Skip to content

Updated Variational AutoEncoder example for Keras 3 #1836

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Apr 24, 2024
Merged

Updated Variational AutoEncoder example for Keras 3 #1836

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
dd15473
vae keras 3 example updated
sitammeur Apr 16, 2024
0e376da
seed generator added to rng layer
sitammeur Apr 19, 2024
c95284d
generated files are added
sitammeur Apr 23, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
45 changes: 24 additions & 21 deletions examples/generative/ipynb/vae.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
"\n",
"**Author:** [fchollet](https://twitter.com/fchollet)<br>\n",
"**Date created:** 2020/05/03<br>\n",
"**Last modified:** 2023/11/22<br>\n",
"**Last modified:** 2024/04/24<br>\n",
"**Description:** Convolutional Variational AutoEncoder (VAE) trained on MNIST digits."
]
},
Expand All @@ -25,7 +25,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand All @@ -38,6 +38,7 @@
"import numpy as np\n",
"import tensorflow as tf\n",
"import keras\n",
"from keras import ops\n",
"from keras import layers"
]
},
Expand All @@ -52,7 +53,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand All @@ -62,13 +63,16 @@
"class Sampling(layers.Layer):\n",
" \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n",
"\n",
" def __init__(self, **kwargs):\n",
" super().__init__(**kwargs)\n",
" self.seed_generator = keras.random.SeedGenerator(1337)\n",
"\n",
" def call(self, inputs):\n",
" z_mean, z_log_var = inputs\n",
" batch = tf.shape(z_mean)[0]\n",
" dim = tf.shape(z_mean)[1]\n",
" epsilon = tf.random.normal(shape=(batch, dim))\n",
" return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n",
""
" batch = ops.shape(z_mean)[0]\n",
" dim = ops.shape(z_mean)[1]\n",
" epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)\n",
" return z_mean + ops.exp(0.5 * z_log_var) * epsilon\n"
]
},
{
Expand All @@ -82,7 +86,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand Down Expand Up @@ -113,7 +117,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand All @@ -140,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand Down Expand Up @@ -170,14 +174,14 @@
" with tf.GradientTape() as tape:\n",
" z_mean, z_log_var, z = self.encoder(data)\n",
" reconstruction = self.decoder(z)\n",
" reconstruction_loss = tf.reduce_mean(\n",
" tf.reduce_sum(\n",
" reconstruction_loss = ops.mean(\n",
" ops.sum(\n",
" keras.losses.binary_crossentropy(data, reconstruction),\n",
" axis=(1, 2),\n",
" )\n",
" )\n",
" kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n",
" kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\n",
" kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var))\n",
" kl_loss = ops.mean(ops.sum(kl_loss, axis=1))\n",
" total_loss = reconstruction_loss + kl_loss\n",
" grads = tape.gradient(total_loss, self.trainable_weights)\n",
" self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
Expand All @@ -188,8 +192,7 @@
" \"loss\": self.total_loss_tracker.result(),\n",
" \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
" \"kl_loss\": self.kl_loss_tracker.result(),\n",
" }\n",
""
" }\n"
]
},
{
Expand All @@ -203,7 +206,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand All @@ -229,7 +232,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand Down Expand Up @@ -286,7 +289,7 @@
},
{
"cell_type": "code",
"execution_count": 0,
"execution_count": null,
"metadata": {
"colab_type": "code"
},
Expand Down Expand Up @@ -340,4 +343,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
23 changes: 14 additions & 9 deletions examples/generative/md/vae.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2020/05/03<br>
**Last modified:** 2023/11/22<br>
**Last modified:** 2024/04/24<br>
**Description:** Convolutional Variational AutoEncoder (VAE) trained on MNIST digits.


Expand All @@ -22,6 +22,7 @@ os.environ["KERAS_BACKEND"] = "tensorflow"
import numpy as np
import tensorflow as tf
import keras
from keras import ops
from keras import layers
```

Expand All @@ -34,12 +35,16 @@ from keras import layers
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""

def __init__(self, **kwargs):
super().__init__(**kwargs)
self.seed_generator = keras.random.SeedGenerator(1337)

def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.random.normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
batch = ops.shape(z_mean)[0]
dim = ops.shape(z_mean)[1]
epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)
return z_mean + ops.exp(0.5 * z_log_var) * epsilon

```

Expand Down Expand Up @@ -204,14 +209,14 @@ class VAE(keras.Model):
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.reduce_sum(
reconstruction_loss = ops.mean(
ops.sum(
keras.losses.binary_crossentropy(data, reconstruction),
axis=(1, 2),
)
)
kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var))
kl_loss = ops.mean(ops.sum(kl_loss, axis=1))
total_loss = reconstruction_loss + kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
Expand Down
23 changes: 14 additions & 9 deletions examples/generative/vae.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
Title: Variational AutoEncoder
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2020/05/03
Last modified: 2023/11/22
Last modified: 2024/04/24
Description: Convolutional Variational AutoEncoder (VAE) trained on MNIST digits.
Accelerator: GPU
"""
Expand All @@ -18,6 +18,7 @@
import numpy as np
import tensorflow as tf
import keras
from keras import ops
from keras import layers

"""
Expand All @@ -28,12 +29,16 @@
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""

def __init__(self, **kwargs):
super().__init__(**kwargs)
self.seed_generator = keras.random.SeedGenerator(1337)

def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.random.normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
batch = ops.shape(z_mean)[0]
dim = ops.shape(z_mean)[1]
epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)
return z_mean + ops.exp(0.5 * z_log_var) * epsilon


"""
Expand Down Expand Up @@ -94,14 +99,14 @@ def train_step(self, data):
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.reduce_sum(
reconstruction_loss = ops.mean(
ops.sum(
keras.losses.binary_crossentropy(data, reconstruction),
axis=(1, 2),
)
)
kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var))
kl_loss = ops.mean(ops.sum(kl_loss, axis=1))
total_loss = reconstruction_loss + kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
Expand Down