DeepLearning 笔记:用 Python 实现反向传播算法
DeepLearning笔记:Backpropagation 反向传播算法
上一篇我们总结了用反向传播算法更新权重的算法:
在 python 中实现如下:
import numpy as np
from data_prep import features, targets, features_test, targets_test
np.random.seed(21)
def sigmoid(x):
"""
Calculate sigmoid
"""
return 1 / (1 + np.exp(-x))
# Hyperparameters
n_hidden = 2 # number of hidden units
epochs = 900
learnrate = 0.005
n_records, n_features = features.shape
last_loss = None
# Initialize weights
weights_input_hidden = np.random.normal(scale=1 / n_features ** .5,
size=(n_features, n_hidden))
weights_hidden_output = np.random.normal(scale=1 / n_features ** .5,
size=n_hidden)
for e in range(epochs):
del_w_input_hidden = np.zeros(weights_input_hidden.shape)
del_w_hidden_output = np.zeros(weights_hidden_output.shape)
for x, y in zip(features.values, targets):
## Forward pass ##
# Calculate the output
hidden_input = np.dot(x, weights_input_hidden) # x·w
hidden_output = sigmoid(hidden_input)
output = sigmoid(np.dot(hidden_output, weights_hidden_output))
## Backward pass ##
# Calculate the network's prediction error
error = y - output
# Calculate error term for the output unit
output_error_term = error * output * (1 - output)
## propagate errors to hidden layer
# Calculate the hidden layer's contribution to the error
hidden_error = np.dot(output_error_term, weights_hidden_output)
# Calculate the error term for the hidden layer
hidden_error_term = hidden_error * hidden_output * (1 - hidden_output)
# Update the change in weights
del_w_hidden_output += output_error_term * hidden_output
del_w_input_hidden += hidden_error_term * x[:,None] # x.T
# Update weights
weights_input_hidden += learnrate * del_w_input_hidden / n_records
weights_hidden_output += learnrate * del_w_hidden_output / n_records
# Printing out the mean square error on the training set
if e % (epochs / 10) == 0:
hidden_output = sigmoid(np.dot(x, weights_input_hidden))
out = sigmoid(np.dot(hidden_output,
weights_hidden_output))
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
hidden = sigmoid(np.dot(features_test, weights_input_hidden))
out = sigmoid(np.dot(hidden, weights_hidden_output))
predictions = out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))
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@kidult00 . Thanks for Upvoting !