好像还挺好玩的生成式对抗网络生成一维数据(DCGAN)

from __future__ import print_function, division
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D, GlobalAveragePooling2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import pandas as pd
import matplotlib.pyplot as plt
import sys
import numpy as np
import csv


class GAN():
    def __init__(self):
        self.data_rows = 20
        self.data_cols = 20
        self.channels = 1
        self.data_shape = (self.data_rows, self.data_cols, self.channels)
        self.latent_dim = 100
        self.sample_size = 200
        optimizer = Adam(0.0002, 0.5)
        # 构建和编译判别器
        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
                                   optimizer=optimizer,
                                   metrics=['accuracy'])

        # 构建生成器
        self.generator = self.build_generator()

        # 生成器输入噪音，生成假的图片
        z = Input(shape=(self.latent_dim,))
        data = self.generator(z)  # 生成器生成的图片

        # 为了组合模型，只训练生成器,不训练判别器
        self.discriminator.trainable = False

        # 判别器将生成的图像作为输入并确定有效性
        validity = self.discriminator(data)  # 这个是判别器判断生成器生成图片的结果

        # The combined model  (stacked generator and discriminator)
        # 训练生成器骗过判别器
        self.combined = Model(z, validity)
        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

    def build_generator(self):
        model = Sequential()
        # 先全连接到32*7*7的维度上
        model.add(Dense(128 * 5 * 5, activation="relu", input_dim=self.latent_dim))
        # reshape成特征层的样式
        model.add(Reshape((5, 5, 128)))

        # 5, 5, 64
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))

        # 上采样
        # 5, 5, 64 -> 10, 10, 128
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))

        # 上采样
        # 10, 10, 128 -> 20, 20, 64
        # model.add(UpSampling2D())
        # model.add(Conv2D(64, kernel_size=3, padding="same"))
        # model.add(BatchNormalization(momentum=0.8))
        # model.add(Activation("relu"))

        # 20, 20, 64 -> 20, 20, 1
        model.add(Conv2D(1, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        data = model(noise)

        # 输入噪音，输出图片
        return Model(noise, data)

    def build_discriminator(self):
        model = Sequential()
        # 20, 20, 1 -> 10, 10 ,32
        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.data_shape, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dropout(0.25))

        # 10, 10, 32 -> 5, 5, 64
        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
        model.add(ZeroPadding2D(((0, 1), (0, 1))))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))

        # 5, 5, 64 -> 6, 6, 64 -> 3, 3, 128
        model.add(Conv2D(128, 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(256, 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(Flatten())
        # 全连接
        model.add(Dense(1, activation="sigmoid"))

        model.summary()

        data = Input(shape=self.data_shape)
        validity = model(data)

        return Model(data, validity)

    def train(self, epochs, batch_size=128, sample_interval=500):
        # 加载数据集
        data = pd.read_csv("gan_data.csv", header=None)
        data = np.array(data.values.tolist())
        data_list = []
        for i in data:
            a = np.hstack((i, np.zeros(200)))
            data_list.append(a)
        data = np.array(data_list).reshape(3520, 20, 20)
        data = np.expand_dims(data, axis=3)
        # 将数据进行归一化处理
        data = data / 194 - 1
        # data = np.expand_dims(, axis=3)
        # Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))
        for epoch in range(epochs):
            # ---------------------
            #  训练判别器
            # ---------------------
            # X_train.shape[0]为数据集的数量，随机生成batch_size个数量的随机数，作为数据的索引
            idx = np.random.randint(0, data.shape[0], batch_size)

            # 从数据集随机挑选batch_size个数据，作为一个批次训练
            x = data[idx]
            # 噪音维度(batch_size,100)
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

            # 由生成器根据噪音生成假的图片
            gen_x = self.generator.predict(noise)

            # 训练判别器，判别器希望真实图片，打上标签1，假的图片打上标签0
            d_loss_real = self.discriminator.train_on_batch(x, valid)
            # print(d_loss_real)
            d_loss_fake = self.discriminator.train_on_batch(gen_x, fake)
            # print(d_loss_fake)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
            # print(d_loss)
            # ---------------------
            #  训练生成器
            # ---------------------
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
            # Train the generator (to have the discriminator label samples as valid)
            g_loss = self.combined.train_on_batch(noise, valid)
            # gen_x = (gen_x + 1) * 192
            # 打印loss值
            print("%d [D loss: %f, acc: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))
            # print("data", gen_x)
            # 每sample_interval个epoch保存一次生成图片
            if epoch % sample_interval == 0:
                self.sample_data(epoch)
                if not os.path.exists("dcgan_model"):
                    os.makedirs("dcgan_model")
                self.generator.save_weights("dcgan_model/G_model%d.hdf5" % epoch, True)
                self.discriminator.save_weights("dcgan_model/D_model%d.hdf5" % epoch, True)

    def data_write_csv(self, epoch, gen_datas, num):
        if not os.path.exists("dcgan_data"):
            os.makedirs("dcgan_data")
        if epoch == 666:
            file_name = "dcgan_test/test.csv"
        else:
            file_name = "dcgan_data/%d.csv" % epoch
        print(file_name)
        gen_datas = gen_datas.reshape(num, 400)
        dt = pd.DataFrame(gen_datas)
        dt.to_csv(file_name, index=0)

    def sample_data(self, epoch):
        # 重新生成一批噪音，维度为(self.sample_size,100)
        noise = np.random.normal(0, 1, (self.sample_size, self.latent_dim))
        gen_datas = self.generator.predict(noise)
        # 将生成数据反归一化
        gen_datas = (gen_datas + 1) * 194
        gen_datas = np.expand_dims(gen_datas, axis=2).reshape(200, 1, 400)
        self.data_write_csv(epoch, gen_datas, self.sample_size)

    def test(self, gen_nums=200):
        self.generator.load_weights("dcgan_model/G_model9000.hdf5", by_name=True)
        self.discriminator.load_weights("dcgan_model/D_model9000.hdf5", by_name=True)
        noise = np.random.normal(0, 1, (gen_nums, self.latent_dim))
        gen_datas = self.generator.predict(noise)
        # 将生成的数据反归一化
        gen_datas = (gen_datas + 1) * 192
        print(gen_datas)
        if not os.path.exists("gen_test"):
            os.makedirs("gen_test")
        self.data_write_csv(666, gen_datas, gen_nums)


if __name__ == '__main__':
    gan = GAN()
    gan.train(epochs=2000, batch_size=256, sample_interval=400)
    # gan.test()