TFP 概率层：变分自动编码器

在 TensorFlow.org 上查看

在 Google Colab 中运行

在 GitHub 上查看源代码

下载笔记本

在本示例中，我们将展示如何使用 TFP 的“概率层”拟合变分自动编码器。

依赖项和先决条件

导入

import numpy as np

import tensorflow as tf
import tf_keras as tfk
import tensorflow_datasets as tfds
import tensorflow_probability as tfp


tfkl = tf_keras.layers
tfpl = tfp.layers
tfd = tfp.distributions

加速！

在深入研究之前，让我们确保我们正在使用 GPU 进行此演示。

为此，请选择“运行时”->“更改运行时类型”->“硬件加速器”->“GPU”。

以下代码段将验证我们是否可以访问 GPU。

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

加载数据集

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))

请注意，上面的 preprocess() 返回 image, image 而不是仅 image，因为 Keras 是为具有 (示例，标签) 输入格式的判别模型而设置的，即 \(p\theta(y|x)\)。由于 VAE 的目标是从 x 本身恢复输入 x（即 \(p_\theta(x|x)\)），因此数据对为 (示例，示例)。

VAE 代码高尔夫

指定模型。

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]))

进行推理。

negloglik = lambda x, rv_x: -rv_x.log_prob(x)

vae.compile(optimizer=tf_keras.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

看，没有手张量！

# We'll just examine ten random digits.
x = next(iter(eval_dataset))[0][:10]
xhat = vae(x)
assert isinstance(xhat, tfd.Distribution)

图像绘图实用程序

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:

png

Decoded Random Samples:

png

Decoded Modes:

png

Decoded Means:

png

# 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:

png

Randomly Generated Modes:

png

Randomly Generated Means:

png