Convolutional layers typically consist of fewer parameters than fully connected layers.ĭefine the layers of the fully convolutional network described in, comprising 16 convolutional layers. The layer convolves the input by moving the filters along the input vertically and horizontally and computing the dot product of the weights and the input, and then adding a bias term. A 2-D convolutional layer applies sliding filters to the input. Speech Denoising with Convolutional LayersĬonsider a network that uses convolutional layers instead of fully connected layers. This example uses the adaptive moment estimation (Adam) solver. Set ValidationFrequency such that the validation mean square error is computed once per epoch. Set ValidationData to the validation predictors and targets. Specify LearnRateSchedule to "piecewise" to decrease the learning rate by a specified factor (0.9) every time a certain number of epochs (1) has passed. Specify Shuffle as "every-epoch" to shuffle the training sequences at the beginning of each epoch. Set Verbose to false to disable printing the table output that corresponds to the data shown in the plot into the command line window. Specify Plots as "training-progress" to generate plots that show the training progress as the number of iterations increases. Set MiniBatchSize of 128 so that the network looks at 128 training signals at a time. Set Ma圎pochs to 3 so that the network makes 3 passes through the training data. Next, specify the training options for the network.
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