Wir trainieren ein generativ-wettbewerbsfähiges Netzwerk für das Zeichnen von Bildern in Azure ML

Deep Learning sieht manchmal wie pure Magie aus, besonders wenn der Computer lernt, etwas wirklich Kreatives zu tun, zum Beispiel Bilder zu malen! Die dafür verwendete Technologie heißt GAN, ein wettbewerbsfähiges generatives Netzwerk. In diesem Artikel werden wir untersuchen, wie solche Netzwerke organisiert sind und wie sie trainiert werden, um mithilfe von Azure Machine Learning Bilder zu generieren.

Dieser Beitrag ist Teil der AI April- Initiative . Jeden April schreiben meine Microsoft-Kollegen interessante Artikel über KI und maschinelles Lernen. Schauen Sie sich den Kalender an - plötzlich finden Sie dort andere Themen, die Sie interessieren. Die Artikel sind hauptsächlich in englischer Sprache.

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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 :

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• 32x32x16 16x16x32
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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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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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