d1 = d_layer(combined_imgs, df, bn=False) d2 = d_layer(d1, df*2) d3 = d_layer(d2, df*4) d4 = d_layer(d3, df*8)

这段代码是基于深度学习框架Keras，通过调用一个自定义的d_layer函数来搭建一个基本的卷积神经网络（CNN）判别器模型。其中，d_layer是一个卷积层的封装函数，用于简化模型构建过程。df是一个整数类型的超参数，表示模型的初始卷积核数量，bn是一个布尔类型的超参数，用于控制是否使用批归一化（batch normalization）技术。

具体来说，这段代码实现了一个含有4个卷积层的CNN判别器模型，其中每个卷积层的卷积核数量都是前一个卷积层的2倍，即第1层的卷积核数量为df，第2层为df*2，第3层为df*4，第4层为df*8。模型的输入是combined_imgs，即三张图片的拼接结果，模型的输出是一个实数，表示输入图片的真实度分数。

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)

解释一下 def __getitem__(self, index): current_index = np.random.choice(range(0, self.num_samples)) imgs_path = self.imgseqs[current_index].split() current_imgs = [] current_imgs_path = imgs_path[:len(imgs_path) - 1] current_label_path = imgs_path[len(imgs_path) - 1] current_label = self.loader(current_label_path)

这是一个在自定义数据集中使用的函数，用于返回给定索引对应的数据样本。在这个函数里，它随机选择一个索引，读取对应的图像序列路径（imgs_path），并把图像序列路径分成两个部分：当前图像的路径（current_imgs_path）和标签的路径（current_label_path）。然后，这个函数通过调用 self.loader() 函数来加载标签图像，并把当前图像路径和标签图像一起返回。