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SoloSynth1SoloSynth1
authoredSep 22, 2017
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‎main.py

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import numpy as np
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import matplotlib.pyplot as plt
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# import scipy.special as sp
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# scipy is broken in Windows PyCharm environment. Use np.exp instead.
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def sigmoid(x):
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return 1/(1+np.exp(-x))
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class NeuralNetwork:
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def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
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# three layers
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self.inodes = input_nodes
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self.hnodes = hidden_nodes
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self.onodes = output_nodes
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# learing rate
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self.lr = learning_rate
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# weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
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self.wih = np.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
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self.who = np.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))
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# activation function - sigmoid
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self.activation_function = lambda x: sigmoid(x)
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def train(self, inputs_list, targets_list):
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# same as query()
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inputs = np.array(inputs_list, ndmin=2).T
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hidden_inputs = np.dot(self.wih, inputs)
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hidden_outputs = self.activation_function(hidden_inputs)
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final_inputs = np.dot(self.who, hidden_outputs)
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final_outputs = self.activation_function(final_inputs)
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targets = np.array(targets_list, ndmin=2).T
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# error is the (target - actual)
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output_errors = targets - final_outputs
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# hidden layer error is the output_errors, split by weights, recombined at hidden nodes
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hidden_errors = np.dot(self.who.T, output_errors)
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# update the weights for the links between the hidden and output layers
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self.who += self.lr * np.dot((output_errors * final_outputs * (1.0 - final_outputs)), hidden_outputs.T)
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# update the weights for the links between the input and hidden layers
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self.wih += self.lr * np.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), inputs.T)
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def query(self, inputs_list):
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inputs = np.array(inputs_list, ndmin=2).T
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hidden_inputs = np.dot(self.wih, inputs)
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hidden_outputs = self.activation_function(hidden_inputs)
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final_inputs = np.dot(self.who, hidden_outputs)
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final_outputs = self.activation_function(final_inputs)
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return final_outputs
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# input = 28 x 28 pixel input = 784 nodes
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inodes = 784
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# hidden nodes = 100 (random)
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hnodes = 100
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# output = probability of 0 to 9 - 10 nodes
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onodes = 10
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# learning rate
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learning_rate = 0.3
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# read .csv
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data = open('mnist_train.csv', 'r')
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data_list = data.readlines()
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data.close()
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# initialize the neural network
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n = NeuralNetwork(inodes, hnodes, onodes, learning_rate)
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# train the network using the .csv data
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for item in data_list:
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all_values = item.split(',')
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scaled_input = (np.asfarray(all_values[1:])/255.0 * 0.99) + 0.01
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targets = np.zeros(onodes) + 0.01
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targets[int(all_values[0])] = 0.99
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n.train(scaled_input, targets)
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# load test data
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test_data = open('mnist_test.csv', 'r')
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test_data_list = test_data.readlines()
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test_data.close()
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# choose a random MNIST data-set
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query_input = test_data_list[np.random.randint(len(test_data_list))].split(',')
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scaled_query_input = (np.asfarray(query_input[1:])/255.0 * 0.99) + 0.01
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# show the digit
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print('Target digit: %s'%(query_input[0]))
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# show the output
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print n.query(scaled_query_input)
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# image_array = np.asfarray(scaled_query_input).reshape((28, 28))
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# plt.imshow(image_array, cmap='binary', interpolation='None')
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# plt.show()

‎mnist_test.csv

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‎mnist_test_small.csv

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5+
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6+
1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,77,254,107,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,227,254,254,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,81,254,254,165,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,203,254,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,53,254,254,250,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,134,254,254,180,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,196,254,248,48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,58,254,254,237,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,111,254,254,132,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,163,254,238,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,60,252,254,223,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,79,254,254,154,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,163,254,238,53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,252,254,210,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,86,254,254,131,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,105,254,234,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,175,254,204,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,211,254,196,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,158,254,160,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,157,107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
7+
4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,192,134,32,0,0,0,0,0,0,0,0,15,77,5,0,0,0,0,0,0,0,0,0,0,0,0,17,235,250,169,0,0,0,0,0,0,0,0,15,220,241,37,0,0,0,0,0,0,0,0,0,0,0,20,189,253,147,0,0,0,0,0,0,0,0,0,139,253,100,0,0,0,0,0,0,0,0,0,0,0,0,70,253,253,21,0,0,0,0,0,0,0,0,43,254,173,13,0,0,0,0,0,0,0,0,0,0,0,22,153,253,96,0,0,0,0,0,0,0,0,43,231,254,92,0,0,0,0,0,0,0,0,0,0,0,0,163,255,204,11,0,0,0,0,0,0,0,0,104,254,158,0,0,0,0,0,0,0,0,0,0,0,0,0,162,253,178,5,0,0,0,0,0,0,9,131,237,253,0,0,0,0,0,0,0,0,0,0,0,0,0,0,162,253,253,191,175,70,70,70,70,133,197,253,253,169,0,0,0,0,0,0,0,0,0,0,0,0,0,0,51,228,253,253,254,253,253,253,253,254,253,253,219,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,65,137,254,232,137,137,137,44,253,253,161,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,34,254,206,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,160,253,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,85,254,241,50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,158,254,165,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,231,244,50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,104,254,232,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,208,253,157,0,13,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,208,253,154,91,204,161,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,208,253,254,253,154,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,61,190,128,23,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
8+
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9+
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10+
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‎mnist_train_small.csv

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