pyNN.spiNNaker如何安装
时间: 2023-09-02 18:11:36 浏览: 49
pyNN.spiNNaker是一个Python库,用于在SpiNNaker硬件平台上模拟神经网络。它可以在Ubuntu Linux上安装。以下是安装步骤:
1. 安装依赖项
在终端中输入以下命令以安装pyNN.spiNNaker所需的依赖项:
```
sudo apt-get install build-essential python-dev python-setuptools python-numpy python-scipy python-pip python-matplotlib python-tk libhdf5-dev libyaml-dev libltdl-dev
```
2. 安装SpiNNaker软件
按照SpiNNaker Wiki上的指示安装SpiNNaker软件,包括sPyNNaker和SpiNNaker tools。
3. 安装pyNN.spiNNaker
在终端中输入以下命令以安装pyNN.spiNNaker:
```
sudo pip install pyNN[sPyNNaker]
```
4. 测试安装
在Python终端中输入以下命令以测试pyNN.spiNNaker:
```
import pyNN.spiNNaker as sim
```
如果没有错误消息,则安装成功。
注意:如果您使用的是其他Linux发行版(例如Debian),则可能需要按照不同的指示进行安装。请参阅pyNN.spiNNaker文档以获取更多信息。
相关问题
脉冲神经网络实现手写数字识别代码实现
脉冲神经网络实现手写数字识别的代码实现通常需要使用特定的神经网络库,例如PyNN,NEST或Brian2。以下是使用PyNN库实现手写数字识别的示例代码:
```python
import numpy as np
import matplotlib.pyplot as plt
import pyNN.spiNNaker as sim
# 定义输入和输出神经元数量
input_size = 784 # 28x28像素的图像
output_size = 10 # 数字0-9的分类
# 定义脉冲神经元模型
cell_params = {
'tau_m': 20.0, # 膜电位时间常数
'tau_refrac': 2.0, # 绝对不应期时间常数
'tau_syn_E': 2.5, # 兴奋性突触时间常数
'tau_syn_I': 2.5, # 抑制性突触时间常数
'v_reset': -70.0, # 重置电位
'v_rest': -65.0, # 静息电位
'v_thresh': -50.0, # 阈值电位
}
# 创建神经元群
input_population = sim.Population(input_size, sim.SpikeSourcePoisson, {'rate': 0.0})
hidden_population = sim.Population(100, sim.IF_curr_exp, cell_params)
output_population = sim.Population(output_size, sim.IF_curr_exp, cell_params)
# 连接神经元群
input_to_hidden = sim.Projection(input_population, hidden_population, sim.AllToAllConnector())
hidden_to_output = sim.Projection(hidden_population, output_population, sim.AllToAllConnector())
# 定义输入和输出标签
input_label = np.zeros((input_size, output_size))
for i in range(output_size):
input_label[i*input_size//output_size:(i+1)*input_size//output_size, i] = 1
# 定义训练数据
train_data = np.load('train_data.npy')
train_label = np.load('train_label.npy')
# 将训练数据编码为脉冲序列并输入到神经元群中
for i in range(train_data.shape[0]):
input_population.set(rate=train_data[i])
sim.run(50.0) # 模拟50毫秒
spikes = output_population.get_data().segments[0].spiketrains
output = np.zeros(output_size)
for j in range(output_size):
output[j] = len(spikes[j]) # 记录输出脉冲数
# 训练输出层
error = output_label[i] - output
hidden_spikes = hidden_population.get_data().segments[0].spiketrains
for j in range(output_size):
for k in range(100):
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 = 0.1 * error[j] * hidden_population.get_data().segments[0].filter(name='v')[j]
hidden_population[j].weight[0] += dW
sim.end() # 结束模拟
```
上述代码仅为示例,实际的脉冲神经网络实现手写数字识别需要更复杂的网络结构和算法。
脉冲神经网络实现手写数字识别代码具体实现
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