脉冲神经网络实现手写数字识别代码具体实现
时间: 2023-11-10 07:20:05 浏览: 45
好的,以下是一个使用PyNN和MNIST数据集实现手写数字识别的示例代码。
首先,我们需要准备MNIST数据集。可以使用`tensorflow`库来下载和加载数据集:
```python
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
train_images = mnist.train.images
train_labels = mnist.train.labels
test_images = mnist.test.images
test_labels = mnist.test.labels
```
接下来,我们需要定义脉冲神经元模型和神经网络结构。这里我们使用IF神经元模型,包括一个输入层、一个隐藏层和一个输出层。同时,我们还需要定义训练参数和训练过程。
```python
import numpy as np
import pyNN.spiNNaker as sim
# 定义神经元模型
cell_params = {
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 2.5,
'tau_syn_I': 2.5,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0,
}
# 定义网络结构
input_size = 784
hidden_size = 256
output_size = 10
input_population = sim.Population(input_size, sim.SpikeSourcePoisson, {'rate': 0.0})
hidden_population = sim.Population(hidden_size, sim.IF_curr_exp, cell_params)
output_population = sim.Population(output_size, sim.IF_curr_exp, cell_params)
input_to_hidden = sim.Projection(input_population, hidden_population, sim.AllToAllConnector())
hidden_to_output = sim.Projection(hidden_population, output_population, sim.AllToAllConnector())
# 定义训练参数
batch_size = 64
num_epochs = 10
learning_rate = 0.1
# 定义训练过程
def train(images, labels):
num_batches = images.shape[0] // batch_size
for i in range(num_batches):
input_population.set(rate=images[i*batch_size:(i+1)*batch_size])
sim.run(50.0)
spikes = output_population.get_data().segments[0].spiketrains
output = np.zeros((batch_size, output_size))
for j in range(output_size):
output[:, j] = [len(spikes[j][t]) for t in range(batch_size)]
error = output - labels[i*batch_size:(i+1)*batch_size]
hidden_spikes = hidden_population.get_data().segments[0].spiketrains
for j in range(output_size):
for k in range(hidden_size):
if k in [int(t/10.0) for t in hidden_spikes[j]]:
hidden_population[k].set(
tau_m=sim.Time(100.0, sim.ms),
tau_syn_E=sim.Time(1.0, sim.ms),
v_thresh=sim.Time(-50.0, sim.mV),
v_reset=sim.Time(-70.0, sim.mV),
)
dW = learning_rate * np.dot(error[:, j], hidden_population.get_data().segments[0].filter(name='v')[j])
hidden_population[j].weight[0] += dW
sim.reset()
```
最后,我们可以使用训练数据对神经网络进行训练,并使用测试数据进行测试:
```python
# 训练网络
for i in range(num_epochs):
train(train_images, train_labels)
# 测试网络
input_population.set(rate=test_images)
sim.run(50.0)
spikes = output_population.get_data().segments[0].spiketrains
output = np.zeros((test_images.shape[0], output_size))
for j in range(output_size):
output[:, j] = [len(spikes[j][t]) for t in range(test_images.shape[0])]
predictions = np.argmax(output, axis=1)
accuracy = np.mean(predictions == np.argmax(test_labels, axis=1))
print('Test accuracy: {:.2f}%'.format(accuracy * 100))
```
完整的代码示例可以在以下链接中找到:https://github.com/nmi-lab/pynn-mnist
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