"Pytorch" Pytorch Advanced Tutorial Three

The previous section describes the use of Pytorch to construct a CNN network, which introduces points to advanced things lstm.

Please refer to the two famous blogs about Lstm's introduction to the theory:

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

And one of my previous Chinese translation blogs:

http://blog.csdn.net/q295684174/article/details/78973445 LSTM

Class Torch.nn.LSTM (*args, **kwargs)

Parameters input_size input feature dimension hidden_size the number of dimensions of the hidden layer num_layers rnn layer bias The hidden layer state is bias, the default is True Batch_first Whether the first dimension of the input output is batchsize dropout whether to add dropout layer after the RNN layer except the last RNN layer bidirectional is bidirectional RNN, the default is False

Inputs:input, (H_0, c_0) input (Seq_len, batch, input_size) contains the input sequence of the feature, and if Batch_first is set, batch is the first dimension (H_0, c_0) hidden layer State

The outputs:output, (H_n, c_n) output (Seq_len, batch, Hidden_size * num_directions) contains the output characteristics for each moment, if the Batch_first is set , batch is the first dimension (H_n, c_n) hidden layer State Model

Class RNN (NN. Module):
    def __init__ (self, input_size, hidden_size, Num_layers, num_classes):
        super (RNN, self). __init__ ()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn. LSTM (Input_size, Hidden_size, Num_layers, 
                            batch_first=true)
        self.fc = nn. Linear (Hidden_size, Num_classes)  # 2 for Bidirection 

    def forward (self, x):

        # forward Propagate RNN
        Out, _ = self.lstm (x) # Decode Hidden state of the last time

        step
        out = SELF.FC (out[:,-1,:])
        return

out RNN = RNN (Input_size, Hidden_size, Num_layers, num_classes)
Rnn.cuda ()

Pytorch implementation of LSTM is very convenient, only need to define the input dimension, the hidden layer dimension, the number of RNN, as well as the number of categories can be. If the input state of the lstm is empty, it is initialized to 0 by default. On Mnist, it takes only 2 epochs to achieve a 97% accuracy.