def predict(network, x):
w1, w2, w3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = np.dot(x, w1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, w2) + b2
z2 = sigmoid(a2)
a3 = np.dot(z2, w3) + b3
y = softmax(a3)
return y
def forward(network, x):
W1, W2, W3 = network['W1'], network['W2'], network['W3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z2, W2) + b2
z2 = sigmoid(a2)
a3 = np.dot(z2, W3) + b3
y = identity_function(a3)
return y
def forward(network, x):
w1, w2, w3 = network['w1'], network['w2'], network['w3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = np.dot(x, w1) + b1
z1 = sg.sigmoid(a1)
a2 = np.dot(z1, w2) + b2
z2 = sg.sigmoid(a2)
a3 = np.dot(z2, w3) + b3
y = it.identity_function(a3)
return y
import numpy as np from sigmoid_function import sigmoid X = np.array([1.0, 0.5]) W1 = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]]) B1 = np.array([0.1, 0.2, 0.3]) print(W1.shape) print(X.shape) print(B1.shape) A1 = np.dot(X, W1) + B1 Z1 = sigmoid(A1) print(A1) print(Z1) # second layer W2 = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]]) B2 = np.array([0.1, 0.2]) print(Z1.shape) print(W2.shape) print(B2.shape) A2 = np.dot(Z1, W2) + B2 Z2 = sigmoid(A2) print(A2) print(Z2)
import numpy as np import sigmoid_function as sg x = np.array([1.0, 0.5]) w1 = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]]) b1 = np.array([0.1, 0.2, 0.3]) a1 = np.dot(x, w1) + b1 print(a1) z1 = sg.sigmoid(a1) print(z1) w2 = np.array([[0.1, 0.4], [0.2, 0.5], [0.4, 0.6]]) b2 = np.array([0.1, 0.2]) a2 = np.dot(z1, w2) z2 = sg.sigmoid(a2) print(z2)
import numpy as np from sigmoid_function import sigmoid from identity_function import identity_function # first ---------------------- # definition X = np.array([1.0, 0.5]) W1 = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]]) B1 = np.array([0.1, 0.2, 0.3]) A1 = np.dot(X, W1) + B1 print(A1) Z1 = sigmoid(A1) # activation function print(Z1) # second ---------------------- # definition W2 = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]]) B2 = np.array([0.1, 0.2]) A2 = np.dot(Z1, W2) + B2 print(A2) Z2 = sigmoid(A2) print(Z2) # final # definition W3 = np.array([[0.1, 0.3], [0.2, 0.4]]) B3 = np.array([0.1, 0.2]) A3 = np.dot(Z2, W3) + B3