2

我是一般的生成对抗网络（GAN）和神经网络的新手。使用Python和Keras，我想将 GAN 应用于时间序列预测。我的最终目标还包括检测时间序列中的异常。

我正在使用流行的Air-Passangers时间序列数据。

这是我用于时间序列预测的代码。然而，我使用 GAN 得到的结果对我来说有点难以解释，我认为它需要一些改进。

谢谢你的帮助。

from __future__ import print_function, division

from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
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 matplotlib.pyplot as plt

from matplotlib import pyplot as plt

import pandas as pd

import sys

import numpy as np

class GAN():
    def __init__(self):
        self.data_rows = 1
        self.data_cols = 1
        self.data_shape = (self.data_rows, self.data_cols)
        self.latent_dim = 48

        optimizer = Adam(0.0002, 0.5)

        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
            optimizer=optimizer,
            metrics=['accuracy'])

        self.generator = self.build_generator()

        z = Input(shape=(self.latent_dim,))
        data = self.generator(z)
        self.discriminator.trainable = False

        validity = self.discriminator(data)
        self.combined = Model(z, validity)
        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)


    def build_generator(self):

        model = Sequential()

        model.add(Dense(256, input_dim=self.latent_dim))
        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(np.prod(self.data_shape), activation='linear'))
        model.add(Reshape(self.data_shape))

        model.summary()

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

        return Model(noise, data)

    def build_discriminator(self):

        model = Sequential()

        model.add(Flatten(input_shape=self.data_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))
        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=50):

        df = pd.read_csv("AirPassengers.csv")
        ts = df[["#Passengers"]]
        X_train = ts.as_matrix()

        # Rescale -1 to 1
        #X_train = X_train / 127.5 - 1.

        X_train = np.expand_dims(X_train, axis=3)

        print("X_train")
        print(X_train.shape)
        #print(X_train)

        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):

            idx = np.random.randint(0, X_train.shape[0], batch_size)
            data_s = X_train[idx]

            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

            gen_data = self.generator.predict(noise)


            d_loss_real = self.discriminator.train_on_batch(data_s, valid)
            d_loss_fake = self.discriminator.train_on_batch(gen_data, fake)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

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

            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)

            print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))


            if epoch % sample_interval == 0:
                self.plot_gan_result(epoch, batch_size)
                c = X_train.reshape(144, 1)
                fig, axs = plt.subplots()
                axs.plot(c, color = "blue", label = 'true')


    def plot_gan_result(self, epoch, batch_size):
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
            gen_data = self.generator.predict(noise)

            b = gen_data.reshape(24, 1)
            fig, axs = plt.subplots()
            print("noise shape")
            print(noise.shape)
            print(noise[0])
            axs.plot(b, color = "red", label = 'generated')

if __name__ == '__main__':
    gan = GAN()
    gan.train(epochs=30, batch_size=24, sample_interval=200)
4

0 回答 0