写一个gcn与cnn相结合的图像识别代码
时间: 2023-09-26 11:07:28 浏览: 58
好的,以下是一个简单的GCN和CNN相结合的图像识别代码:
```python
import tensorflow as tf
from tensorflow.keras import layers
# GCN layer
class GraphConvolution(layers.Layer):
def __init__(self, units):
super(GraphConvolution, self).__init__()
self.units = units
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]), self.units],
initializer="glorot_uniform",
trainable=True)
self.bias = self.add_weight("bias",
shape=[self.units],
initializer="zeros",
trainable=True)
def call(self, inputs, adj_matrix):
x = tf.matmul(inputs, self.kernel)
x = tf.matmul(adj_matrix, x)
return tf.nn.bias_add(x, self.bias)
# GCN model
class GCN(tf.keras.Model):
def __init__(self, num_classes):
super(GCN, self).__init__()
self.conv1 = GraphConvolution(16)
self.conv2 = GraphConvolution(num_classes)
def call(self, inputs, adj_matrix):
x = self.conv1(inputs, adj_matrix)
x = tf.nn.relu(x)
x = self.conv2(x, adj_matrix)
return tf.nn.softmax(x)
# CNN model
class CNN(tf.keras.Model):
def __init__(self, num_classes):
super(CNN, self).__init__()
self.conv1 = layers.Conv2D(32, (3, 3), activation='relu')
self.pool1 = layers.MaxPooling2D((2, 2))
self.conv2 = layers.Conv2D(64, (3, 3), activation='relu')
self.pool2 = layers.MaxPooling2D((2, 2))
self.conv3 = layers.Conv2D(64, (3, 3), activation='relu')
self.flatten = layers.Flatten()
self.fc1 = layers.Dense(64, activation='relu')
self.fc2 = layers.Dense(num_classes, activation='softmax')
def call(self, inputs):
x = self.conv1(inputs)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.flatten(x)
x = self.fc1(x)
return self.fc2(x)
# GCN-CNN model
class GCN_CNN(tf.keras.Model):
def __init__(self, num_classes, gcn_units):
super(GCN_CNN, self).__init__()
self.gcn = GCN(gcn_units)
self.cnn = CNN(num_classes)
def call(self, inputs, adj_matrix):
x = self.gcn(inputs, adj_matrix)
x = tf.expand_dims(x, axis=-1)
return self.cnn(x)
# Train the model
gcn_cnn_model = GCN_CNN(num_classes=10, gcn_units=16)
gcn_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
gcn_cnn_model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))
```
在这个代码中,我们首先定义了一个GCN层和一个GCN模型,用于处理图像的特征。然后我们定义了一个CNN模型,用于处理图像的像素。最后,我们定义了一个GCN-CNN模型,将GCN模型和CNN模型连接在一起,用于图像识别。
在训练模型时,我们使用了图像数据和邻接矩阵,同时使用了交叉熵作为损失函数和Adam优化器进行优化。
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