关于#Python#生成对抗网络代码的问题，如何解决？（相关搜索：python代码|数据集|训练集）

关注 码龄 粉丝数 原力等级 -- 被采纳 被点赞 采纳率 SilyaSophie 2024-04-01 22:15 采纳率: 60% 浏览 6 首页/ 编程语言 / 关于#Python#生成对抗网络代码的问题，如何解决？（相关搜索：python代码|数据集|训练集） pythonkeras生成对抗网络 代码如下： from __future__ import print_function, division from keras.datasets import mnist from keras.layers import Input, Dense, Reshape, Flatten, Conv1D,GRU,Dropout,InputLayer from keras.layers import BatchNormalization, Activation, ZeroPadding2D from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam import matplotlib.pyplot as plt import sys import numpy as np import pandas as pd import import2023 class GAN: def __init__(self): self.img_rows = 28 self.img_cols = 28 self.channels = 1 self.img_shape = (self.img_rows, self.img_cols, self.channels) self.latent_dim = 100 optimizer = Adam(0.0002, 0.5) # Build and compile the discriminator self.discriminator = self.build_discriminator() self.discriminator.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) # Build the generator self.generator = self.build_generator() # The generator takes noise as input and generates imgs z = Input(shape=(self.latent_dim,)) img = self.generator(z) # For the combined model we will only train the generator self.discriminator.trainable = False # The discriminator takes generated images as input and determines validity validity = self.discriminator(img) # The combined model (stacked generator and discriminator) # Trains the generator to fool the discriminator self.combined = Model(z, validity) self.combined.compile(loss='mse', optimizer=optimizer) def build_generator(self): model = Sequential() model.add(GRU(256, input_shape=self.img_shape, activation='relu')) #model.add(Dense(256, input_dim=self.latent_dim)) model.add(Dense(512, activation='relu')) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(Dense(512, activation='relu')) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(1024)) model.add(Dense(1024, activation='relu')) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() noise = Input(shape=(self.latent_dim,1,1)) img = model(noise) return Model(noise,img) def build_discriminator(self): model = Sequential() #model.add(Flatten(input_shape=self.img_shape)) model.add(InputLayer(input_shape=(32, 64))) #model.add(Dense(256,input_shape=self.img_shape,activation='relu')) #Conv2D(filters, kernel_size, data_format='NHWC') model.add(Conv1D(1024, kernel_size=3, strides=2, padding='same', data_format='channels_first',activation='relu')) model.add(Dense(512)) model.add(Dense(512, activation='relu')) model.add(Dense(256)) model.add(Dense(64, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img,validity) def train(self, epochs, batch_size=128, sample_interval=50): # Load the dataset # csv文件路径 csv_path_train = 'E:/dataset/CICIoT2023/benign.csv' # 读取数据 X_train= pd.read_csv(csv_path_train) # Rescale -1 to 1 X_train = X_train / 127.5 - 1. #X_train = np.expand_dims(X_train, axis=3) X_train = np.reshape(X_train, (-1, 100, 46)) print(X_train.shape) # Adversarial ground truths valid = np.ones((batch_size, 1)) fake = np.zeros((batch_size, 1)) print(valid.shape) print(fake.shape) for epoch in range(epochs): # --------------------- # Train Discriminator # --------------------- # Select a random batch of images idx = np.random.randint(0, X_train.shape[0], batch_size) imgs = X_train[idx] print(imgs.shape) noise = np.random.normal(0, 1, (batch_size, self.latent_dim,1)) print(noise.shape) # Generate a batch of new images gen_imgs = self.generator.predict(noise) # Train the discriminator d_loss_real = self.discriminator.train_on_batch(imgs, valid) d_loss_fake = self.discriminator.train_on_batch(gen_imgs, 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,1)) # Train the generator (to have the discriminator label samples as valid) g_loss = self.combined.train_on_batch(noise, valid) # Plot the progress print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss)) # # If at save interval => save generated image samples # if epoch % sample_interval == 0: # self.sample_images(epoch) if __name__ == '__main__': gan = GAN() gan.train(epochs=30000, batch_size=32, sample_interval=200) 运行结果及报错： 展开全部 收起 写回答 好问题 0 提建议 追加酬金 关注问题 微信扫一扫 点击复制链接 分享 邀请回答 编辑 收藏 删除 结题 收藏 举报 追加酬金 (90%的用户在追加酬金后获得了解决方案) 当前问题酬金 ¥ 0 (可追加 ¥500） ¥ 15¥ 20¥ 50¥ 100¥ 200 支付方式 扫码支付 二维码出错 点击刷新 支付金额 15 元 提供问题酬金的用户不参与问题酬金结算和分配 支付即为同意 《付费问题酬金结算规则》 结题 再想想 删除 再等等