How to create a network using lasagne?

This recipe helps you to create a network using lasagne.

Recipe Objective - How to create a network using lasagne?

We can specify one or more other layers that the layer you are creating gets its input from. But "InputLayer" can be used to

represent the input of a network.

Learn How to Build a Multi Class Text Classification Model using BERT

For more related projects:-

/projects/data-science-projects/keras-deep-learning-projects
/projects/data-science-projects/neural-network-projects

1. Creating a Network:-

import lasagne
import theano.tensor as T

ly_in = lasagne.layers.InputLayer((100, 50))

ly_hidden = lasagne.layers.DenseLayer(ly_in, num_units=200)
ly_out = lasagne.layers.DenseLayer(ly_hidden, num_units=10, nonlinearity=T.nnet.softmax)

2. Naming the layers:-

ly_hidden = lasagne.layers.DenseLayer(ly_in, num_units=200, name="hidden_layer")

3. Initializing parameters:-

ly = lasagne.layers.DenseLayer(ly_in, num_units=100, W=lasagne.init.Normal(0.01))

4. Parameter sharing:-

ly1 = lasagne.layers.DenseLayer(ly_in, num_units=100)
ly2 = lasagne.layers.DenseLayer(ly_in, num_units=100, W=ly1.W)

5. Propagating data through layers:-

y = lasagne.layers.get_output(ly_out)

import theano
import numpy as np

f = theano.function([ly_in.input_var], lasagne.layers.get_output(ly_out))

x = T.matrix('x')
y = lasagne.layers.get_output(ly_out, x)
f = theano.function([x], y)

y = lasagne.layers.get_output(ly_out, deterministic=True)
y1 = lasagne.layers.get_output([ly_out])
print(y) print(y1)

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