import numpy
import scipy.special
import matplotlib.pyplot
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5),
(self.hnodes, self.inodes))
self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5),
(self.onodes, self.hnodes))
self.lr = learningrate
self.activation_function = lambda x: scipy.special.expit(x)
pass
def train(self, inputs_list, targets_list):
inputs = numpy.array(inputs_list, ndmin=2).T
targets = numpy.array(targets_list, ndmin=2).T
hidden_inputs = numpy.dot(self.wih, inputs)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = numpy.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
ouput_errors = (target - actual)
hidden_errors = numpy.dot(self.who.T, outputs_errors)
layers
self.who += self.lr * numpy.dot((outputs_errors *
final_outputs * (1.0 - final_outputs)),
numpy.transpose(hidden_outputs))
layers
self.wih += self.lr * numpy.dot((hidden_errors *
hidden_outputs * (1.0 - hidden_outputs)),
numpy.transpose(inputs))
pass
def query(self, inouts_list):
inputs = numpy.array(inputs_list, ndmin=2).T
hidden_inputs = numpy.dot(self.wih, inputs)
hidden_outputs = self.ativation_function(hidden_inputs)
final_inputs = numpy.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
return final_outputs
input_nodes = 784
hidden_nodes = 200
output_nodes = 10
learning_rate = 1.0
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes,
learning_rate)
training_data_file = open("mnist_dataset/mnist_train.csv", 'r')
training_data_list = training_data_file.readline()
training_data_file.close()
epochs = 5
for e in range(epochs):
for record in training_data_list:
all_values = record.split(',')
inputs = (numpy.asfarray(all_values[1:] / 255.0 * 0.99) + 0.01151)
targets = numpy.zeros(output_nodes) + 0.01
targets[int(all_values[0])] = 0.99
n.train(inputs, targets)
pass
pass
test_data_file = open("mnist_dataset/mnist_test.csv", 'r')
test_data_list = test_data_file.readline()
test_data_file.close()
for record in test_data_list:
all_values = record.split(',')
correct_label = int(all_values[0])
inputs = (numpy.asfarray(all_values[1:] / 255.0 * 0.99) + 1.0)
outputs = n.query(inputs)
label = numpy.argmax(outputs)
if (label == correct_label):
scorecard.append(1)
else:
scorecard.append(0)
pass
pass
scorecard_array = numpy.asarray(scorecard)
print("performmance = ", secordcard_array.sum()/scorecard_array.size)
改好了
import numpy
import scipy.special
import matplotlib.pyplot
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5),
(self.hnodes, self.inodes))
self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5),
(self.onodes, self.hnodes))
self.lr = learningrate
self.activation_function = lambda x: scipy.special.expit(x)
pass
def train(self, inputs_list, targets_list):
inputs = numpy.array(inputs_list, ndmin=2).T
targets = numpy.array(targets_list, ndmin=2).T
hidden_inputs = numpy.dot(self.wih, inputs)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = numpy.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
ouput_errors = (target - actual)
hidden_errors = numpy.dot(self.who.T, outputs_errors)
layers
self.who += self.lr * numpy.dot((outputs_errors *
final_outputs * (1.0 - final_outputs)),
numpy.transpose(hidden_outputs))
layers
self.wih += self.lr * numpy.dot((hidden_errors *
hidden_outputs * (1.0 - hidden_outputs)),
numpy.transpose(inputs))
pass
def query(self, inouts_list):
inputs = numpy.array(inputs_list, ndmin=2).T
hidden_inputs = numpy.dot(self.wih, inputs)
hidden_outputs = self.ativation_function(hidden_inputs)
final_inputs = numpy.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
return final_outputs
def neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate):
return 0
input_nodes = 784
hidden_nodes = 200
output_nodes = 10
learning_rate = 1.0
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes,
learning_rate)
training_data_file = open("mnist_dataset/mnist_train.csv", 'r')
training_data_list = training_data_file.readline()
training_data_file.close()
epochs = 5
for e in range(epochs):
for record in training_data_list:
all_values = record.split(',')
inputs = (numpy.asfarray(all_values[1:] / 255.0 * 0.99) + 0.01151)
targets = numpy.zeros(output_nodes) + 0.01
targets[int(all_values[0])] = 0.99
n.train(inputs, targets)
pass
pass
test_data_file = open("mnist_dataset/mnist_test.csv", 'r')
test_data_list = test_data_file.readline()
test_data_file.close()
for record in test_data_list:
all_values = record.split(',')
correct_label = int(all_values[0])
inputs = (numpy.asfarray(all_values[1:] / 255.0 * 0.99) + 1.0)
outputs = n.query(inputs)
label = numpy.argmax(outputs)
if (label == correct_label):
scorecard.append(1)
else:
scorecard.append(0)
pass
pass
scorecard_array = numpy.asarray(scorecard)
print("performmance = ", secordcard_array.sum()/scorecard_array.size)