-compare-knowledge-based-approach-with-gen

问题 import torch,ipdb import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable rnn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2) input = Variable(torch.randn(5, 3, 10)) h0 = Variable(torch.randn(2, 3, 20)) c0 = Variable(torch.randn(2, 3, 20)) output, hn = rnn(input, (h0, c0)) This is the LSTM example from the docs. I don't know understand the following things: What is output-size and why is it

问题 Using this implementation I have included attention to my RNN (which classify the input sequences into two classes) as follows. visible = Input(shape=(250,)) embed=Embedding(vocab_size,100)(visible) activations= keras.layers.GRU(250, return_sequences=True)(embed) attention = TimeDistributed(Dense(1, activation='tanh'))(activations) attention = Flatten()(attention) attention = Activation('softmax')(attention) attention = RepeatVector(250)(attention) attention = Permute([2, 1])(attention) sent