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classify.py
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from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import random
image_size = 28 # width and length
no_of_different_labels = 10 # i.e. 0, 1, 2, 3, ..., 9
image_pixels = image_size * image_size
train_data = np.loadtxt( "mnist_train.csv",
delimiter=",")
test_data = np.loadtxt( "mnist_test.csv",
delimiter=",")
fac = 0.99 / 255 # all this from this website :
train_imgs = np.asfarray(train_data[:, 1:]) * fac + 0.01 # https://www.python-course.eu/
test_imgs = np.asfarray(test_data[:, 1:]) * fac + 0.01 # neural_network_mnist.php
train_labels = np.asfarray(train_data[:, :1])
test_labels = np.asfarray(test_data[:, :1])
my_train_labels = np.asfarray(train_data[:, 0])
my_test_labels = np.asfarray(test_data[:, 0])
lr = np.arange(no_of_different_labels)
## transform labels into one hot representation
train_labels_one_hot = (lr==train_labels).astype(np.float)
test_labels_one_hot = (lr==test_labels).astype(np.float)
## we don't want zeroes and ones in the labels neither:
train_labels_one_hot[train_labels_one_hot==0] = 0.01
train_labels_one_hot[train_labels_one_hot==1] = 0.99
test_labels_one_hot[test_labels_one_hot==0] = 0.01
test_labels_one_hot[test_labels_one_hot==1] = 0.99
@np.vectorize
def sigmoid(x):
return 1 / (1 + np.e ** -x)
activation_function = sigmoid
from scipy.stats import truncnorm
def truncated_normal(mean=0, sd=1, low=0, upp=10):
return truncnorm((low - mean) / sd,
(upp - mean) / sd,
loc=mean,
scale=sd)
class NeuralNetwork:
def __init__(self,
no_of_in_nodes,
no_of_out_nodes,
no_of_hidden_nodes,
learning_rate):
self.no_of_in_nodes = no_of_in_nodes
self.no_of_out_nodes = no_of_out_nodes
self.no_of_hidden_nodes = no_of_hidden_nodes
self.learning_rate = learning_rate
self.create_weight_matrices()
def create_weight_matrices(self):
"""
A method to initialize the weight
matrices of the neural network
"""
rad = 1 / np.sqrt(self.no_of_in_nodes)
X = truncated_normal(mean=0,
sd=1,
low=-rad,
upp=rad)
self.wih = X.rvs((self.no_of_hidden_nodes,
self.no_of_in_nodes))
rad = 1 / np.sqrt(self.no_of_hidden_nodes)
X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad)
self.who = X.rvs((self.no_of_out_nodes,
self.no_of_hidden_nodes))
def train(self, input_vector, target_vector):
"""
input_vector and target_vector can
be tuple, list or ndarray
"""
input_vector = np.array(input_vector, ndmin=2).T
target_vector = np.array(target_vector, ndmin=2).T
output_vector1 = np.dot(self.wih,
input_vector)
output_hidden = activation_function(output_vector1)
output_vector2 = np.dot(self.who,
output_hidden)
output_network = activation_function(output_vector2)
output_errors = target_vector - output_network
# update the weights:
tmp = output_errors * output_network \
* (1.0 - output_network)
tmp = self.learning_rate * np.dot(tmp,
output_hidden.T)
self.who += tmp
# calculate hidden errors:
hidden_errors = np.dot(self.who.T,
output_errors)
# update the weights:
tmp = hidden_errors * output_hidden * \
(1.0 - output_hidden)
self.wih += self.learning_rate \
* np.dot(tmp, input_vector.T)
def run(self, input_vector):
# input_vector can be tuple, list or ndarray
input_vector = np.array(input_vector, ndmin=2).T
output_vector = np.dot(self.wih,
input_vector)
output_vector = activation_function(output_vector)
output_vector = np.dot(self.who,
output_vector)
output_vector = activation_function(output_vector)
return output_vector
def confusion_matrix(self, data_array, labels):
cm = np.zeros((10, 10), int)
for i in range(len(data_array)):
res = self.run(data_array[i])
res_max = res.argmax()
target = labels[i][0]
cm[res_max, int(target)] += 1
return cm
def precision(self, label, confusion_matrix):
col = confusion_matrix[:, label]
return confusion_matrix[label, label] / col.sum()
def recall(self, label, confusion_matrix):
row = confusion_matrix[label, :]
return confusion_matrix[label, label] / row.sum()
def evaluate(self, data, labels):
corrects, wrongs = 0, 0
for i in range(len(data)):
res = self.run(data[i])
res_max = res.argmax()
if res_max == labels[i]:
corrects += 1
else:
wrongs += 1
return corrects, wrongs
ANN = NeuralNetwork(no_of_in_nodes = image_pixels,
no_of_out_nodes = 10,
no_of_hidden_nodes = 100,
learning_rate = 0.1)
for i in range(len(train_imgs)):
ANN.train(train_imgs[i], train_labels_one_hot[i])
#for i in range(20):
# res = ANN.run(test_imgs[i])
# print(test_labels[i], np.argmax(res), np.max(res))
def get_grayscale(image_path):
rgb=plt.imread(image_path)
gs=np.dot(rgb[...,:3],[0.299,0.587,0.114]) # convert rgb to greyscale
gs=gs*fac+0.01
return 1-gs # for some strange reason, gs was inverted : white ka black;
# black ka white. hence 1-gs.
threshold=0.55
def sliding_window(image_path):
#im = Image.open("image25.jpeg")
#img=im.convert(mode='L')
#img=img.resize((252,252))
gs=get_grayscale(image_path)
ans=[[0]*9]*9
for i in range(1,10):
for j in range(1,10):
one_digits_array=gs[(i-1)*28:i*28,(j-1)*28:j*28]
abc=one_digits_array.reshape(784,1)
res=ANN.run(abc[:,0]*fac+0.01)
if (np.max(res)<threshold):
ans[i-1][j-1]=0
else:
ans[i-1][j-1]=np.argmax(res)
return ans
print sliding_window("image25.jpeg")
#print ans
#this is the 28*28 array for the seven at the (0,4) position
arrfor7=array([[0.51635294, 0.52023529, 0.51635294, 0.51635294, 0.51635294,
0.51635294, 0.51247059, 0.52411765, 0.51635294, 0.51635294,
0.51635294, 0.51635294, 0.51635294, 0.51635294, 0.51635294,
0.51635294, 0.51635294, 0.51635294, 0.51635294, 0.51635294,
0.51635294, 0.51635294, 0.51635294, 0.51635294, 0.51635294,
0.51635294, 0.52411765, 0.50470588],
[0.51635294, 0.00388235, 0. , 0.00776471, 0. ,
0.00776471, 0. , 0. , 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0. ,
0.00776471, 0. , 0.52411765],
[0.51635294, 0. , 0.01941176, 0. , 0.00776471,
0.00776471, 0.00776471, 0.01164706, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00776471,
0. , 0.01552941, 0.50082353],
[0.51635294, 0.00776471, 0. , 0. , 0. ,
0.00388235, 0.01164706, 0. , 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0. ,
0.00776471, 0. , 0.52411765],
[0.52411765, 0. , 0.00776471, 0.02717647, 0. ,
0.01164706, 0. , 0.01164706, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00776471,
0.01552941, 0. , 0.52023529],
[0.52023529, 0.01552941, 0.01164706, 0. , 0.00388235,
0.01552941, 0. , 0.00776471, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00776471, 0.52023529],
[0.50082353, 0. , 0.01164706, 0.01941176, 0.00776471,
0. , 0.01164706, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00776471,
0. , 0.00776471, 0.51247059],
[0.528 , 0.00388235, 0. , 0.00776471, 0. ,
0.00388235, 0. , 0.00776471, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0. ,
0.00388235, 0. , 0.53188235],
[0.52411765, 0. , 0. , 0.01164706, 0. ,
0.01164706, 0. , 0.00776471, 0. , 0.01164706,
0. , 0.01164706, 0. , 0.03105882, 0.05823529,
0.04270588, 0.41541176, 0.46976471, 0.45811765, 0.132 ,
0.03882353, 0. , 0.01164706, 0.00388235, 0.00388235,
0.00388235, 0. , 0.52411765],
[0.50858824, 0.01552941, 0.00776471, 0.02329412, 0.10870588,
0.10870588, 0.10482353, 0.10870588, 0.11258824, 0.10870588,
0.10482353, 0.52411765, 0.56682353, 0.83082353, 0.97447059,
0.99 , 0.98223529, 0.98611765, 0.99 , 0.97058824,
0.64058824, 0.01552941, 0. , 0. , 0.00776471,
0.00776471, 0.00776471, 0.51247059],
[0.528 , 0. , 0.43482353, 0.69882353, 0.99 ,
0.98611765, 0.98611765, 0.98223529, 0.98611765, 0.98223529,
0.99 , 0.97447059, 0.99 , 0.98223529, 0.99 ,
0.97835294, 0.98611765, 0.98611765, 0.99 , 0.99 ,
0.98223529, 0.462 , 0.00776471, 0.00388235, 0.00776471,
0. , 0. , 0.52023529],
[0.51247059, 0.00388235, 0.99 , 0.99 , 0.97835294,
0.98223529, 0.99 , 0.99 , 0.98223529, 0.98611765,
0.99 , 0.98223529, 0.98611765, 0.99 , 0.858 ,
0.57070588, 0.56294118, 0.10482353, 0.16694118, 0.86964706,
0.97058824, 0.99 , 0.00388235, 0.00388235, 0. ,
0.00388235, 0.01164706, 0.528 ],
[0.52411765, 0. , 0.264 , 0.90458824, 0.45035294,
0.45423529, 0.44258824, 0.49305882, 0.98611765, 0.85411765,
0.45035294, 0.47364706, 0.20188235, 0.02717647, 0.04658824,
0. , 0.00776471, 0. , 0. , 0.66388235,
0.99 , 0.98223529, 0.00388235, 0. , 0.00776471,
0.00776471, 0. , 0.51247059],
[0.51247059, 0.00388235, 0. , 0.00388235, 0. ,
0.00776471, 0.01552941, 0.00776471, 0.00388235, 0. ,
0. , 0. , 0. , 0.00388235, 0.02329412,
0.00388235, 0. , 0.01941176, 0.06211765, 0.83470588,
0.99 , 0.95117647, 0.00388235, 0.01164706, 0.00388235,
0. , 0.00388235, 0.51635294],
[0.50858824, 0.01164706, 0.00388235, 0. , 0.01552941,
0. , 0. , 0. , 0. , 0.00388235,
0.02329412, 0.00776471, 0.01552941, 0.00776471, 0. ,
0. , 0.01552941, 0. , 0.17470588, 0.99 ,
0.98223529, 0.45811765, 0. , 0.00388235, 0. ,
0.00776471, 0.00776471, 0.51247059],
[0.52411765, 0. , 0.00776471, 0.00388235, 0. ,
0.01941176, 0. , 0.00776471, 0.00776471, 0.00776471,
0. , 0. , 0.00388235, 0.00776471, 0. ,
0.00776471, 0.00388235, 0.02717647, 0.88129412, 0.98611765,
0.99 , 0.12811765, 0.00776471, 0.00388235, 0.01164706,
0.00388235, 0. , 0.52023529],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00776471, 0. ,
0.00776471, 0.00388235, 0. , 0.01552941, 0. ,
0.00776471, 0. , 0.05047059, 0.99 , 0.99 ,
0.60952941, 0.02329412, 0. , 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.52411765],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0. , 0.01164706,
0.00388235, 0.01164706, 0. , 0. , 0.00388235,
0.00388235, 0. , 0.05435294, 0.97835294, 0.98223529,
0.56294118, 0.00388235, 0.00388235, 0.00776471, 0. ,
0.00388235, 0. , 0.50858824],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0. , 0. , 0.00388235, 0.01941176, 0.00388235,
0.00776471, 0. , 0.50858824, 0.99 , 0.97447059,
0.07764706, 0.01164706, 0. , 0.01164706, 0.00388235,
0.00388235, 0.00776471, 0.53576471],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.01552941,
0. , 0.01164706, 0.02329412, 0. , 0. ,
0.00776471, 0.05435294, 0.97835294, 0.99 , 0.33776471,
0.00776471, 0. , 0.02717647, 0. , 0.00776471,
0.00776471, 0. , 0.50858824],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0. ,
0.01552941, 0. , 0.00776471, 0.01164706, 0.00388235,
0.00776471, 0.49694118, 0.99 , 0.98223529, 0.16694118,
0.01164706, 0. , 0. , 0.00776471, 0. ,
0.01941176, 0.00388235, 0.51635294],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0. , 0.00776471, 0.00388235, 0.01164706,
0.198 , 0.96670588, 0.99 , 0.81141176, 0.02717647,
0.00388235, 0. , 0. , 0.00776471, 0.00776471,
0.00776471, 0.00388235, 0.52023529],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0.01552941,
0. , 0.02717647, 0. , 0. , 0.02329412,
0.24847059, 0.97835294, 0.99 , 0.29894118, 0. ,
0. , 0.01552941, 0.00388235, 0. , 0. ,
0.01164706, 0. , 0.51247059],
[0.51635294, 0.00388235, 0.00388235, 0.00388235, 0.00388235,
0.00388235, 0.00388235, 0.00388235, 0.00388235, 0. ,
0.00776471, 0. , 0.01164706, 0.00776471, 0. ,
0.78035294, 0.99 , 0.94341176, 0.08541176, 0.00776471,
0.00388235, 0. , 0.00776471, 0.00388235, 0.01164706,
0. , 0.00776471, 0.528 ],
[0.51635294, 0. , 0.00776471, 0.00388235, 0.00776471,
0. , 0.01164706, 0. , 0. , 0.01552941,
0.00388235, 0. , 0.02329412, 0. , 0.15917647,
0.924 , 0.98223529, 0.77647059, 0. , 0.01941176,
0. , 0.00388235, 0.00776471, 0. , 0. ,
0.01164706, 0.00388235, 0.52411765],
[0.51635294, 0.00776471, 0. , 0.00776471, 0. ,
0.00388235, 0. , 0.00388235, 0.00776471, 0. ,
0.01164706, 0.00776471, 0. , 0.01552941, 0.28729412,
0.98223529, 0.99 , 0.08929412, 0. , 0. ,
0.01164706, 0.01164706, 0. , 0.00776471, 0.00388235,
0.01552941, 0.00388235, 0.50470588],
[0.51247059, 0. , 0.01164706, 0. , 0.01164706,
0. , 0. , 0.01552941, 0.00776471, 0.01164706,
0. , 0.00776471, 0.00388235, 0.00388235, 0.03105882,
0.82305882, 0.53964706, 0. , 0.01164706, 0.01164706,
0. , 0. , 0. , 0.00776471, 0.00776471,
0. , 0.00388235, 0.52411765],
[0.51635294, 0.51635294, 0.51635294, 0.51247059, 0.51635294,
0.52023529, 0.52023529, 0.51247059, 0.51247059, 0.51247059,
0.528 , 0.51635294, 0.52023529, 0.51635294, 0.52023529,
0.50082353, 0.51247059, 0.53188235, 0.50858824, 0.52023529,
0.52411765, 0.52411765, 0.52023529, 0.50858824, 0.51247059,
0.53188235, 0.51635294, 0.50858824]])