keras.layers.BatchNormalization(momentum = 0.8),是什么意思

keras.layers.BatchNormalization(momentum=0.8)是一个在Keras深度学习框架中用于实现批量标准化的层。其中，momentum参数是指动量，它控制着每个批次标准化的结果对于前面批次标准化结果的贡献。具体来说，这里的动量指的是滑动平均的动量，用于计算每个批次的均值和方差。在实际使用中，通常将momentum参数设置为一个较小的值，例如0.8，以实现更平滑的标准化结果。

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)

用keras编写CGAN扩增DEAP脑电数据集的代码

以下是基于Keras编写的CGAN扩增DEAP脑电数据集的代码：

import numpy as np
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, concatenate, Conv1D, Conv2DTranspose
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.datasets import mnist
from keras import backend as K

# Load DEAP dataset
def load_data():
    # Load data from file
    data = np.load('data.npy')
    labels = np.load('labels.npy')

    # Normalize data
    data = (data - np.min(data)) / (np.max(data) - np.min(data))

    # Reshape data to (samples, channels, samples_per_channel)
    data = np.reshape(data, (data.shape[0], data.shape[1], -1))

    # Convert labels to one-hot encoding
    labels = np_utils.to_categorical(labels)

    return data, labels

# Define generator
def build_generator():

    # Neural network architecture
    model = Sequential()

    model.add(Dense(256, input_dim=100))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(1024))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(28*28*1, activation='tanh'))
    model.add(Reshape((28, 28, 1)))

    # Output
    noise = Input(shape=(100,))
    label = Input(shape=(10,))
    label_embedding = Flatten()(Embedding(10, 100)(label))

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

    return Model([noise, label], img)

# Define discriminator
def build_discriminator():

    # Neural network architecture
    model = Sequential()

    model.add(Conv1D(32, kernel_size=3, strides=2, input_shape=(40, 8064)))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv1D(64, kernel_size=3, strides=2))
    model.add(ZeroPadding2D(padding=((0,1),(0,1))))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Conv1D(128, kernel_size=3, strides=2))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Conv1D(256, kernel_size=3, strides=1))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Conv1D(512, kernel_size=3, strides=1))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    # Output
    img = Input(shape=(40, 8064))
    label = Input(shape=(10,))
    label_embedding = Flatten()(Embedding(10, 40*8064)(label))
    label_embedding = Reshape((40, 8064, 1))(label_embedding)
    concat = concatenate([img, label_embedding], axis=3)
    validity = model(concat)

    return Model([img, label], validity)

# Define CGAN model
def build_cgan(generator, discriminator):

    # Discriminator is not trainable during generator training
    discriminator.trainable = False

    # Model architecture
    model = Sequential()
    model.add(generator)
    model.add(discriminator)

    # Compile model
    optimizer = Adam(0.0002, 0.5)
    model.compile(loss='binary_crossentropy', optimizer=optimizer)

    return model

# Train CGAN model
def train_cgan(generator, discriminator, cgan, data, labels, epochs, batch_size):

    # Adversarial ground truth
    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # ---------------------
        #  Train Discriminator
        # ---------------------

        # Select a random batch of data and labels
        idx = np.random.randint(0, data.shape[0], batch_size)
        real_data = data[idx]
        real_labels = labels[idx]

        # Sample noise and generate a batch of new data
        noise = np.random.normal(0, 1, (batch_size, 100))
        fake_labels = np_utils.to_categorical(np.random.randint(0, 10, batch_size), 10)
        gen_data = generator.predict([noise, fake_labels])

        # Train the discriminator on real and fake data
        d_loss_real = discriminator.train_on_batch([real_data, real_labels], valid)
        d_loss_fake = discriminator.train_on_batch([gen_data, fake_labels], fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # ---------------------
        #  Train Generator
        # ---------------------

        # Sample noise and generate a batch of new data
        noise = np.random.normal(0, 1, (batch_size, 100))
        fake_labels = np_utils.to_categorical(np.random.randint(0, 10, batch_size), 10)

        # Train the generator to fool the discriminator
        g_loss = cgan.train_on_batch([noise, fake_labels], valid)

        # Print progress
        print ("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss, g_loss))

# Load data
data, labels = load_data()

# Build generator and discriminator
generator = build_generator()
discriminator = build_discriminator()

# Build CGAN model
cgan = build_cgan(generator, discriminator)

# Train CGAN model
train_cgan(generator, discriminator, cgan, data, labels, epochs=2000, batch_size=32)

注意：以上代码仅供参考，可能需要根据具体情况进行调整和修改。