介绍一下脑电的DEAP数据集

DEAP数据集是一个用于情感分析研究的公开数据集，包含来自32个受试者的脑电信号、生理信号、音频和视频数据。这些数据用于研究人类情感感知和情感识别，以及基于情感的多媒体内容的自适应推荐。数据集详细描述了每个试验的协议，包括被试者的情感状态、刺激呈现方式和各种生理测量。该数据集在情感研究中得到了广泛的应用。

相关问题

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。