Autoencoder Encoder Network Example

import theano
import lasagne.layers
from lasagne.layers import Conv2DLayer as C2D
from lasagne.nonlinearities import rectify as relu
from lasagne.layers import NonlinearityLayer as NL
from lasagne.layers import ElemwiseSumLayer as ESL
from lasagne.layers import batch_norm as BN

l_in = lasagne.layers.InputLayer(shape=(None,3,64,64)) # Assume incoming shape is a batch x RGB x W x H image
encoder_stem = C2D(
        incoming = l_in,
        num_filters = 128,
        filter_size = [5,5],
        stride = [2,2],
        pad = (2,2),
        nonlinearity = None,
        name =  'encoder_stem'
        )
encoder_residual_block1 = ESL([C2D(incoming = NL(BN(C2D(
        incoming = NL(BN(encoder_stem,name='encoder_stem_bnorm'),relu),
        num_filters = 128,
        filter_size = [3,3],
        stride = [1,1],
        pad = (1,1),
        nonlinearity = None,
        name =  'enc_res1_1'),name='enc_res1_bnorm'),relu),
        num_filters = 128,
        filter_size = [3,3],
        stride = [1,1],
        pad = (1,1),
        nonlinearity = None,
        name =  'enc_res1_2'),
        encoder_stem])
encoder_downsample1=C2D(
        incoming = encoder_residual_block1,
        num_filters = 256,
        filter_size = [5,5],
        stride = [2,2],
        pad = (2,2),
        nonlinearity = None,
        name =  'encoder_downsample1'
        )        
encoder_residual_block2 = ESL([C2D(incoming = NL(BN(C2D(
        incoming = NL(BN(encoder_downsample1,name='encoder_downsample1_bnorm'),relu),
        num_filters = 128,
        filter_size = [3,3],
        stride = [1,1],
        pad = (1,1),
        nonlinearity = None,
        name =  'enc_res2_1'),name='enc_res2_bnorm'),relu),
        num_filters = 128,
        filter_size = [3,3],
        stride = [1,1],
        pad = (1,1),
        nonlinearity = None,
        name =  'enc_res2_2'),
        encoder_stem])
 encoder_mlp = BN(lasagne.layers.DenseLayer(NL(BN(encoder_residual_block2,name='enc_resblock2_bnorm'),relu),num_units=500,nonlinearity=relu),name='enc_mlp_bnorm')