def forward(network, x):
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = c.np.dot(x, W1) + b1
z1 = sig.sigmoid(a1)
a2 = c.np.dot(z1, W2) + b2
z2 = sig.sigmoid(a2)
a3 = c.np.dot(z2, W3) + b3
Y = identity_function(a3)
return Y
def predict(network, x):
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = c.np.dot(x, W1) + b1
z1 = sig.sigmoid(a1)
a2 = c.np.dot(z1, W2) + b2
z2 = sig.sigmoid(a2)
a3 = c.np.dot(z2, W3) + b3
y = soft.softmax(a3)
return y
def predict(self, x):
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
a1 = c.np.dot(x, W1) + b1
z1 = sig.sigmoid(a1)
a2 = c.np.dot(z1, W2) + b2
y = softmax(a2)
return y
from lib import common as c
from lib import sigmoid as sig
X = c.np.array([1.0, 0.5])
W1 = c.np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
B1 = c.np.array([0.1, 0.2, 0.3])
print(X.shape)
print(W1.shape)
print(B1.shape)
A1 = c.np.dot(X, W1) + B1
print(A1)
Z1 = sig.sigmoid(A1)
W2 = c.np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
B2 = c.np.array([0.1, 0.2])
print(Z1.shape)
print(W2.shape)
print(B2.shape)
A2 = c.np.dot(Z1, W2) + B2
print(A2)
Z2 = sig.sigmoid(A2)
print(Z2)
def identity_function(x):
return x