Question

为什么我在Rstudio的Keras上使用Keras在训练期间打印所有这些新线？我可以以某种方式阻止它吗？我有另一台运行相同型号的机器，我没有得到这些新线。在Windows 7上运行R 3.4.1

这是代码，我打电话给TestOOS（）开始培训

library('caret')
library(keras)

neural.train = function(model,XY) 
{
  XY <- as.matrix(XY)
  X <- XY[,-ncol(XY)]
  Y <- XY[,ncol(XY)]
  Y <- ifelse(Y > 0,1,0)

  newModel <- keras_model_sequential() 
  newModel %>% 
    layer_dense(units = 20, activation = 'relu', input_shape = c(20)) %>% 
    layer_dense(units = 15, activation = 'relu') %>% 
    layer_dense(units = 10, activation = 'relu') %>% 
    layer_dropout(rate = 0.2) %>% 
    layer_dense(units = 5, activation = 'relu') %>% 
    layer_dropout(rate = 0.2) %>% 
    layer_dense(units = 1) %>% 
    layer_activation('sigmoid')

  newModel %>% compile( 
    optimizer = optimizer_sgd(lr = 0.01, momentum = 0, decay = 0, nesterov = FALSE),
    loss = 'binary_crossentropy',
    metrics = c('accuracy')
  )

  newModel %>% fit(X, Y, epochs=100, batch_size=100, validation_split = 0.2)
  Models[[model]] <<- newModel
}

neural.predict = function(model,X) 
{
  if(is.vector(X)) X <- t(X)
  classes <- model %>% predict_classes(X)
  #return(nn.predict(Models[[model]],X))
}

neural.init = function()
{
  set.seed(365)
  Models <<- vector("list")
}

TestOOS = function() 
{
  neural.init()
  XY <<- read.csv('C:/Users/BL-Kyl/Zorro/Data/DeepSignalsEURUSD_L.csv',header = F)
  splits <- nrow(XY)*0.8
  XY.tr <<- head(XY,splits);
  XY.ts <<- tail(XY,-splits)
  neural.train(2,XY.tr)
  X <<- XY.ts[,-ncol(XY.ts)]
  Y <<- XY.ts[,ncol(XY.ts)]
  Y.ob <<- ifelse(Y > 0,1,0)
  Y <<- neural.predict(2,X)
  Y.pr <<- ifelse(Y > 0.5,1,0)
  confusionMatrix(Y.pr,Y.ob)
}

并产生此输出

Using TensorFlow backend.

Train on 95666 samples, validate on 23917 samples

Epoch 1/100

  8

 15

 2

 2

 3

....

Another 100 lines or so

...

63

64

65

66100/95666 [===================>..........]
 - ETA: 2s - loss: 0.6886 - acc: 0.5411

如果最后两行的前几位数字发生变化，则看起来会打印一个新行

编辑：创建一个gif来显示问题http://imgur.com/a/Evwl7

Rstudio中的Keras / Tensorflow - 为什么训练期间所有这些新线路？

0 个答案: