Learning Data Mining with Python

Classifying Objects in Images Using Deep Learning
We then import some requirements, which we will explain as we use them:
from lasagne import updates
from nolearn.lasagne import NeuralNet
from lasagne.nonlinearities import sigmoid, softmax
Next we define the neural network, which is represented as a scikit-learn-compatible
estimator:
net1 = NeuralNet(layers=layers,
Note that we haven't closed off the parenthesis—this is deliberate. At this point, we
enter the parameters for the neural network, starting with the size of each layer:
input_shape=X.shape,
hidden_num_units=100,
output_num_units=26,
The parameters here match the layers. In other words, the input_shape parameter
first finds the layer in our layers that has given the name input, working much the
same way as setting parameters in pipelines.
Next, we define the nonlinearities. Again, we will use sigmoid for the hidden layer
and softmax for the output layer:
hidden_nonlinearity=sigmoid,
output_nonlinearity=softmax,
Next, we will use bias nodes, which are nodes that are always turned on in the
hidden layer. Bias nodes are important to train a network, as they allow for
the activations of neurons to train more specifically to their problems. As an
oversimplified example, if our prediction is always off by 4, we can add a bias of -4
to remove this bias. Our bias nodes allow for this, and the training of the weights
dictates the amount of bias that is used.
The biases are given as a set of weights, meaning that it needs to be the same size as
the layer the bias is attaching to:
hidden_b=np.zeros((100,), dtype=np.float32),
[ 256 ]