What are Recurrent layers in lasagne layers?

This recipe explains what are Recurrent layers in lasagne layers.

Recipe Objective - What are Recurrent layers in lasagne layers?

Layers to construct recurrent networks. Recurrent layers can be used similarly to feed-forward layers except that the

input shape is expected to be ("batch_size", "sequence_length", "num_inputs").

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Recurrent layers:-

1. CustomRecurrentLayer - A layer which implements a recurrent connection.

2. RecurrentLayer - Dense recurrent neural network (RNN) layer.

3. LSTMLayer - A long short-term memory (LSTM) layer.

4. GRULayer - Gated Recurrent Unit (GRU) Layer.

Example:-

from lasagne.layers import *
num_inputs, num_units, num_classes = 10, 12, 5
# By setting the first two dimensions as None, we are allowing them to vary
# They correspond to batch size and sequence length, so we will be able to
# feed in batches of varying size with sequences of varying length.
ly_inp = InputLayer((None, None, num_inputs))
# We can retrieve symbolic references to the input variable's shape
# we will later use in reshape layers.
batchsize, seqlen, _ = ly_inp.input_var.shape
ly_lstm = LSTMLayer(ly_inp, num_units=num_units)
# In order to connect a recurrent layer to a dense layer

ly_shp = ReshapeLayer(ly_lstm, (-1, num_units))
ly_dense = DenseLayer(ly_shp, num_units=num_classes)

# To reshape back to our original shape, we can use the symbolic shape
ly_out = ReshapeLayer(ly_dense, (batchsize, seqlen, num_classes))


import lasagne
no_batch, no_steps, no_in = (2, 3, 4)
no_hid = 5
ly_in = lasagne.layers.InputLayer((no_batch, no_steps, no_in))
ly_in_hid = lasagne.layers.DenseLayer(lasagne.layers.InputLayer((None, no_in)), no_hid)
ly_hid_hid = lasagne.layers.DenseLayer(lasagne.layers.InputLayer((None, no_hid)), no_hid)
ly_rec = lasagne.layers.CustomRecurrentLayer(ly_in, ly_in_hid, ly_hid_hid)

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