验证模型时，Keras 中的verbose 有什么用?

问题描述

我是第一次运行 LSTM 模型.这是我的模型:

opt = Adam(0.002)inp = 输入(...)打印(输入)x = 嵌入(....)(inp)x = LSTM(...)(x)x = BatchNormalization()(x)pred = Dense(5,activation='softmax')(x)模型 = 模型(输入，预测)模型.编译(....)idx = np.random.permutation(X_train.shape[0])模型.fit(X_train[idx]，y_train[idx]，nb_epoch=1，batch_size=128，verbose=1)

在训练模型时verbose有什么用?

解决方案

查看 model.fit 的文档

verbose=2 会像这样提到时代的数量:

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?

解决方案

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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