What is the use of verbose in Keras while validating the model?
PythonDeep LearningKerasVerbosePython Problem Overview
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?
Python Solutions
Solution 1 - Python
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:
Solution 2 - Python
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
Solution 3 - Python
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.
Solution 4 - Python
verbose is the choice that how you want to see the output of your Nural Network while it's training. If you set verbose = 0, It will show nothing
If you set verbose = 1, It will show the output like this Epoch 1/200 55/55[==============================] - 10s 307ms/step - loss: 0.56 - accuracy: 0.4949
If you set verbose = 2, The output will be like Epoch 1/200 Epoch 2/200 Epoch 3/200
Solution 5 - Python
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
Solution 6 - Python
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