def loss(self, x, t):
x = np.array(x).astype(np.float)
t = np.array(t).astype(np.float)
y = afm.softmax(self.predict(x))
loss = lf1.loss_cross_entropy(y, t)
return loss
def dW(self, x, y, delta=1e-4):
H = np.zeros_like(self.W)
#print('delta sparse matrix check =', H)
gradient_Weight = np.zeros_like(self.W)
for i in range(self.W.shape[0]):
for j in range(self.W.shape[1]):
H[i][j] = delta
W_forward = self.W + H
W_backward = self.W - H
loss_f = lf1.loss_cross_entropy(
afm.softmax(np.dot(x, W_forward)), y)
loss_b = lf1.loss_cross_entropy(
afm.softmax(np.dot(x, W_backward)), y)
gradient = (loss_f - loss_b) / (2 * delta)
#print('current gradient = ', gradient)
H[i][j] = 0
gradient_Weight[i][j] = gradient
#print('gradient_Weight matrix check =', gradient_Weight)
return gradient_Weight
def predict(ntw, x):
W1, W2, W3 = ntw['W1'],ntw['W2'],ntw['W3']
b1, b2, b3 = ntw['b1'],ntw['b2'],ntw['b3']
a1 = np.dot(x, W1) + b1
z1 = afm.sigmoid(a1)
a2 = np.dot(z1, W2) + b2
z2 = afm.sigmoid(a2)
a3 = np.dot(z2, W3) + b3
z3 = afm.softmax(a3)
return z3
def predict(self, x):
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
p1 = afm.sigmoid(np.dot(x, W1) + b1)
p2 = afm.softmax(np.dot(p1, W2) + b2)
return p2
"""
# print(inspect.getsource(nn_1)) # shows all source code. not recommended.
# print(inspect.getsource(np))
print('version 3 test')
sl_input = np.array([0.1,0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.1])
sl = single_layer_3(
initial_input=sl_input,
layers=[3, 4, 3, 2, 6, 4, 10, 4, 3, 5, 2],
output_size=10,
activation_function=afm.softmax)
x = sl.product()[0]
print( (x - np.mean(x)) / np.std(x) ) # z-centralisation
print( (x - np.min(x)) / (np.max(x) - np.min(x))) # min-max normalization.
# it seems, we may not need to add z-centralisation or min-max normalization
print(x)
print(np.sum(afm.softmax(sl_input))) # = 1.0
print('\n')
"""How can we access files, which are not on this directory?
"""
#print(inspect.getsource(os.path))
print(os.path.relpath(
'C:/Users/Azerates/PycharmProjects/ITWILL/SCRATCH_LECT_11', os.getcwd()
)) #..\..\..\PycharmProjects\ITWILL\SCRATCH_LECT_11
files = os.listdir(os.path.join('..', '..', '..','PycharmProjects', 'ITWILL', 'SCRATCH_LECT_11'))
for i in files:
print(i)
"""
azerates20191211a.py
breast-cancer-wisconsin-data.csv