What is the use of verbose in Keras while validating the model

Question

I m running the LSTM model for the first time  Here is my model   opt   Adam 0 002  inp   Input      print inp  x   Embedding       inp  x   LSTM      x  x   BatchNormalization   x  pred   Dense 5 activation  softmax   x   model   Model inp pred  model compile        idx   np random permutation X train shape 0   model fit X train idx   y train idx   nb epoch 1  batch size 128  verbose 1    What is the use of verbose while training the model

User · Answer

verbose  Integer  0  1  or 2  Verbosity mode   Verbose 0  silent   Verbose 1  progress bar   Train on 186219 samples  validate on 20691 samples Epoch 1 2 186219 186219                                  - 85s 455us step - loss  0 5815 - acc   0 7728 - val loss  0 4917 - val acc  0 8029 Train on 186219 samples  validate on 20691 samples Epoch 2 2 186219 186219                                  - 84s 451us step - loss  0 4921 - acc   0 8071 - val loss  0 4617 - val acc  0 8168   Verbose 2  one line per epoch   Train on 186219 samples  validate on 20691 samples Epoch 1 1  - 88s - loss  0 5746 - acc  0 7753 - val loss  0 4816 - val acc  0 8075 Train on 186219 samples  validate on 20691 samples Epoch 1 1  - 88s - loss  0 4880 - acc  0 8076 - val loss  0 5199 - val acc  0 8046

User · Answer

By default verbose   1    verbose   1  which includes both progress bar and one line per epoch  verbose   0  means silent  verbose   2  one line per epoch i e  epoch no  total no  of epochs

User · Answer

For verbose   0  fit method logs    loss  value of loss function for your training data acc  accuracy value for your training data    Note  If regularization mechanisms are used  they are turned on to avoid overfitting   if validation data or validation split arguments are not empty  fit method logs    val loss  value of loss function for your validation data val acc  accuracy value for your validation data   Note  Regularization mechanisms are turned off at testing time because we are using all the capabilities of the network   For example  using verbose while training the model helps to detect overfitting which occurs if your acc keeps improving while your val acc gets worse

User · Answer

The order of details provided with verbose flag are as  Less details     More details 0   lt   2   lt   1  Default is 1 For production environment  2 is recommended

User · Answer

Check documentation for model fit here   By setting verbose 0  1 or 2 you just say how do you want to  see  the training progress for each epoch   verbose 0 will show you nothing  silent   verbose 1 will show you an animated progress bar like this     verbose 2 will just mention the number of epoch like this

[python] What is the use of verbose in Keras while validating the model?

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