转自：https://blog.csdn.net/u012458963/article/details/74357802  

RNN

首先了解了RNN，有很多的教程，这里大概贴几个网址 
https://www.yunaitong.cn/understanding-lstm-networks.html 
http://feisky.xyz/machine-learning/rnn/ 
还有莫烦的教程 我自己尝试着用keras写RNN的例子，参考了莫烦的教程，但是用莫烦的程序得到的结果特别不理想 然后就尝试着用开发者fchollet的例子 
https://github.com/fchollet/keras/blob/8f4d6fc3fa6cd35a36de190a5e44ab4817cc68e8/examples/mnist_irnn.py 用f大神的例子运行速度特别慢，运行结果如图所示，结果也不理想 
然后我看了下代码，代码用的是784个timestep，一个量一个量传的。 X_train = X_train.reshape(X_train.shape[0], -1, 1) X_test = X_test.reshape(X_test.shape[0], -1, 1)

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于是我把输入进行了修改， 变成28个timestep, 每次输入的量是28个量 x_train = x_train.reshape(-1, 28, 28) x_test = x_test.reshape(-1, 28, 28)

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这样运行的速度就快多了，但是运行效果还是不理想 
我百思不得其解，然后我自己重新写代码，发现在f大神的代码中有learning rate, 有了LR，运行结果总是60~70%，当我去掉LR时，运行结果就在96%左右 附上代码 #!/usr/bin/python # -*- coding:utf8 -*- from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Activation from keras.layers import SimpleRNN from keras import initializers from keras.optimizers import RMSprop batch_size = 50 num_classes = 10 epochs = 100 hidden_units = 50 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(-1, 28, 28) x_test = x_test.reshape(-1, 28, 28) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) print('Evaluate RNN...') model = Sequential() model.add(SimpleRNN(hidden_units, kernel_initializer=initializers.RandomNormal(stddev=0.001), recurrent_initializer=initializers.Identity(gain=1.0), activation='relu', input_shape=x_train.shape[1:])) model.add(Dense(num_classes)) model.add(Activation('softmax')) rmsprop = RMSprop() model.compile(loss='categorical_crossentropy', optimizer=rmsprop, metrics=['accuracy']) model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) scores = model.evaluate(x_test, y_test, verbose=0) print('RNN test score:', scores[0]) print('RNN test accuracy:', scores[1])

PhysioNet

下载了几篇文章 https://www.researchgate.net/publication/266963784_EEG-Based_Classification_of_Imagined_Fists_Movements_using_Machine_Learning_and_Wavelet_Transform_Analysis