## read in the digits_train.csv file
training_line = []
for line in islice(f, 1, None):
training_line.append(line.replace('\r\n', '').split(','))
## parse each line in the data, convert expected to binary list
## and normalizes pixel value from 0 to 1
for line in training_line:
line[0] = create_out(int(line[0]))
for i, item in enumerate(line[1:]):
line[i + 1] = float(item) / 255.0
print("done")
## create the network and connect the nodes
n = NeuralNetwork(784, 50, 10)
n.connect_nodes()
print("connected")
print("training...")
## train the network with first 10000 digit values
i = 1
for line in islice(training_line, 10000):
n.forward_prop(line[1:])
n.back_prop(line[0], 1)
print i
i += 1
print("done training")
## test the network on the rest of the digit values
correct = 0
## read in the digits_train.csv file
training_line = []
for line in islice(f, 1, None):
training_line.append(line.replace("\r\n", "").split(","))
## parse each line in the data, convert expected to binary list
## and normalizes pixel value from 0 to 1
for line in training_line:
line[0] = create_out(int(line[0]))
for i, item in enumerate(line[1:]):
line[i + 1] = float(item) / 255.0
print ("done")
## create the network and connect the nodes
n = NeuralNetwork(784, 50, 10)
n.connect_nodes()
print ("connected")
print ("training...")
## train the network with first 10000 digit values
i = 1
for line in islice(training_line, 10000):
n.forward_prop(line[1:])
n.back_prop(line[0], 1)
print i
i += 1
print ("done training")
## test the network on the rest of the digit values
#! /usr/local/bin/python2.7 from n_network import NeuralNetwork, InputNode, HiddenNode, OutputNode n = NeuralNetwork(2,3,1) n.connect_nodes(False) n.forward_prop([1,2]) for o_node in n.output_nodes: print o_node.output n.back_prop([0],10) n.print_net() """example of how the Node sub-classes work without using the class NeuralNetwork""" def build_n_network(): i1 = InputNode(1) i2 = InputNode(2) h3 = HiddenNode(3) h4 = HiddenNode(4) h5 = HiddenNode(5) out1 = OutputNode(6) i1.connect(h3,-3) i1.connect(h4,2) i1.connect(h5,4) i2.connect(h3,2) i2.connect(h4,-3) i2.connect(h5,0.5)
