写出上述步骤的Python代码
时间: 2023-05-31 16:06:47 浏览: 78
以下是一个可能的Python代码实现:
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def initialize_parameters(n_x, n_h, n_y):
np.random.seed(2)
W1 = np.random.randn(n_h, n_x) * 0.01
b1 = np.zeros((n_h, 1))
W2 = np.random.randn(n_y, n_h) * 0.01
b2 = np.zeros((n_y, 1))
parameters = {
"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2
}
return parameters
def forward_propagation(X, parameters):
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
Z1 = np.dot(W1, X) + b1
A1 = np.tanh(Z1)
Z2 = np.dot(W2, A1) + b2
A2 = sigmoid(Z2)
cache = {
"Z1": Z1,
"A1": A1,
"Z2": Z2,
"A2": A2
}
return A2, cache
def compute_cost(A2, Y):
m = Y.shape[1]
cost = (-1/m) * np.sum(Y*np.log(A2) + (1-Y)*np.log(1-A2))
cost = np.squeeze(cost)
return cost
def backward_propagation(parameters, cache, X, Y):
m = X.shape[1]
W2 = parameters["W2"]
A1 = cache["A1"]
A2 = cache["A2"]
dZ2 = A2 - Y
dW2 = (1/m) * np.dot(dZ2, A1.T)
db2 = (1/m) * np.sum(dZ2, axis=1, keepdims=True)
dZ1 = np.dot(W2.T, dZ2) * (1 - np.power(A1, 2))
dW1 = (1/m) * np.dot(dZ1, X.T)
db1 = (1/m) * np.sum(dZ1, axis=1, keepdims=True)
gradients = {
"dW1": dW1,
"db1": db1,
"dW2": dW2,
"db2": db2
}
return gradients
def update_parameters(parameters, gradients, learning_rate):
W1 = parameters["W1"]
b1 = parameters["b1"]
W2 = parameters["W2"]
b2 = parameters["b2"]
dW1 = gradients["dW1"]
db1 = gradients["db1"]
dW2 = gradients["dW2"]
db2 = gradients["db2"]
W1 = W1 - learning_rate * dW1
b1 = b1 - learning_rate * db1
W2 = W2 - learning_rate * dW2
b2 = b2 - learning_rate * db2
parameters = {
"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2
}
return parameters
def nn_model(X, Y, n_h, num_iterations, learning_rate):
n_x = X.shape[0]
n_y = Y.shape[0]
parameters = initialize_parameters(n_x, n_h, n_y)
for i in range(num_iterations):
A2, cache = forward_propagation(X, parameters)
cost = compute_cost(A2, Y)
gradients = backward_propagation(parameters, cache, X, Y)
parameters = update_parameters(parameters, gradients, learning_rate)
if i % 1000 == 0:
print("Cost after iteration {}: {}".format(i, cost))
return parameters
def predict(parameters, X):
A2, cache = forward_propagation(X, parameters)
predictions = np.round(A2)
return predictions
# 测试代码
X = np.array([[0, 0, 1, 1], [0, 1, 0, 1]])
Y = np.array([[0, 1, 1, 0]])
parameters = nn_model(X, Y, n_h=4, num_iterations=10000, learning_rate=1.2)
predictions = predict(parameters, X)
print("Predictions: {}".format(predictions))