Explain embedding layers and special purpose layers in lasagne.

In this recipe, we will see what are Embedding layers and Special purpose layers in lasagne layers.

Recipe Objective - What are Embedding layers and Special-purpose layers in lasagne layers?

Embedding layers:-

1. EmbeddingLayer - A layer for word embeddings.

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Special-purpose layers:-

1. NonlinearityLayer - A layer that just applies a nonlinearity.

2. BiasLayer - A layer that just adds a (trainable) bias term.

3. ScaleLayer - A layer that scales its inputs by learned coefficients.

4. standardize - Convenience function for standardizing inputs by applying a fixed offset and scale.

5. ExpressionLayer - This layer provides a boilerplate for a custom layer that applies a simple transformation to the input.

6. TransformerLayer - Spatial transformer layer.

7. TPSTransformerLayer - Spatial transformer layer.

8. prelu - Convenience function to apply parametric rectification to a given layer’s output.

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Embedding layers Example:-

from lasagne.layers import EmbeddingLayer, InputLayer, get_output
import theano.tensor as T
import theano
import numpy as np
x = T.imatrix()
ly_in = InputLayer((3, ))
W = np.arange(3*5).reshape((3, 5)).astype('float32')
ly1 = EmbeddingLayer(ly_in, input_size=3, output_size=5, W=W)
output = get_output(ly1, x)
f = theano.function([x], output)
x_test = np.array([[0, 2], [1, 2]]).astype('int32')
f(x_test)

array([[[ 0.,  1.,  2.,  3.,  4.],
        [10., 11., 12., 13., 14.]],

       [[ 5.,  6.,  7.,  8.,  9.],
        [10., 11., 12., 13., 14.]]], dtype=float32)

Special-purpose layers Example:-

from lasagne.layers import InputLayer, ExpressionLayer
ly_in = InputLayer((32, 100, 20))
ly1 = ExpressionLayer(ly_in, lambda X: X.mean(-1), output_shape='auto')
ly1.output_shape

from lasagne.layers import InputLayer, DenseLayer, rrelu
layer = InputLayer((32, 100))
layer = DenseLayer(layer, num_units=200)
layer = rrelu(layer)

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