tensorflow2.0实现基于卷积神经网络的人脸识别,对卷积神经网络设置不同的卷积层,池化层,dropout参数,、全连接层、输出层(分类器),不同的优化器等,做多方案对比实验,分析选择不同参数对实验结果的影响。数据集采用Yale_64x64.mat,要图片或数据的形式,展示实验结果,展示源代码。
时间: 2024-02-25 09:53:54 浏览: 27
好的,以下是基于TensorFlow 2.0实现基于卷积神经网络的人脸识别的代码,使用Yale_64x64数据集,并对不同参数进行实验对比:
```python
import tensorflow as tf
from tensorflow.keras import layers, models, optimizers
import matplotlib.pyplot as plt
import scipy.io as sio
import numpy as np
# 数据集准备
data = sio.loadmat('Yale_64x64.mat')
X = data['fea'].astype(np.float32)
y = data['gnd'][:, 0]
X = X.reshape(-1, 64, 64, 1)
X_train, X_test = X[:1800], X[1800:]
y_train, y_test = y[:1800], y[1800:]
X_train, X_test = X_train / 255.0, X_test / 255.0
# 搭建卷积神经网络
def create_model(conv_layers, kernel_size, pool_size, dropout_rate, optimizer):
model = models.Sequential()
model.add(layers.Conv2D(conv_layers, kernel_size, activation='relu', input_shape=(64, 64, 1)))
model.add(layers.MaxPooling2D(pool_size=pool_size))
model.add(layers.Conv2D(conv_layers*2, kernel_size, activation='relu'))
model.add(layers.MaxPooling2D(pool_size=pool_size))
model.add(layers.Conv2D(conv_layers*4, kernel_size, activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dropout(dropout_rate))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(15, activation='softmax'))
model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
# 实验对比
conv_layers_list = [16, 32, 64]
kernel_size_list = [(3, 3), (5, 5), (7, 7)]
pool_size_list = [(2, 2), (3, 3), (4, 4)]
dropout_rate_list = [0.2, 0.3, 0.4]
optimizer_list = [optimizers.Adam(), optimizers.SGD()]
results = {}
for conv_layers in conv_layers_list:
for kernel_size in kernel_size_list:
for pool_size in pool_size_list:
for dropout_rate in dropout_rate_list:
for optimizer in optimizer_list:
model = create_model(conv_layers, kernel_size, pool_size, dropout_rate, optimizer)
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
key = f"conv_layers:{conv_layers},kernel_size:{kernel_size},pool_size:{pool_size},dropout_rate:{dropout_rate},optimizer:{optimizer}"
results[key] = history
# 结果展示
for key, history in results.items():
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.title(key)
plt.legend()
plt.show()
```
在以上代码中,我们首先使用`scipy.io`模块加载了Yale_64x64数据集,并将像素值缩放到0到1之间。接着我们使用函数`create_model`搭建了卷积神经网络,并设置了不同的卷积层数量、卷积核大小、池化层大小、dropout参数、优化器等参数。在实验对比部分,我们使用了嵌套循环,对不同的参数进行实验对比,并将训练结果保存在字典`results`中。最后,我们使用Matplotlib将不同实验的训练集和测试集准确率进行可视化展示。
希望以上代码可以帮助您实现基于TensorFlow 2.0的人脸识别,并对不同参数进行实验对比。如果还有疑问,欢迎继续追问。
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