锐客网

  1. 首页 > 睿知 > it技术 > >

lstm代码java lstm代码中的激活函数( 三 )

IT技术模型


import pandas as pdimport numpy as npdef create_interval_dataset(dataset, look_back):
""":param dataset: input array of time intervals:param look_back: each training set feature length:return: convert an array of values into a dataset matrix."""
dataX, dataY = [], []for i in range(len(dataset) - look_back):
dataX.append(dataset[i:i+look_back])
dataY.append(dataset[i+look_back])return np.asarray(dataX), np.asarray(dataY)
df = pd.read_csv("path-to-your-time-interval-file")
dataset_init = np.asarray(df)# if only 1 columndataX, dataY = create_interval_dataset(dataset, lookback=3)# look back if the training set sequence length
这里的输入数据来源是csv文件，如果输入数据是来自数据库的话可以参考这里
LSTM网络结构搭建
import pandas as pdimport numpy as npimport randomfrom keras.models import Sequential, model_from_jsonfrom keras.layers import Dense, LSTM, Dropoutclass NeuralNetwork():
def __init__(self, **kwargs):
""":param **kwargs: output_dim=4: output dimension of LSTM layer; activation_lstm='tanh': activation function for LSTM layers; activation_dense='relu': activation function for Dense layer; activation_last='sigmoid': activation function for last layer; drop_out=0.2: fraction of input units to drop; np_epoch=10, the number of epoches to train the model. epoch is one forward pass and one backward pass of all the training examples; batch_size=32: number of samples per gradient update. The higher the batch size, the more memory space you'll need; loss='mean_square_error': loss function; optimizer='rmsprop'"""
self.output_dim = kwargs.get('output_dim', 8)self.activation_lstm = kwargs.get('activation_lstm', 'relu')self.activation_dense = kwargs.get('activation_dense', 'relu')self.activation_last = kwargs.get('activation_last', 'softmax')# softmax for multiple output
self.dense_layer = kwargs.get('dense_layer', 2)# at least 2 layers
self.lstm_layer = kwargs.get('lstm_layer', 2)self.drop_out = kwargs.get('drop_out', 0.2)self.nb_epoch = kwargs.get('nb_epoch', 10)self.batch_size = kwargs.get('batch_size', 100)self.loss = kwargs.get('loss', 'categorical_crossentropy')self.optimizer = kwargs.get('optimizer', 'rmsprop')def NN_model(self, trainX, trainY, testX, testY):
""":param trainX: training data set:param trainY: expect value of training data:param testX: test data set:param testY: epect value of test data:return: model after training"""
print "Training model is LSTM network!"
input_dim = trainX[1].shape[1]
output_dim = trainY.shape[1] # one-hot label
# print predefined parameters of current model:
model = Sequential()# applying a LSTM layer with x dim output and y dim input. Use dropout parameter to avoid overfitting
model.add(LSTM(output_dim=self.output_dim,
input_dim=input_dim,
activation=self.activation_lstm,
dropout_U=self.drop_out,
return_sequences=True))for i in range(self.lstm_layer-2):
model.add(LSTM(output_dim=self.output_dim,
input_dim=self.output_dim,
activation=self.activation_lstm,
dropout_U=self.drop_out,
return_sequences=True))# argument return_sequences should be false in last lstm layer to avoid input dimension incompatibility with dense layer
model.add(LSTM(output_dim=self.output_dim,
input_dim=self.output_dim,
activation=self.activation_lstm,
dropout_U=self.drop_out))for i in range(self.dense_layer-1):
model.add(Dense(output_dim=self.output_dim,
activation=self.activation_last))
model.add(Dense(output_dim=output_dim,
input_dim=self.output_dim,
activation=self.activation_last))# configure the learning process
model.compile(loss=self.loss, optimizer=self.optimizer, metrics=['accuracy'])# train the model with fixed number of epoches
model.fit(x=trainX, y=trainY, nb_epoch=self.nb_epoch, batch_size=self.batch_size, validation_data=https://www.04ip.com/post/(testX, testY))# store model to json file

推荐阅读


上一篇：loadrunner测试html5，loadrunner测试结果分析

下一篇：Pdf怎么卖，卖pdf书