Sentence examples for mini batches from inspiring English sources

In the optimization, we use 25,000 training samples per class and feed mini batches in size of 10.

In all the above experiments, for DNN training, we used the Adam optimizer algorithm, in mini batches, considering the Mean Squared Error (MSE) as cost function.

The cross entropy was set as the objective function of the DNN training, and the stochastic gradient descendent (SGD) approach was employed to perform optimization, with the mini batch size set to 256 frames.

To update these parameters we used mini-batches gradient descent technique.

We then subdivided all training datasets into mini-batches, with 128 data vectors for unsupervised pre-training.

We use the "mini-batches" learning [36], where the parameters are updated after every n data points (i.e., this approach divides the dataset into piles and learns each pile separately).

Based on the experimental results in this work, choosing optimized parameters for learning rate between 0.008 and 0.01, a frequency of input spikes 18 Hz, the sizes of mini-batches between 25 and 50, and two different membrane voltage thresholds for hidden and visible layers leads to the optimum results in the accuracy rate of the network recognition.

The weight was updated after each mini-batch.

The mini-batch size was set to 128.

DAE training was carried out using mini-batch conjugate gradients with a mini-batch size of 128 samples.

The DNN training is carried out using stochastic mini-batch gradient descend with a mini-batch size of 512 samples.