Mac

# 数据集
~/.keras/datasets/

# 模型
~/.keras/models/

Linux

# 数据集
~/.keras/datasets/

Windows

# win10
C:\Users\user_name\.keras\datasets

补充知识：Keras_gan生成自己的数据，并保存模型

我就废话不多说了，大家还是直接看代码吧~

from __future__ import print_function, division from keras.datasets import mnistfrom keras.layers import Input, Dense, Reshape, Flatten, Dropoutfrom keras.layers import BatchNormalization, Activation, ZeroPadding2Dfrom keras.layers.advanced_activations import LeakyReLUfrom keras.layers.convolutional import UpSampling2D, Conv2Dfrom keras.models import Sequential, Modelfrom keras.optimizers import Adamimport osimport matplotlib.pyplot as pltimport sysimport numpy as np class GAN(): def __init__(self): self.img_rows = 3 self.img_cols = 60 self.channels = 1 self.img_shape = (self.img_rows, self.img_cols, self.channels) self.latent_dim = 100 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,)) img = self.generator(z) # 为了组合模型，只训练生成器 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(img) # 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() model.add(Dense(64, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(128)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(256)) 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)) #np.prod(self.img_shape)=3x60x1 model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) #输入噪音，输出图片 return Model(noise, img) def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(128)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(64)) model.add(LeakyReLU(alpha=0.2)) 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): ############################################################ #自己数据集此部分需要更改 # 加载数据集 data = np.load('data/相对大小分叉.npy') data = data[:,:,0:60] # 归一化到-1到1 data = data * 2 - 1 data = np.expand_dims(data, 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 # --------------------- # data.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 imgs = 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_imgs = 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(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) # --------------------- # 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) # 打印loss值 print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss)) # 没sample_interval个epoch保存一次生成图片 if epoch % sample_interval == 0: self.sample_images(epoch) if not os.path.exists("keras_model"): os.makedirs("keras_model") self.generator.save_weights("keras_model/G_model%d.hdf5" % epoch,True) self.discriminator.save_weights("keras_model/D_model%d.hdf5" %epoch,True) def sample_images(self, epoch): r, c = 10, 10 # 重新生成一批噪音，维度为(100,100) noise = np.random.normal(0, 1, (r * c, self.latent_dim)) gen_imgs = self.generator.predict(noise) # 将生成的图片重新归整到0-1之间 gen = 0.5 * gen_imgs + 0.5 gen = gen.reshape(-1,3,60) fig,axs = plt.subplots(r,c) cnt = 0 for i in range(r): for j in range(c): xy = gen[cnt] for k in range(len(xy)): x = xy[k][0:30] y = xy[k][30:60] if k == 0: axs[i,j].plot(x,y,color='blue') if k == 1: axs[i,j].plot(x,y,color='red') if k == 2: axs[i,j].plot(x,y,color='green') plt.xlim(0.,1.) plt.ylim(0.,1.) plt.xticks(np.arange(0,1,0.1)) plt.xticks(np.arange(0,1,0.1)) axs[i,j].axis('off') cnt += 1 if not os.path.exists("keras_imgs"): os.makedirs("keras_imgs") fig.savefig("keras_imgs/%d.png" % epoch) plt.close() def test(self,gen_nums=100,save=False): self.generator.load_weights("keras_model/G_model4000.hdf5",by_name=True) self.discriminator.load_weights("keras_model/D_model4000.hdf5",by_name=True) noise = np.random.normal(0,1,(gen_nums,self.latent_dim)) gen = self.generator.predict(noise) gen = 0.5 * gen + 0.5 gen = gen.reshape(-1,3,60) print(gen.shape) ############################################################### #直接可视化生成图片 if save: for i in range(0,len(gen)): plt.figure(figsize=(128,128),dpi=1) plt.plot(gen[i][0][0:30],gen[i][0][30:60],color='blue',linewidth=300) plt.plot(gen[i][1][0:30],gen[i][1][30:60],color='red',linewidth=300) plt.plot(gen[i][2][0:30],gen[i][2][30:60],color='green',linewidth=300) plt.axis('off') plt.xlim(0.,1.) plt.ylim(0.,1.) plt.xticks(np.arange(0,1,0.1)) plt.yticks(np.arange(0,1,0.1)) if not os.path.exists("keras_gen"): os.makedirs("keras_gen") plt.savefig("keras_gen"+os.sep+str(i)+'.jpg',dpi=1) plt.close() ################################################################## #重整图片到0-1 else: for i in range(len(gen)): plt.plot(gen[i][0][0:30],gen[i][0][30:60],color='blue') plt.plot(gen[i][1][0:30],gen[i][1][30:60],color='red') plt.plot(gen[i][2][0:30],gen[i][2][30:60],color='green') plt.xlim(0.,1.) plt.ylim(0.,1.) plt.xticks(np.arange(0,1,0.1)) plt.xticks(np.arange(0,1,0.1)) plt.show() if __name__ == '__main__': gan = GAN() gan.train(epochs=300000, batch_size=32, sample_interval=2000)# gan.test(save=True)

以上这篇Keras自动下载的数据集/模型存放位置介绍就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。