In [16]:
import numpy as np
import scipy.special
import matplotlib.pyplot
%matplotlib inline
In [17]:
#defining it
class neuralNetwork:
def __init__(self, inputnodes, hiddennodes, outputnodes, learning_rate):
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
self.lr = learning_rate
self.wih = (np.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes, self.inodes)))
self.who = (np.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes)))
self.activation_function = lambda x:scipy.special.expit(x)
pass
def train(self, inputs_list, targets_list):
inputs = np.array(inputs_list, ndmin=2).T
targets = np.array(targets_list,ndmin=2).T
hidden_inputs = np.dot(self.wih,inputs)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
output_errors = targets - final_outputs
hidden_errors = np.dot(self.who.T,output_errors)
self.who += self.lr*np.dot((output_errors*final_outputs*(1-final_outputs)),np.transpose(hidden_outputs))
self.wih += self.lr*np.dot((hidden_errors*hidden_outputs*(1-hidden_outputs)),np.transpose(inputs))
pass
def query(self, inputs_list):
inputs = np.array(inputs_list, ndmin=2).T
hidden_inputs = np.dot(self.wih,inputs)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
return final_outputs
In [18]:
#creating the network
input_nodes = 784
hidden_nodes = 200
output_nodes = 10
learning_rate = 0.1
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
In [19]:
data_file = open("mnist_train.csv","r")
data_list = data_file.readlines()
data_file.close()
In [20]:
for record in data_list:
all_values = record.split(',')
inputs = (np.asfarray(all_values[1:])/255.0 *0.99)+0.01
targets = np.zeros(output_nodes)+0.01
targets[int(all_values[0])] = 0.99
n.train(inputs,targets)
pass
In [ ]:
In [21]:
test_data_file = open("mnist_test.csv","r")
test_data_list = test_data_file.readlines()
test_data_file.close()
In [22]:
all_values1 = test_data_list[0].split(',')
all_values1[0]
Out[22]:
'7'
In [8]:
image_array = np.asfarray(all_values1[1:]).reshape((28,28))
matplotlib.pyplot.imshow(image_array, cmap="Greys",interpolation="None")
Out[8]:
<matplotlib.image.AxesImage at 0x7fe2b7ce5978>
In [9]:
n.query((np.asfarray(all_values1[1:])/255.0 * 0.99) + 0.01)
Out[9]:
array([[ 0.00981296],
[ 0.00291237],
[ 0.01382717],
[ 0.01207517],
[ 0.00125286],
[ 0.01440796],
[ 0.0101734 ],
[ 0.98560708],
[ 0.00882559],
[ 0.00855959]])
In [10]:
len(n.query((np.asfarray(all_values[1:])/255.0*0.99)+0.01))
Out[10]:
10
In [11]:
scorecard = []
for record in test_data_list:
all_values = record.split(',')
correct_label = int(all_values[0])
print(correct_label,"correct label")
inputs = (np.asfarray(all_values[1:])/255.0 *0.99)+0.01
outputs = n.query(inputs)
label = np.argmax(outputs)
print(label,"found answer")
if(label == correct_label):
scorecard.append(1)
else:
scorecard.append(0)
pass
pass
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In [12]:
print(scorecard)
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
In [13]:
sum_array = np.asarray(scorecard)
print("performance = ", sum_array.sum()/sum_array.size)
performance = 0.9609
In [ ]:
Content source: jessicabodosa/digit_trial
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