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

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?

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:

progres_bar

verbose=2 will just mention the number of epoch like this:

enter image description here

From: stackoverflow.com/q/47902295