## 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)