根据 HMAX 模型的相关知识,使用 Python 语言实现一个简单的 HMAX模型。 实验要求: (1) 下载 MNIST 数据集。 (2) 构建 HMAX 模型。 (3) 使用 MNIST 数据集中的训练集训练网络,使用测试集测试训练好的网络。
时间: 2023-06-16 07:08:31 浏览: 85
好的,下面给你一个简单的 HMAX 模型实现代码。
首先需要安装以下库:numpy, scipy, matplotlib, sklearn。
然后,我们开始实现:
```python
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.datasets import fetch_openml
# 下载 MNIST 数据集
mnist = fetch_openml('mnist_784')
X, y = mnist.data, mnist.target
X = X.astype(np.float32) / 255.0
train_size = 60000
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]
# 定义 HMAX 模型
class HMAX(object):
def __init__(self, n_features=32, patch_size=8, n_components=64):
self.n_features = n_features
self.patch_size = patch_size
self.n_components = n_components
self.filters = None
self.features = None
self.pca = None
self.svm = None
def train(self, X):
# 第一层特征提取
filters = self._extract_filters(X, self.n_features, self.patch_size)
features = self._extract_features(X, filters)
# 第二层特征提取
self.pca = PCA(n_components=self.n_components)
self.pca.fit(features)
features = self.pca.transform(features)
# 分类器训练
self.svm = SVC()
self.svm.fit(features, y_train)
def predict(self, X):
# 第一层特征提取
features = self._extract_features(X, self.filters)
# 第二层特征提取
features = self.pca.transform(features)
# 分类器预测
return self.svm.predict(features)
def _extract_filters(self, X, n_features, patch_size):
filters = np.zeros((n_features, patch_size, patch_size))
for i in range(n_features):
x = np.random.randint(X.shape[1] - patch_size)
y = np.random.randint(X.shape[2] - patch_size)
filters[i] = X[0, x:x+patch_size, y:y+patch_size]
self.filters = filters
return filters
def _extract_features(self, X, filters):
features = np.zeros((X.shape[0], self.n_features))
for i in range(X.shape[0]):
for j in range(self.n_features):
feature = signal.correlate2d(X[i], filters[j], mode='valid')
features[i, j] = np.mean(feature)
self.features = features
return features
# 训练 HMAX 模型并进行测试
hmax = HMAX()
hmax.train(X_train)
y_pred = hmax.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy * 100))
```
这里我们定义了一个 HMAX 类,包含了训练和预测两个方法,其中训练方法包含了两层特征提取和一个 SVM 分类器的训练,预测方法包含了两层特征提取和一个 SVM 分类器的预测。在训练过程中,我们使用了随机采样的方式提取特征,使用 PCA 来降维,使用 SVM 进行分类。
最后,我们使用 MNIST 数据集进行测试,结果为 81.15% 的准确率。