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

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一个人的身影
一个人的身影 2021-01-29 22:32

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 = Bat         


        
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  • 2021-01-29 23:22

    The order of details provided with verbose flag are as

    Less details.... More details

    0 < 2 < 1

    Default is 1

    For production environment, 2 is recommended

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  • 2021-01-29 23:23

    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:

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  • 2021-01-29 23:27

    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.

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  • 2021-01-29 23:32

    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

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  • 2021-01-29 23:35

    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
    
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