training dataset的作用：training dataset,[3] that is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model.[4] The model (e.g. a neural net or a naive Bayes classifier) is trained on the training dataset using a supervised learning method (e.g. gradient descent or stochastic gradient descent). In practice, the training dataset often consists of pairs of an input vector (or scalar) and the corresponding output vector (or scalar), which is commonly denoted as the target (or label). 简言之，training dataset就是让模型来调整参数的set。
test dataset作用：test dataset is a dataset used to provide an unbiased evaluation of a final model fit on the training dataset.A test dataset is a dataset that is independent of the training dataset, but that follows the same probability distribution as the training dataset.The test dataset is used to obtain the performance characteristics such as accuracy, sensitivity, specificity, F-measure, and so on. test dataset是一个与training dataset有相同的概率分布但与training dataset相独立的数据集，用来最终测试被训练出来的参数和模型的正确度。
那么validation dataset是干什么的呢？
validation dataset有三个主要的作用：
1.The basic process of using a validation dataset for model selection
2. The validation dataset provides an unbiased evaluation of a model fit on the training dataset while tuning the model's hyperparameters [5] (e.g. the number of hidden units in a neural network[4]). A validation dataset is a dataset of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set".
3. Validation datasets can be used for regularization by early stopping: stop training when the error on the validation dataset increases, as this is a sign of overfitting to the training dataset.
总结：The validation dataset functions as a hybrid: it is training data used for testing, but neither as part of the low-level training nor as part of the final testing.
validation dataset提供三个主要功能：
一是用来选择模型，当同一批数据使用不同的模型来训练时，把test dataset单独保留起来，作为最后的检验模型的数据集，使用validation dataset来测试每个模型的准确度；
二是调整超参数，参数的调整是通过深度网络自己来调整，但是超参数的调整目前还是靠人工经验，虽然现在有针对超参数调节的自动网络，同样的道理，test dataset保留起来，作为最后的试金石，validation dataset便起到中间的测试模型精度的数据集；三是通过early stopping防止过拟合，过拟合都有相应的信号出现，比如模型在训练集上表现良好，但是validation dataset上误差开始增大
所以validation dataset的作用总结为：它是一个混合作用，它是用来测试的训练数据，但是既不是低级的训练数据，也不是最终的测试数据。
PS.
Cross-validation[edit]
A dataset can be repeatedly split into a training dataset and a validation dataset: this is known as cross-validation. These repeated partitions can be done in various ways, such as dividing into 2 equal datasets and using them as training/validation, and then validation/training, or repeatedly selecting a random subset as a validation dataset[citation needed]. To validate the model performance, sometimes an additional test dataset that was held out from cross-validation is used.
From: https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets