其他
DL笔记：用 Python 实现梯度下降的算法
回顾上回讲的梯度下降算法，想实现梯度下降，需要不断更新 w：
具体步骤如下：
代码实现
初始化权重变化率为 0
del_w = np.zeros(weights.shape)正向传播计算
output = sigmoid(np.dot(x, weights))计算输出单元的 error term
error = y - output
error_term = error * output * (1-output)更新权重变化率
del_w += error_term * x更新权重
weights += learnrate * del_w / n_records重复 epochs
for e in range(epochs):
del_w = np.zeros(weights.shape)
for x, y in zip(features.values, targets):
# Loop through all records, x is the input, y is the target
# Calculate the output
output = sigmoid(np.dot(x, weights))
# Calculate the error
error = y - output
# Calculate the error term
error_term = error * output * (1-output)
# Calculate the change in weights for this sample
# and add it to the total weight change
del_w += error_term * x
# Update weights using the learning rate and the average change in weights
weights += learnrate * del_w / n_records完整代码：
import numpy as np
from data_prep import features, targets, features_test, targets_test
# Defining the sigmoid function for activations
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# reserve seed
np.random.seed(42)
n_records, n_features = features.shape
last_loss = None
# Initialize weights
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
# Neural Network hyperparameters
epochs = 1000
learnrate = 0.5
for e in range(epochs):
del_w = np.zeros(weights.shape)
for x, y in zip(features.values, targets):
# Loop through all records, x is the input, y is the target
# Calculate the output
output = sigmoid(np.dot(x, weights))
# Calculate the error
error = y - output
# Calculate the error term
error_term = error * output * (1-output)
# Calculate the change in weights for this sample
# and add it to the total weight change
del_w += error_term * x
# Update weights using the learning rate and the average change in weights
weights += learnrate * del_w / n_records
# Printing out the mean square error on the training set
if e % (epochs / 10) == 0:
out = sigmoid(np.dot(features, weights))
loss = np.mean((out - targets) ** 2)
if last_loss and last_loss < loss:
print("Train loss: ", loss, " WARNING - Loss Increasing")
else:
print("Train loss: ", loss)
last_loss = loss
# Calculate accuracy on test data
test_out = sigmoid(np.dot(features_test, weights))
predictions = test_out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))