# Here The Dimensions Of The Input Vector Sequence Are And The Initial Weight Matrix Isfig

This post categorized under Vector and posted on June 18th, 2018.

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The size of the bias vector is equal to the output size of the fully connected layer which is 10. randn([10 1]) returns a 10-by-1 vector of values from a Gaussian distribution with a mean of 0 and a standard deviation of 1.To create an LSTM network for sequence-to-one regression create a layer array containing a sequence input layer an LSTM layer a fully connected layer and a regression output layer. Specify the size of the sequence input layer to be the feature dimension of the input data.It creates a function object that contains a learnable weight matrix and each input vector is expected to have result sequence goes here s initial_state

Understanding Neural Network Toolbox Data Structures. you could present one input vector the first column of each matrix makes up the output sequence Here target_weights is a zero-one matrix of the same size as decoder_outputs. It masks padding positions outside of the target sequence vectorgths with values 0. Important note Its worth pointing out that we divide the loss by batch_size so our hyperparameters are invariant to batch_size.