你帮我用tensorflow2来建立微表情识别模型的框架代码
时间: 2024-05-06 10:20:40 浏览: 7
好的,以下是基于TensorFlow 2的微表情识别模型框架代码:
```
import tensorflow as tf
# 定义模型类
class MicroexpressionModel(tf.keras.Model):
def __init__(self):
super(MicroexpressionModel, self).__init__()
# 定义卷积层
self.conv1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(48, 48, 1))
self.conv2 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu')
self.conv3 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu')
# 定义池化层
self.pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
self.pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
# 定义扁平层
self.flatten = tf.keras.layers.Flatten()
# 定义全连接层
self.fc1 = tf.keras.layers.Dense(128, activation='relu')
self.fc2 = tf.keras.layers.Dense(7, activation='softmax')
# 前向传播函数
def call(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.fc2(x)
return x
# 加载数据集
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
X_train = tf.image.resize(X_train, (48, 48))
X_test = tf.image.resize(X_test, (48, 48))
y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)
# 创建模型实例
model = MicroexpressionModel()
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
```
这个模型是一个简单的卷积神经网络,用于对48x48像素的微表情图像进行分类。你可以根据你的具体需求和数据集进行修改。