concatenate_1 = tf.keras.layers.concatenate([conv_22,conv_41],axis=3)

这段代码是在使用 TensorFlow 中的 keras 模块实现卷积神经网络中的层叠操作，其中 conv_22 和 conv_41 是两个卷积层的输出结果，axis=3 表示在第四个维度上进行拼接操作。

相关问题

下面代码在tensorflow中出现了init() missing 1 required positional argument: 'cell'报错： class Model(): def init(self): self.img_seq_shape=(10,128,128,3) self.img_shape=(128,128,3) self.train_img=dataset # self.test_img=dataset_T patch = int(128 / 2 ** 4) self.disc_patch = (patch, patch, 1) self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) self.build_generator=self.build_generator() self.build_discriminator=self.build_discriminator() self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy']) self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer) img_seq_A = Input(shape=(10,128,128,3)) #输入图片 img_B = Input(shape=self.img_shape) #目标图片 fake_B = self.build_generator(img_seq_A) #生成的伪目标图片 self.build_discriminator.trainable = False valid = self.build_discriminator([img_seq_A, fake_B]) self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B]) self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy']) def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out) def build_discriminator(self): def d_layer(layer_input, filters, f_size=4, bn=True): d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) if bn: d = tf.keras.layers.BatchNormalization(momentum=0.8)(d) d = tf.keras.layers.LeakyReLU(alpha=0.2)(d) return d img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3)) img_B = tf.keras.layers.Input(shape=(128, 128, 3)) df = 32 lstm_out = ConvRNN2D(filters=df, kernel_size=4, padding="same")(img_A) lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out) combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B]) d1 = d_layer(combined_imgs, df)#64 d2 = d_layer(d1, df * 2)#32 d3 = d_layer(d2, df * 4)#16 d4 = d_layer(d3, df * 8)#8 validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4) return tf.keras.Model([img_A, img_B], validity)

你在定义 Model 类时，没有传入参数 cell，但是在代码中使用了 ConvRNN2D 的实例化对象，这个对象需要一个 cell 参数。你需要在初始化函数中添加这个参数，如下所示：

class Model():
    def __init__(self, cell):
        self.img_seq_shape=(10,128,128,3)
        self.img_shape=(128,128,3)
        self.train_img=dataset
        patch = int(128 / 2 ** 4)
        self.disc_patch = (patch, patch, 1)
        self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)
        self.build_generator=self.build_generator()
        self.build_discriminator=self.build_discriminator(cell)
        self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy'])
        self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer)
        img_seq_A = Input(shape=(10,128,128,3))
        img_B = Input(shape=self.img_shape)
        fake_B = self.build_generator(img_seq_A)
        self.build_discriminator.trainable = False
        valid = self.build_discriminator([img_seq_A, fake_B])
        self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B])
        self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy'])

    def build_generator(self):
        def res_net(inputs, filters):
            x = inputs
            net = conv2d(x, filters // 2, (1, 1), 1)
            net = conv2d(net, filters, (3, 3), 1)
            net = net + x 
            return net 

        def conv2d(inputs, filters, kernel_size, strides):
            x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
            x = tf.keras.layers.BatchNormalization()(x)
            x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
            return x 

        d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3))
        out= ConvRNN2D(cell, filters=32, kernel_size=3,padding='same')(d0)
        out=tf.keras.layers.Conv2D(3,1,1,'same')(out)
        return keras.Model(inputs=d0, outputs=out)

    def build_discriminator(self, cell):
        def d_layer(layer_input, filters, f_size=4, bn=True):
            d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
            if bn:
                d = tf.keras.layers.BatchNormalization(momentum=0.8)(d)
            d = tf.keras.layers.LeakyReLU(alpha=0.2)(d)
            return d 

        img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3))
        img_B = tf.keras.layers.Input(shape=(128, 128, 3))
        df = 32
        lstm_out = ConvRNN2D(cell, filters=df, kernel_size=4, padding="same")(img_A)
        lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out)
        combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B])
        d1 = d_layer(combined_imgs, df)#64
        d2 = d_layer(d1, df * 2)#32
        d3 = d_layer(d2, df * 4)#16
        d4 = d_layer(d3, df * 8)#8
        validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
        return tf.keras.Model([img_A, img_B], validity)

def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42,42,1)) conv1 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs1) bn1 = layers.BatchNormalization()(conv1) pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn1) do1 = layers.Dropout(0.3)(pool1) # channel 2 inputs2 = layers.Input(shape=(42,42,1)) conv2 = layers.Conv2D(3, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs2) bn2 = layers.BatchNormalization()(conv2) pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn2) do2 = layers.Dropout(0.3)(pool2) # channel 3 inputs3 = layers.Input(shape=(42,42,1)) conv3 = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs3) bn3 = layers.BatchNormalization()(conv3) pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(bn3) do3 = layers.Dropout(0.3)(pool3) # merge 1 merged = layers.Concatenate()([do1, do2, do3]) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model 如何先Concatenate输入图像，然后加入CBAM

您可以使用以下代码来加入CBAM：

from tensorflow.keras import layers

def conv_block(inputs, filters):
    x = layers.Conv2D(filters, kernel_size=3, padding='same', use_bias=False)(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    return x

def channel_attention(inputs, ratio=8):
    channels_axis = 3
    channels = inputs.shape[channels_axis]
    shared_layer_one = layers.Dense(channels//ratio, activation='relu', use_bias=True, kernel_initializer='he_normal')
    shared_layer_two = layers.Dense(channels, activation='sigmoid', use_bias=True, kernel_initializer='he_normal')

    avg_pool = layers.GlobalAveragePooling2D()(inputs)    
    avg_pool = layers.Reshape((1,1,channels))(avg_pool)
    assert avg_pool.shape[1:] == (1,1,channels)
    avg_pool = shared_layer_one(avg_pool)
    assert avg_pool.shape[1:] == (1,1,channels//ratio)
    avg_pool = shared_layer_two(avg_pool)
    assert avg_pool.shape[1:] == (1,1,channels)
    return layers.multiply([inputs, avg_pool])

def spatial_attention(inputs):
    kernel_size = 7
    channels = inputs.shape[3]
    x = layers.Conv2D(channels, kernel_size, padding='same', use_bias=False)(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    avg_pool = layers.Lambda(lambda x: tf.reduce_mean(x, axis=3, keepdims=True))(x)
    max_pool = layers.Lambda(lambda x: tf.reduce_max(x, axis=3, keepdims=True))(x)
    concat = layers.Concatenate(axis=3)([avg_pool, max_pool])
    assert concat.shape[1:] == (inputs.shape[1], inputs.shape[2], 2)
    x = layers.Conv2D(1, kernel_size, padding='same', use_bias=False, activation='sigmoid')(concat)
    assert x.shape[1:] == (inputs.shape[1], inputs.shape[2], 1)
    return layers.multiply([inputs, x])

def cbam_block(inputs, ratio=8):
    x = channel_attention(inputs, ratio)
    x = spatial_attention(x)
    return x

def MEAN_Spot(opt):
    # channel 1
    inputs1 = layers.Input(shape=(42,42,1))
    conv1 = conv_block(inputs1, 3)
    pool1 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(conv1)
    do1 = layers.Dropout(0.3)(pool1)

    # channel 2
    inputs2 = layers.Input(shape=(42,42,1))
    conv2 = conv_block(inputs2, 3)
    pool2 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(conv2)
    do2 = layers.Dropout(0.3)(pool2)

    # channel 3
    inputs3 = layers.Input(shape=(42,42,1))
    conv3 = conv_block(inputs3, 8)
    pool3 = layers.MaxPooling2D(pool_size=(3, 3), padding='same', strides=(3,3))(conv3)
    do3 = layers.Dropout(0.3)(pool3)

    # merge 1
    merged = layers.Concatenate()([do1, do2, do3])
    cbam = cbam_block(merged)

    # interpretation 1
    merged_conv = conv_block(cbam, 8)
    merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv)
    flat = layers.Flatten()(merged_pool)
    flat_do = layers.Dropout(0.2)(flat)

    # outputs
    outputs = layers.Dense(1, activation='linear', name='spot')(flat_do)

    #Takes input u, v, os
    model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs])
    model.compile(
        loss={'spot':'mse'},
        optimizer=opt,
        metrics={'spot':tf.keras.metrics.MeanAbsoluteError()},
    )

    return model

这里定义了`conv_block`函数来构建卷积块，同时定义了`channel_attention`和`spatial_attention`函数来构建CBAM的通道注意力和空间注意力模块，最后用`cbam_block`函数将两个注意力模块组合起来，加入到模型中。在`MEAN_Spot`函数中，先将输入图像进行Concatenate，然后加入CBAM模块，最后继续进行后续的卷积、池化和全连接层操作。