我在python中编写了一个神经网络来解决mnist任务。 但错误率在一个时代之后变化很小（逗号后的第6位）并且网络在10000个时期之后没有多少学习... 你能帮我解决我做错了什么以及如何改进我的代码来解决mnist吗？ 我将学习率eta设为0.05。
import numpy as np
import pickle
import time
class FeedForwardNetwork():
def __init__(self, input_dim, hidden_dim, output_dim):
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.input_layer = np.array([])
self.hidden_layer = np.array([])
self.output_layer = np.array([])
self.weights_input_hidden = (2 * np.random.random((input_dim, hidden_dim)) - 1)/1000
self.weights_hidden_output = (2* np.random.random((hidden_dim, output_dim)) - 1)/1000
self.validation_data = np.array([])
self.validation_data_solution = np.array([])
def _tanh(self, x, deriv=False):
if not deriv:
return np.tanh(x)
return 1-np.tanh(x)**2
def _softmax(self, x):
return np.exp(x) / np.sum(np.exp(x), axis=0)
def set_training_data(self, training_data_input, training_data_target):
"""Splits the data up into training and validation data with a ratio of 0.75/0.25 and sets the data for training."""
if len(training_data_input) != len(training_data_target):
raise Exception("Number of training examples and training targets does not match!")
len_training_data = int((len(training_data_input)/100*75)//1)
self.input_layer = training_data_input[:len_training_data]
self.output_layer = training_data_target[:len_training_data]
self.validation_data = np.array([training_data_input[len_training_data:]])
self.validation_data_solution = np.array([training_data_target[len_training_data:]])
def save(self, filename):
"""Saves the weights into a pickle file."""
with open(filename, "wb") as network_file:
pickle.dump(self.weights_input_hidden, network_file)
pickle.dump(self.weights_hidden_output, network_file)
def load(self, filename):
"""Loads network weights from a pickle file."""
with open(filename, "rb") as network_file:
weights_input_hidden = pickle.load(network_file)
weights_hidden_output = pickle.load(network_file)
if len(weights_input_hidden) != len(self.weights_input_hidden):
raise Exception("File contains weights that does not match the current networks size!")
if len(weights_hidden_output) != len(self.weights_hidden_output):
raise Exception("File contains weights that does not match the current networks size!")
self.weights_input_hidden = weights_input_hidden
self.weights_hidden_output = weights_hidden_output
def measure_error(self, input_data, output_data):
return 1/2 * np.sum((output_data - self.activate(input_data))**2)
def forward_propagate(self, input_data):
"""Proceds the input data from input neurons up to output neurons and returns the output layer"""
input_layer = input_data
self.hidden_layer = self.__tanh(np.dot(input_layer, self.weights_input_hidden))
output_layer = self.__tanh(np.dot(self.hidden_layer, self.weights_hidden_output))
return output_layer
def activate(self, input_data):
"""Sends the given input through the net and returns the net's prediction."""
return self.forward_propagate(input_data)
def back_propagate(self, input_data, output_data, eta):
"""Calculates the difference between target output and output and adjust the weights to fit the target output better.
The parameter eta is the learning rate."""
num_of_samples = len(input_data)
output_layer = self.forward_propagate(input_data)
output_layer_error = output_data - output_layer
output_layer_delta = output_layer_error * self.__tanh(output_layer, deriv=True)
#How much did each hidden neuron contribute to the output error?
#Multiplys delta term with weights
hidden_layer_error = output_layer_delta.dot(self.weights_hidden_output.T)
#If the prediction is good, the second term will be small and the change will be small
#Ex: target: 1 -> Slope will be 1 so the second term will be big
hidden_layer_delta = hidden_layer_error * self.__tanh(self.hidden_layer, deriv=True)
#The both lines return a matrix. A row stands for all weights connected to one neuron.
#E.g. [1, 2, 3] -> Weights to Neuron A
# [4, 5, 6] -> Weights to Neuron B
hidden_weights_change = self.input_layer.T.dot(hidden_layer_delta)/num_of_samples
output_weights_change = self.hidden_layer.T.dot(output_layer_delta)/num_of_samples
self.weights_hidden_output += (output_weights_change * eta) / num_of_samples
self.weights_input_hidden += (hidden_weights_change * eta) / num_of_samples
def batch_train(self, epochs, eta, patience=10):
"""Trains the network in batch mode that means the weigts are updated after showing all training examples.
Eta is the learning rate and patience is the number of epochs that the validation error is allowed to increase before aborting."""
validation_error = self.measure_error(self.validation_data, self.validation_data_solution)
for epoch in range(epochs):
self.back_propagate(self.input_layer, self.output_layer, eta)
validation_error_new = self.measure_error(self.validation_data, self.validation_data_solution)
if validation_error_new < validation_error:
validation_error = validation_error_new
else:
patience -= 1
if patience == 0:
print("Abort Training. Overfitting has started! Epoch: {0}. Error: {1}".format(epoch, validation_error_new))
return
print("Epoch: {0}, Error: {1}".format(epoch, validation_error))
self.save("Network_Mnist.net")
谢谢！
Epoch：1813，错误：7499.944371111551 时代：1814年，错误：7499.944368765047
答案 0 :(得分：0)
我猜你可能想要添加一个带有交叉熵错误的softmax图层。 当输入为负时，Tanh将输出负值，显然不是输出层所需的值，因为概率应在[0,1]范围内。
This是我实施的NN玩具前馈，可能对您有所帮助。