import numpy as np
from sklearn.datasets import load_digits
from sklearn.preprocessing import  LabelBinarizer
from sklearn.model_selection import  train_test_split
from sklearn.metrics import  classification_report, confusion_matrix
import matplotlib.pyplot as plt

# load data
digits = load_digits()
print digits.images.shape

# show image
plt.imshow(digits.images[0], cmap='gray')
plt.show()

# data
X = digits.data
# label
y = digits.target
print X.shape
print y.shape
print X[:3]
print y[:3]

# define a nerve network, struct: 64-100-10
# define input layer to hidden layer's value metrics
V = np.random.random((64, 100)) * 2 - 1
# define hidden layer to input layer's value metrics
W = np.random.random((100, 10)) * 2 - 1

# data split
X_train, X_test, y_train, y_test = train_test_split(X, y)

# tag binaryzation
labels_train = LabelBinarizer().fit_transform(y_train)
print y_train[:5]
print labels_train[:5]

# active function
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# active function's derivation
def dsigmoid(x):
    return x * (1 - x)

# train model
def train(X, y, steps=10000, lr=0.11):
    global V, W
    for n in range(steps + 1):
        # random get a data
        i = np.random.randint(X.shape[0])
        # get a data
        x = X[i]
        x = np.atleast_2d(x)
        # BP
        # calculate hidden layer's output
        L1 = sigmoid(np.dot(x, V))
        # calculate ouput layer's output
        L2 = sigmoid(np.dot(L1, W))
        # calculate L2_delta, L1_delta
        L2_delta = (y[i] - L2) * dsigmoid(L2)
        L1_delta = L2_delta.dot(W.T) * dsigmoid(L1)
        # update weight
        W += lr * L1.T.dot(L2_delta)
        V += lr * x.T.dot(L1_delta)

        # train 1000 times to get a prediction
        if n % 1000 == 0:
            output = predict(X_test)
            predictions = np.argmax(output, axis=1)
            acc = np.mean(np.equal(predictions, y_test))
            print 'steps: ', n, 'accuracy: ', acc

def predict(x):
    L1 = sigmoid(np.dot(x, V))
    L2 = sigmoid(np.dot(L1, W))
    return L2

train(X_train, labels_train, 30000)

output = predict(X_test)
predictions = np.argmax(output, axis=1)
print classification_report(predictions, y_test)

print confusion_matrix(predictions, y_test)
 

(1797, 8, 8)
(1797, 64)
(1797,)
[[ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.
  15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.
   0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.
   0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]
 [ 0.  0.  0. 12. 13.  5.  0.  0.  0.  0.  0. 11. 16.  9.  0.  0.  0.  0.
   3. 15. 16.  6.  0.  0.  0.  7. 15. 16. 16.  2.  0.  0.  0.  0.  1. 16.
  16.  3.  0.  0.  0.  0.  1. 16. 16.  6.  0.  0.  0.  0.  1. 16. 16.  6.
   0.  0.  0.  0.  0. 11. 16. 10.  0.  0.]
 [ 0.  0.  0.  4. 15. 12.  0.  0.  0.  0.  3. 16. 15. 14.  0.  0.  0.  0.
   8. 13.  8. 16.  0.  0.  0.  0.  1.  6. 15. 11.  0.  0.  0.  1.  8. 13.
  15.  1.  0.  0.  0.  9. 16. 16.  5.  0.  0.  0.  0.  3. 13. 16. 16. 11.
   5.  0.  0.  0.  0.  3. 11. 16.  9.  0.]]
[0 1 2]
[9 6 6 4 5]
[[0 0 0 0 0 0 0 0 0 1]
 [0 0 0 0 0 0 1 0 0 0]
 [0 0 0 0 0 0 1 0 0 0]
 [0 0 0 0 1 0 0 0 0 0]
 [0 0 0 0 0 1 0 0 0 0]]
steps:  0 accuracy:  0.08222222222222222
steps:  1000 accuracy:  0.6666666666666666
steps:  2000 accuracy:  0.7555555555555555
steps:  3000 accuracy:  0.7777777777777778
steps:  4000 accuracy:  0.7777777777777778
steps:  5000 accuracy:  0.7977777777777778
steps:  6000 accuracy:  0.8422222222222222
steps:  7000 accuracy:  0.8266666666666667
steps:  8000 accuracy:  0.8422222222222222
steps:  9000 accuracy:  0.8377777777777777
steps:  10000 accuracy:  0.8555555555555555
steps:  11000 accuracy:  0.8555555555555555
steps:  12000 accuracy:  0.8533333333333334
steps:  13000 accuracy:  0.8533333333333334
steps:  14000 accuracy:  0.8644444444444445
steps:  15000 accuracy:  0.9
steps:  16000 accuracy:  0.9333333333333333
steps:  17000 accuracy:  0.9488888888888889
steps:  18000 accuracy:  0.9488888888888889
steps:  19000 accuracy:  0.9555555555555556
steps:  20000 accuracy:  0.96
steps:  21000 accuracy:  0.9555555555555556
steps:  22000 accuracy:  0.9622222222222222
steps:  23000 accuracy:  0.9511111111111111
steps:  24000 accuracy:  0.9488888888888889
steps:  25000 accuracy:  0.9555555555555556
steps:  26000 accuracy:  0.9666666666666667
steps:  27000 accuracy:  0.9622222222222222
steps:  28000 accuracy:  0.9666666666666667
steps:  29000 accuracy:  0.9666666666666667
steps:  30000 accuracy:  0.9533333333333334
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       0.98      0.85      0.91        53
           2       0.98      1.00      0.99        49
           3       0.96      0.96      0.96        47
           4       0.91      1.00      0.95        42
           5       0.98      0.98      0.98        41
           6       0.96      0.98      0.97        50
           7       1.00      0.93      0.96        44
           8       0.84      0.90      0.87        41
           9       0.93      0.95      0.94        43

   micro avg       0.95      0.95      0.95       450
   macro avg       0.95      0.95      0.95       450
weighted avg       0.96      0.95      0.95       450

[[40  0  0  0  0  0  0  0  0  0]
 [ 0 45  1  0  3  0  0  0  4  0]
 [ 0  0 49  0  0  0  0  0  0  0]
 [ 0  0  0 45  0  0  0  0  0  2]
 [ 0  0  0  0 42  0  0  0  0  0]
 [ 0  0  0  0  0 40  1  0  0  0]
 [ 0  0  0  0  0  0 49  0  1  0]
 [ 0  0  0  1  1  0  0 41  1  0]
 [ 0  1  0  1  0  0  1  0 37  1]
 [ 0  0  0  0  0  1  0  0  1 41]]