View file src/colab/tf_probabilistic_layers_vae.py - Download
# -*- coding: utf-8 -*-
"""Copie de Probabilistic Layers VAE
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1jM-CEYHNqCkqcci_U_I3pn0UVqnVnaFk
https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_VAE.ipynb
##### Copyright 2019 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
"""
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""# TFP Probabilistic Layers: Variational Auto Encoder
In this example we show how to fit a Variational Autoencoder using TFP's "probabilistic layers."
### Dependencies & Prerequisites
"""
#@title Import { display-mode: "form" }
import numpy as np
import tensorflow as tf
import tf_keras as tfk
import tensorflow_datasets as tfds
import tensorflow_probability as tfp
tfkl = tfk.layers
tfpl = tfp.layers
tfd = tfp.distributions
"""### Make things Fast!
Before we dive in, let's make sure we're using a GPU for this demo.
To do this, select "Runtime" -> "Change runtime type" -> "Hardware accelerator" -> "GPU".
The following snippet will verify that we have access to a 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()))
"""Note: if for some reason you cannot access a GPU, this colab will still work. (Training will just take longer.)
### Load Dataset
"""
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))
"""Note that _preprocess() above returns `image, image` rather than just `image` because Keras is set up for discriminative models with an (example, label) input format, i.e. $p_\theta(y|x)$. Since the goal of the VAE is to recover the input x from x itself (i.e. $p_\theta(x|x)$), the data pair is (example, example).
### VAE Code Golf
#### Specify model.
"""
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)),
])
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]))
"""#### Do inference."""
negloglik = lambda x, rv_x: -rv_x.log_prob(x)
vae.compile(optimizer=tfk.optimizers.Adam(learning_rate=1e-3),
loss=negloglik)
_ = vae.fit(train_dataset,
epochs=15,
validation_data=eval_dataset)
"""### Look Ma, No ~~Hands~~Tensors!"""
# We'll just examine ten random digits.
x = next(iter(eval_dataset))[0][:10]
xhat = vae(x)
assert isinstance(xhat, tfd.Distribution)
#@title Image Plot Util
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())
# 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())
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