读取1*121维csv文件用dcgan生成一维数据代码

以下是一个使用DCGAN生成一维数据的示例代码，假设csv文件中只有一列数据，共121行：

import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

# 读取csv文件
data = pd.read_csv("data.csv", header=None)
x_train = data.values
# 数据预处理
x_train = x_train.astype('float32')
x_train = (x_train - 127.5) / 127.5  # 归一化到[-1, 1]之间

# 定义生成器模型
def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(64, input_shape=(100,), use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(128, use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(121, activation='tanh'))
    return model

# 定义判别器模型
def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(128, input_shape=(121,)))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Dense(64))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Dense(1))
    return model

# 定义损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

# 定义判别器损失函数
def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

# 定义生成器损失函数
def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)

# 定义优化器
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

# 定义训练函数
@tf.function
def train_step(images):
    noise = tf.random.normal([batch_size, 100])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_data = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_data, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 定义训练参数
noise_dim = 100
num_examples_to_generate = 1
generator = make_generator_model()
discriminator = make_discriminator_model()
EPOCHS = 1000
batch_size = 1

# 开始训练
for epoch in range(EPOCHS):
    for i in range(0, len(x_train), batch_size):
        images = x_train[i:i+batch_size]
        train_step(images)

    # 每个epoch生成一组样本数据
    if epoch % 100 == 0:
        print("Epoch:", epoch)
        noise = tf.random.normal([num_examples_to_generate, noise_dim])
        generated_data = generator(noise, training=False)
        generated_data = (generated_data * 127.5 + 127.5).numpy() # 反归一化
        print("Generated data:", generated_data)

需要注意的是，这里使用的是DCGAN，生成器和判别器都是基于全连接层的模型，因此可能需要更多的训练时间才能生成较为真实的数据。如果需要生成更加复杂的数据，可以考虑使用其他类型的GAN模型。