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इस उदाहरण में हम दिखाते हैं कि TFP की "संभाव्य परतों" का उपयोग करके एक भिन्न ऑटोएन्कोडर को कैसे फ़िट किया जाए।
निर्भरता और पूर्वापेक्षाएँ
आयात
import numpy as np
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
import tensorflow_datasets as tfds
import tensorflow_probability as tfp
tfk = tf.keras
tfkl = tf.keras.layers
tfpl = tfp.layers
tfd = tfp.distributions
चीजें तेजी से करें!
इससे पहले कि हम इसमें गोता लगाएँ, आइए सुनिश्चित करें कि हम इस डेमो के लिए GPU का उपयोग कर रहे हैं।
ऐसा करने के लिए, "रनटाइम" -> "रनटाइम प्रकार बदलें" -> "हार्डवेयर त्वरक" -> "जीपीयू" चुनें।
निम्नलिखित स्निपेट सत्यापित करेगा कि हमारे पास GPU तक पहुंच है।
if tf.test.gpu_device_name() != '/device:GPU:0':
print('WARNING: GPU device not found.')
else:
print('SUCCESS: Found GPU: {}'.format(tf.test.gpu_device_name()))
SUCCESS: Found GPU: /device:GPU:0
डेटासेट लोड करें
datasets, datasets_info = tfds.load(name='mnist',
with_info=True,
as_supervised=False)
def _preprocess(sample):
image = tf.cast(sample['image'], tf.float32) / 255. # Scale to unit interval.
image = image < tf.random.uniform(tf.shape(image)) # Randomly binarize.
return image, image
train_dataset = (datasets['train']
.map(_preprocess)
.batch(256)
.prefetch(tf.data.AUTOTUNE)
.shuffle(int(10e3)))
eval_dataset = (datasets['test']
.map(_preprocess)
.batch(256)
.prefetch(tf.data.AUTOTUNE))
नोट रिटर्न ऊपर है कि preprocess () image, image के बजाय image क्योंकि Keras एक (उदाहरण के लिए, लेबल) इनपुट प्रारूप, यानी साथ विवेकशील मॉडल के लिए सेट किया गया है \(p\theta(y|x)\)। चूंकि VAE के लक्ष्य एक्स से ही इनपुट x (यानी ठीक करने के लिए है \(p_\theta(x|x)\)), डेटा जोड़ी (उदाहरण के लिए, उदाहरण) है।
वीएई कोड गोल्फ
मॉडल निर्दिष्ट करें।
input_shape = datasets_info.features['image'].shape
encoded_size = 16
base_depth = 32
prior = tfd.Independent(tfd.Normal(loc=tf.zeros(encoded_size), scale=1),
reinterpreted_batch_ndims=1)
encoder = tfk.Sequential([
tfkl.InputLayer(input_shape=input_shape),
tfkl.Lambda(lambda x: tf.cast(x, tf.float32) - 0.5),
tfkl.Conv2D(base_depth, 5, strides=1,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2D(base_depth, 5, strides=2,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2D(2 * base_depth, 5, strides=1,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2D(2 * base_depth, 5, strides=2,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2D(4 * encoded_size, 7, strides=1,
padding='valid', activation=tf.nn.leaky_relu),
tfkl.Flatten(),
tfkl.Dense(tfpl.MultivariateNormalTriL.params_size(encoded_size),
activation=None),
tfpl.MultivariateNormalTriL(
encoded_size,
activity_regularizer=tfpl.KLDivergenceRegularizer(prior)),
])
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py:158: calling LinearOperator.__init__ (from tensorflow.python.ops.linalg.linear_operator) with graph_parents is deprecated and will be removed in a future version. Instructions for updating: Do not pass `graph_parents`. They will no longer be used. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py:158: calling LinearOperator.__init__ (from tensorflow.python.ops.linalg.linear_operator) with graph_parents is deprecated and will be removed in a future version. Instructions for updating: Do not pass `graph_parents`. They will no longer be used.
decoder = tfk.Sequential([
tfkl.InputLayer(input_shape=[encoded_size]),
tfkl.Reshape([1, 1, encoded_size]),
tfkl.Conv2DTranspose(2 * base_depth, 7, strides=1,
padding='valid', activation=tf.nn.leaky_relu),
tfkl.Conv2DTranspose(2 * base_depth, 5, strides=1,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2DTranspose(2 * base_depth, 5, strides=2,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2DTranspose(base_depth, 5, strides=1,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2DTranspose(base_depth, 5, strides=2,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2DTranspose(base_depth, 5, strides=1,
padding='same', activation=tf.nn.leaky_relu),
tfkl.Conv2D(filters=1, kernel_size=5, strides=1,
padding='same', activation=None),
tfkl.Flatten(),
tfpl.IndependentBernoulli(input_shape, tfd.Bernoulli.logits),
])
vae = tfk.Model(inputs=encoder.inputs,
outputs=decoder(encoder.outputs[0]))
अनुमान करो।
negloglik = lambda x, rv_x: -rv_x.log_prob(x)
vae.compile(optimizer=tf.optimizers.Adam(learning_rate=1e-3),
loss=negloglik)
_ = vae.fit(train_dataset,
epochs=15,
validation_data=eval_dataset)
Epoch 1/15 235/235 [==============================] - 14s 61ms/step - loss: 206.5541 - val_loss: 163.1924 Epoch 2/15 235/235 [==============================] - 14s 59ms/step - loss: 151.1891 - val_loss: 143.6748 Epoch 3/15 235/235 [==============================] - 14s 58ms/step - loss: 141.3275 - val_loss: 137.9188 Epoch 4/15 235/235 [==============================] - 14s 58ms/step - loss: 136.7453 - val_loss: 133.2726 Epoch 5/15 235/235 [==============================] - 14s 58ms/step - loss: 132.3803 - val_loss: 131.8343 Epoch 6/15 235/235 [==============================] - 14s 58ms/step - loss: 129.2451 - val_loss: 127.1935 Epoch 7/15 235/235 [==============================] - 14s 59ms/step - loss: 126.0975 - val_loss: 123.6789 Epoch 8/15 235/235 [==============================] - 14s 58ms/step - loss: 124.0565 - val_loss: 122.5058 Epoch 9/15 235/235 [==============================] - 14s 58ms/step - loss: 122.9974 - val_loss: 121.9544 Epoch 10/15 235/235 [==============================] - 14s 58ms/step - loss: 121.7349 - val_loss: 120.8735 Epoch 11/15 235/235 [==============================] - 14s 58ms/step - loss: 121.0856 - val_loss: 120.1340 Epoch 12/15 235/235 [==============================] - 14s 58ms/step - loss: 120.2232 - val_loss: 121.3554 Epoch 13/15 235/235 [==============================] - 14s 58ms/step - loss: 119.8123 - val_loss: 119.2351 Epoch 14/15 235/235 [==============================] - 14s 58ms/step - loss: 119.2685 - val_loss: 118.2133 Epoch 15/15 235/235 [==============================] - 14s 59ms/step - loss: 118.8895 - val_loss: 119.4771
देखो माँ, नहीं हाथ टेंसर!
# We'll just examine ten random digits.
x = next(iter(eval_dataset))[0][:10]
xhat = vae(x)
assert isinstance(xhat, tfd.Distribution)
छवि प्लॉट उपयोग
import matplotlib.pyplot as plt
def display_imgs(x, y=None):
if not isinstance(x, (np.ndarray, np.generic)):
x = np.array(x)
plt.ioff()
n = x.shape[0]
fig, axs = plt.subplots(1, n, figsize=(n, 1))
if y is not None:
fig.suptitle(np.argmax(y, axis=1))
for i in range(n):
axs.flat[i].imshow(x[i].squeeze(), interpolation='none', cmap='gray')
axs.flat[i].axis('off')
plt.show()
plt.close()
plt.ion()
print('Originals:')
display_imgs(x)
print('Decoded Random Samples:')
display_imgs(xhat.sample())
print('Decoded Modes:')
display_imgs(xhat.mode())
print('Decoded Means:')
display_imgs(xhat.mean())
Originals:
Decoded Random Samples:
Decoded Modes:
Decoded Means:
# Now, let's generate ten never-before-seen digits.
z = prior.sample(10)
xtilde = decoder(z)
assert isinstance(xtilde, tfd.Distribution)
print('Randomly Generated Samples:')
display_imgs(xtilde.sample())
print('Randomly Generated Modes:')
display_imgs(xtilde.mode())
print('Randomly Generated Means:')
display_imgs(xtilde.mean())
Randomly Generated Samples:
Randomly Generated Modes:
Randomly Generated Means:
