d = BatchNormalization(momentum=0.8)(d)

这行代码是在使用 Keras 搭建神经网络时用来添加 BatchNormalization 层的。BatchNormalization 层是一种常用的技术，可以在每个 mini-batch 的数据上进行归一化处理，有效地防止梯度消失和梯度爆炸的问题，并且可以加速模型的训练和提高模型的泛化能力。其中，momentum 参数表示当前 batch 的统计量与之前 batch 的统计量融合的比重，通常设为 0.8 或 0.9。

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)

ACGAN的Python代码

ACGAN是一种生成对抗网络（GAN）的变种，其中“AC”代表辅助分类器（auxiliary classifier），它通过将类别标签作为输入来帮助生成器学习生成特定类别的图像。以下是一个简单的ACGAN的Python代码实现：

# 导入必要的库
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
from tensorflow.keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
from tensorflow.keras.layers import MaxPooling2D, Conv2DTranspose, UpSampling2D, Conv2D
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam

# 定义超参数
img_rows = 28
img_cols = 28
channels = 1
img_shape = (img_rows, img_cols, channels)
num_classes = 10
latent_dim = 100

# 构建生成器
def build_generator():

    model = Sequential()

    model.add(Dense(128 * 7 * 7, activation="relu", input_dim=latent_dim))
    model.add(Reshape((7, 7, 128)))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding="same"))
    model.add(Conv2D(64, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))
    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding="same"))
    model.add(Activation("tanh"))

    noise = Input(shape=(latent_dim,))
    label = Input(shape=(1,), dtype="int32")
    label_embedding = Flatten()(Embedding(num_classes, latent_dim)(label))

    model_input = multiply([noise, label_embedding])
    img = model(model_input)

    return Model([noise, label], img)

# 构建辅助分类器
def build_discriminator():

    model = Sequential()

    model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=img_shape, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
    model.add(ZeroPadding2D(padding=((0,1),(0,1))))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Flatten())

    img = Input(shape=img_shape)

    features = model(img)

    validity = Dense(1, activation="sigmoid")(features)
    label = Dense(num_classes+1, activation="softmax")(features)

    return Model(img, [validity, label])

# 编译辅助分类器
optimizer = Adam(0.0002, 0.5)
discriminator = build_discriminator()
discriminator.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], loss_weights=[0.5, 0.5], optimizer=optimizer)

# 构建组合模型
generator = build_generator()
noise = Input(shape=(latent_dim,))
label = Input(shape=(1,))
img = generator([noise, label])
discriminator.trainable = False
validity, _ = discriminator(img)
combined = Model([noise, label], validity)
combined.compile(loss=['binary_crossentropy'], optimizer=optimizer)

# 训练ACGAN模型
epochs = 20000
batch_size = 32

(X_train, y_train), (_, _) = mnist.load_data()
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)
y_train = y_train.reshape(-1, 1)

half_batch = int(batch_size / 2)

for epoch in range(epochs):

    # 训练辅助分类器
    idx = np.random.randint(0, X_train.shape, half_batch)
    imgs, labels = X_train[idx], y_train[idx]

    noise = np.random.normal(0, 1, (half_batch, latent_dim))
    gen_labels = np.random.randint(0, num_classes, half_batch).reshape(-1, 1)

    gen_imgs = generator.predict([noise, gen_labels])

    d_loss_real = discriminator.train_on_batch(imgs, [np.ones((half_batch, 1)), labels])
    d_loss_fake = discriminator.train_on_batch(gen_imgs, [np.zeros((half_batch, 1)), gen_labels])
    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

    # 训练生成器
    noise = np.random.normal(0, 1, (batch_size, latent_dim))
    valid_y = np.array( * batch_size).reshape(-1, 1)
    
    # 增加噪声，使得生成的图像具有不同的类别标签
    sampled_labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)
    
    g_loss = combined.train_on_batch([noise, sampled_labels], valid_y)

# 提出相关问题：