CBAM-CNN matlab

CBAM-CNN是一种用于计算机视觉领域的卷积神经网络结构，它通过引入注意力机制来提升网络性能。具体来说，CBAM模型结合了通道注意力模块（Channel Attention Module）和空间注意力模块（Spatial Attention Module）两个部分。通道注意力模块用于学习特征图的通道相关性，以便增强重要特征的表示能力。空间注意力模块则通过对特征图的空间分布进行建模，来捕捉不同区域的上下文信息。通过将这两个模块结合起来，CBAM-CNN能够更好地适应不同的图像特征，并提升网络在分类和检测任务中的性能。

鉴于你提到的是CBAM-CNN与Matlab的

相关问题

CBAM-CNN-LSTM股票预测的Python代码

抱歉，我无法提供完整的CBAM-CNN-LSTM股票预测的Python代码，因为这涉及到一个复杂的模型架构和数据处理步骤。不过，我可以向你解释一下这个模型的主要思想和步骤，帮助你理解如何实现它。

CBAM-CNN-LSTM模型是一种结合了卷积神经网络（CNN）、循环神经网络（LSTM）和通道与空间注意力机制（CBAM）的混合模型，用于股票预测。下面是一个简单的伪代码示例，展示了CBAM-CNN-LSTM模型的主要步骤：

# 导入必要的库
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense, Conv1D, MaxPooling1D, GlobalAveragePooling1D

# 加载和准备数据
data = pd.read_csv('stock_data.csv')
# 数据预处理步骤...

# 构建CBAM-CNN-LSTM模型
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(timesteps, features)))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(GlobalAveragePooling1D())
model.add(LSTM(units=64))
model.add(Dense(units=1, activation='sigmoid'))

# 编译和训练模型
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=10, batch_size=32)

# 使用模型进行预测
predictions = model.predict(X_test)

# 评估模型性能
# ...

这只是一个简单的示例，实际应用中可能需要更复杂的数据预处理、调参等步骤。你可以根据自己的需求和数据集进行相应的修改和调整。记得根据你的数据特性，进行适当的调整和优化，以便获得更好的预测结果。

CBAM-ResNet tf实现

以下是CBAM-ResNet的TensorFlow实现：

import tensorflow as tf

def conv2d(inputs, filters, kernel_size, strides=1, padding='same', activation=None, name=None):
    return tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=kernel_size,
                            strides=strides, padding=padding, activation=activation, name=name)

def batch_norm(inputs, training, momentum=0.997, epsilon=1e-5, name=None):
    return tf.layers.batch_normalization(inputs=inputs, momentum=momentum, epsilon=epsilon,
                                         scale=True, training=training, name=name)

def relu(inputs, name=None):
    return tf.nn.relu(inputs, name=name)

def max_pool2d(inputs, pool_size, strides, padding='same', name=None):
    return tf.layers.max_pooling2d(inputs=inputs, pool_size=pool_size, strides=strides,
                                   padding=padding, name=name)

def avg_pool2d(inputs, pool_size, strides, padding='same', name=None):
    return tf.layers.average_pooling2d(inputs=inputs, pool_size=pool_size, strides=strides,
                                       padding=padding, name=name)

def cbam_block(inputs, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        # Channel attention
        channels = inputs.get_shape()[-1]
        avg_pool = tf.reduce_mean(inputs, axis=[1, 2], keepdims=True)
        assert avg_pool.get_shape()[1:] == (1, 1, channels)
        max_pool = tf.reduce_max(inputs, axis=[1, 2], keepdims=True)
        assert max_pool.get_shape()[1:] == (1, 1, channels)

        fc1 = conv2d(avg_pool, int(channels * reduction_ratio), kernel_size=1, name='fc1')
        assert fc1.get_shape()[1:] == (1, 1, int(channels * reduction_ratio))
        relu1 = relu(fc1, name='relu1')
        fc2 = conv2d(relu1, channels, kernel_size=1, name='fc2')
        assert fc2.get_shape()[1:] == (1, 1, channels)
        # channel attention的权重
        ch_attention = tf.sigmoid(fc2 + max_pool)

        # Spatial attention
        max_pool2d = tf.reduce_max(ch_attention, axis=-1, keepdims=True)
        assert max_pool2d.get_shape()[1:] == (1, 1, 1)
        avg_pool2d = tf.reduce_mean(ch_attention, axis=-1, keepdims=True)
        assert avg_pool2d.get_shape()[1:] == (1, 1, 1)
        # spatial attention的权重
        sp_attention = tf.sigmoid(max_pool2d + avg_pool2d)

        # 输出加权后的特征
        output = inputs * ch_attention * sp_attention
        return output

def cbam_resnet_block(inputs, filters, strides, training, projection_shortcut, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        shortcut = inputs
        if projection_shortcut is not None:
            shortcut = projection_shortcut(inputs)

        inputs = conv2d(inputs, filters, kernel_size=1, strides=1, name='conv1')
        inputs = batch_norm(inputs, training=training, name='bn1')
        inputs = relu(inputs, name='relu1')

        inputs = conv2d(inputs, filters, kernel_size=3, strides=strides, name='conv2')
        inputs = batch_norm(inputs, training=training, name='bn2')
        inputs = relu(inputs, name='relu2')

        inputs = cbam_block(inputs, reduction_ratio=reduction_ratio, name='cbam_block')

        inputs += shortcut
        inputs = relu(inputs, name='relu_output')
        return inputs

def cbam_resnet(inputs, num_blocks, filters, training, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        # 第一层
        inputs = conv2d(inputs, filters[0], kernel_size=7, strides=2, name='conv1')
        inputs = batch_norm(inputs, training=training, name='bn1')
        inputs = relu(inputs, name='relu1')
        inputs = max_pool2d(inputs, pool_size=3, strides=2, name='max_pool1')

        # resnet blocks
        for i in range(num_blocks):
            filters_block = filters[i+1]
            strides = 1
            if i == 0:
                strides = 2
            inputs = cbam_resnet_block(inputs, filters_block, strides, training,
                                        projection_shortcut=cbam_projection_shortcut if strides > 1 else None,
                                        reduction_ratio=reduction_ratio, name='block{}'.format(i+1))

        inputs = batch_norm(inputs, training=training, name='bn_last')
        inputs = relu(inputs, name='relu_last')
        inputs = avg_pool2d(inputs, pool_size=inputs.get_shape()[1:-1], strides=1, name='avg_pool')
        inputs = tf.layers.flatten(inputs, name='flatten')
        return inputs


def cbam_projection_shortcut(inputs, filters, strides, training, reduction_ratio=0.5, name=None):
    with tf.variable_scope(name):
        shortcut = conv2d(inputs, filters, kernel_size=1, strides=strides, name='conv_projection')
        shortcut = batch_norm(shortcut, training=training, name='bn_projection')
        shortcut = cbam_block(shortcut, reduction_ratio=reduction_ratio, name='cbam_projection')
        return shortcut

其中，`cbam_block` 和 `cbam_resnet_block` 实现CBAM模块和CBAM-ResNet模块，`cbam_resnet` 实现CBAM-ResNet网络的整体架构，`cbam_projection_shortcut` 实现CBAM-ResNet中的投影shortcut。