Kami melatih jaringan kompetitif-generatif untuk menggambar di Azure ML

Pembelajaran mendalam kadang-kadang terlihat seperti sihir murni, terutama ketika komputer belajar melakukan sesuatu yang benar-benar kreatif, misalnya melukis gambar! Teknologi yang digunakan untuk ini disebut GAN, jaringan generatif yang kompetitif, dan dalam artikel ini kita akan melihat bagaimana jaringan tersebut diatur dan bagaimana melatih mereka untuk menghasilkan gambar menggunakan Azure Machine Learning.

Posting ini adalah bagian dari inisiatif AI April . Setiap hari di bulan April, kolega Microsoft saya menulis artikel menarik tentang AI dan pembelajaran mesin. Lihatlah kalender - tiba-tiba Anda akan menemukan ada topik lain yang menarik bagi Anda. Artikel terutama dalam bahasa Inggris.

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GAN :

1. :
   

   noise = np.random.normal(0, 1, (batch_size, latent_dim))
   gen_imgs = generator.predict(noise)
   imgs = get_batch(batch_size)

2. . ones zeros :
   

   d_loss_r = discriminator.train_on_batch(imgs, ones)
   d_loss_f = discriminator.train_on_batch(gen_imgs, zeros)
   d_loss = np.add(d_loss_r , d_loss_f)*0.5

3. , , :
   

   g_loss = combined.train_on_batch(noise, ones)

, — :

discriminator = create_discriminator()
generator = create_generator()
discriminator.compile(loss='binary_crossentropy',optimizer=optimizer, 
                      metrics=['accuracy'])
discriminator.trainable = False
z = keras.models.Input(shape=(latent_dim,))
img = generator(z)
valid = discriminator(img)
combined = keras.models.Model(z, valid)
combined.compile(loss='binary_crossentropy', optimizer=optimizer)

, (CNN). 64x64 :

discriminator = Sequential()

for x in [16,32,64]: # number of filters on next layer
    discriminator.add(Conv2D(x, (3,3), strides=1, padding="same"))
    discriminator.add(AveragePooling2D())
    discriminator.addBatchNormalization(momentum=0.8))
    discriminator.add(LeakyReLU(alpha=0.2))
    discriminator.add(Dropout(0.3))

discriminator.add(Flatten())
discriminator.add(Dense(1, activation='sigmoid'))

3 :

• 64x64x3 16- , ( AveragePooling2D ) 32x32x16.
• 32x32x16 16x16x32
• 8x8x64.

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. , , — latent_dim=100. , , 100..

— 100 . . UpSampling2D , :

generator = Sequential()
generator.add(Dense(8 * 8 * 2 * size, activation="relu", 
                                      input_dim=latent_dim))
generator.add(Reshape((8, 8, 2 * size)))

for x in [64;32;16]:
    generator.add(UpSampling2D())
    generator.add(Conv2D(x, kernel_size=(3,3),strides=1,padding="same"))
    generator.add(BatchNormalization(momentum=0.8))
    generator.add(Activation("relu"))

generator.add(Conv2D(3, kernel_size=3, padding="same"))
generator.add(Activation("tanh"))

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def callbk(tr):
    if tr.gan.epoch % 20 == 0:
        res = tr.gan.sample_images(n=3)
        fig,ax = plt.subplots(1,len(res))
        for i,v in enumerate(res):
            ax[i].imshow(v[0])
        run.log_image("Sample",plot=plt)

:

gan = keragan.DCGAN(args)
imsrc = keragan.ImageDataset(args)
imsrc.load()
train = keragan.GANTrainer(image_dataset=imsrc,gan=gan,args=args)

train.train(callbk)

, keragan , args, , , , learning rate ..

Azure ML VS Code, , SDK, Azure ML. submit_gan.ipynb, :

• : ws = Workspace.from_config()
• : cluster = ComputeTarget(workspace=ws, name='My Cluster'). GPU, [NC6][AzureVMNC].
• : ds.upload(...).

, , :

exp = Experiment(workspace=ws, name='KeraGAN')
script_params = {
    '--path': ws.get_default_datastore(),
    '--dataset' : 'faces',
    '--model_path' : './outputs/models',
    '--samples_path' : './outputs/samples',
    '--batch_size' : 32,
    '--size' : 512,
    '--learning_rate': 0.0001,
    '--epochs' : 10000
}
est = TensorFlow(source_directory='.',
    script_params=script_params,
    compute_target=cluster,
    entry_script='train_gan.py',
    use_gpu = True,
    conda_packages=['keras','tensorflow','opencv','tqdm','matplotlib'],
    pip_packages=['git+https://github.com/shwars/keragan@v0.0.1']

run = exp.submit(est)

model_path=./outputs/models samples_path=./outputs/samples, ( ) outputs. , , Azure ML.

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• --learning_rate . , .
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, , . outputs/models, — outputs/samples. Azure ML Portal :

, , . run, , , ( ):

run.download_files(prefix='outputs/samples')

outputs/samples, .

run ( , ), , run id, :

run = Run(experiment=exp,run_id='KeraGAN_1584082108_356cf603')

. , , . , ( gen_):

fnames = run.get_file_names()
fnames = filter(lambda x : x.startswith('outputs/models/gen_'),fnames)

: outputs/models/gen_0.h5, outputs/models/gen_100.h5 .. :

no = max(map(lambda x: int(x[19:x.find('.')]), fnames))
fname = 'outputs/models/gen_{}.h5'.format(no)
fname_wout_path = fname[fname.rfind('/')+1:]
run.download_file(fname)

, fname_wout_path.

, Keras, , , , :

model = keras.models.load_model(fname_wout_path)
latent_dim=model.layers[0].input.shape[1].value
res = model.predict(np.random.normal(0,1,(10,latent_dim)))

, [-1,1], [0,1], matplotlib:

res = (res+1.0)/2
fig,ax = plt.subplots(1,10,figsize=(15,10))
for i in range(10):
    ax[i].imshow(res[i])

:

, :

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