def FeedForward(network, input):
"""
Arguments:
---------
network : a NeuralNetwork instance
input : an Input instance
Returns:
--------
Nothing
Description:
-----------
This function propagates the inputs through the network. That is,
it modifies the *raw_value* and *transformed_value* attributes of the
nodes in the network, starting from the input nodes.
Notes:
-----
The *input* arguments is an instance of Input, and contains just one
attribute, *values*, which is a list of pixel values. The list is the
same length as the number of input nodes in the network.
i.e: len(input.values) == len(network.inputs)
This is a distributed input encoding (see lecture notes 7 for more
informations on encoding)
In particular, you should initialize the input nodes using these input
values:
network.inputs[i].raw_value = input.values[i]
"""
network.CheckComplete()
# 1) Assign input values to input nodes
for i in range(0,len(input.values)):
network.inputs[i].raw_value = input.values[i]
network.inputs[i].transformed_value = input.values[i]
# 2) Propagates to hidden layer
for node in network.hidden_nodes:
node.raw_value = NeuralNetwork.ComputeRawValue(node)
node.transformed_value = NeuralNetwork.Sigmoid(node.raw_value)
# 3) Propagates to the output layer
for node in network.outputs:
node.raw_value = NeuralNetwork.ComputeRawValue(node)
node.transformed_value = NeuralNetwork.Sigmoid(node.raw_value)
pass
def propagate_forward(nodes):
for node in nodes:
node.raw_value = NeuralNetwork.ComputeRawValue(node)
node.transformed_value = NeuralNetwork.Sigmoid(node.raw_value)
