Nous formons un réseau compétitif génératif pour dessiner des images sur Azure ML

L'apprentissage en profondeur ressemble parfois à de la pure magie, surtout lorsque l'ordinateur apprend à faire quelque chose de vraiment créatif, par exemple, pour peindre des images! La technologie utilisée pour cela s'appelle GAN, un réseau génératif compétitif, et dans cet article, nous verrons comment ces réseaux sont organisés et comment les former à générer des images à l'aide d'Azure Machine Learning.

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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 :

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

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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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, , . 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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