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DCGAN overriding training step- Keras 3 Migration (Only Tensorflow Backend) #1693

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Jan 4, 2024
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DCGAN overriding training step- Keras 3 Migration (Only Tensorflow Backend) #1693

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4f11140
Migrate to Keras 3
sineeli Dec 21, 2023
75050b8
Keras 3 Migration
sineeli Dec 21, 2023
7ae103b
Merge branch 'keras-team:master' into master
sineeli Dec 27, 2023
4daca88
Add seed generator to keras.random
sineeli Dec 27, 2023
8766536
Train using seed generator
sineeli Dec 27, 2023
5499fce
Merge branch 'keras-team:master' into master
sineeli Dec 30, 2023
b3f8e5d
Update dcgan_overriding_train_step.py
sineeli Dec 30, 2023
2a9b52d
Rgenerate files
sineeli Jan 2, 2024
75c3698
Merge branch 'keras-team:master' into master
sineeli Jan 2, 2024
7b003f5
Migration to Keras 3
sineeli Jan 2, 2024
42e4262
Revert "Migration to Keras 3"
sineeli Jan 2, 2024
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44 changes: 27 additions & 17 deletions examples/generative/dcgan_overriding_train_step.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,22 +2,26 @@
Title: DCGAN to generate face images
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2019/04/29
Last modified: 2021/01/01
Last modified: 2023/12/21
Description: A simple DCGAN trained using `fit()` by overriding `train_step` on CelebA images.
Accelerator: GPU
"""
"""
## Setup
"""

import keras
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

from keras import layers
from keras import ops
import matplotlib.pyplot as plt
import os
import gdown
from zipfile import ZipFile

seed_generator = keras.random.SeedGenerator(42)
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The seed generator must be attached to the layer. So you should do:

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

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Got it


"""
## Prepare CelebA data

Expand Down Expand Up @@ -64,11 +68,11 @@
[
keras.Input(shape=(64, 64, 3)),
layers.Conv2D(64, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.LeakyReLU(negative_slope=0.2),
layers.Flatten(),
layers.Dropout(0.2),
layers.Dense(1, activation="sigmoid"),
Expand All @@ -91,11 +95,11 @@
layers.Dense(8 * 8 * 128),
layers.Reshape((8, 8, 128)),
layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.LeakyReLU(negative_slope=0.2),
layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"),
],
name="generator",
Expand Down Expand Up @@ -128,18 +132,20 @@ def metrics(self):

def train_step(self, real_images):
# Sample random points in the latent space
batch_size = tf.shape(real_images)[0]
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
batch_size = ops.shape(real_images)[0]
random_latent_vectors = keras.random.normal(
shape=(batch_size, self.latent_dim), seed=seed_generator
)

# Decode them to fake images
generated_images = self.generator(random_latent_vectors)

# Combine them with real images
combined_images = tf.concat([generated_images, real_images], axis=0)
combined_images = ops.concatenate([generated_images, real_images], axis=0)

# Assemble labels discriminating real from fake images
labels = tf.concat(
[tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0
labels = ops.concatenate(
[ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0
)
# Add random noise to the labels - important trick!
labels += 0.05 * tf.random.uniform(tf.shape(labels))
Expand All @@ -154,10 +160,12 @@ def train_step(self, real_images):
)

# Sample random points in the latent space
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
random_latent_vectors = keras.random.normal(
shape=(batch_size, self.latent_dim), seed=seed_generator
)

# Assemble labels that say "all real images"
misleading_labels = tf.zeros((batch_size, 1))
misleading_labels = ops.zeros((batch_size, 1))

# Train the generator (note that we should *not* update the weights
# of the discriminator)!
Expand Down Expand Up @@ -187,7 +195,9 @@ def __init__(self, num_img=3, latent_dim=128):
self.latent_dim = latent_dim

def on_epoch_end(self, epoch, logs=None):
random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))
random_latent_vectors = keras.random.normal(
shape=(self.num_img, self.latent_dim), seed=seed_generator
)
generated_images = self.model.generator(random_latent_vectors)
generated_images *= 255
generated_images.numpy()
Expand Down
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48 changes: 29 additions & 19 deletions examples/generative/ipynb/dcgan_overriding_train_step.ipynb
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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:** 2019/04/29<br>\n",
"**Last modified:** 2021/01/01<br>\n",
"**Last modified:** 2023/12/21<br>\n",
"**Description:** A simple DCGAN trained using `fit()` by overriding `train_step` on CelebA images."
]
},
Expand All @@ -31,13 +31,17 @@
},
"outputs": [],
"source": [
"import keras\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"from keras import layers\n",
"from keras import ops\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import gdown\n",
"from zipfile import ZipFile"
"from zipfile import ZipFile\n",
"\n",
"seed_generator = keras.random.SeedGenerator(42)"
]
},
{
Expand Down Expand Up @@ -141,11 +145,11 @@
" [\n",
" keras.Input(shape=(64, 64, 3)),\n",
" layers.Conv2D(64, kernel_size=4, strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.LeakyReLU(negative_slope=0.2),\n",
" layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.LeakyReLU(negative_slope=0.2),\n",
" layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.LeakyReLU(negative_slope=0.2),\n",
" layers.Flatten(),\n",
" layers.Dropout(0.2),\n",
" layers.Dense(1, activation=\"sigmoid\"),\n",
Expand Down Expand Up @@ -182,11 +186,11 @@
" layers.Dense(8 * 8 * 128),\n",
" layers.Reshape((8, 8, 128)),\n",
" layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.LeakyReLU(negative_slope=0.2),\n",
" layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.LeakyReLU(negative_slope=0.2),\n",
" layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.LeakyReLU(negative_slope=0.2),\n",
" layers.Conv2D(3, kernel_size=5, padding=\"same\", activation=\"sigmoid\"),\n",
" ],\n",
" name=\"generator\",\n",
Expand Down Expand Up @@ -233,18 +237,20 @@
"\n",
" def train_step(self, real_images):\n",
" # Sample random points in the latent space\n",
" batch_size = tf.shape(real_images)[0]\n",
" random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n",
" batch_size = ops.shape(real_images)[0]\n",
" random_latent_vectors = keras.random.normal(\n",
" shape=(batch_size, self.latent_dim), seed=seed_generator\n",
" )\n",
"\n",
" # Decode them to fake images\n",
" generated_images = self.generator(random_latent_vectors)\n",
"\n",
" # Combine them with real images\n",
" combined_images = tf.concat([generated_images, real_images], axis=0)\n",
" combined_images = ops.concatenate([generated_images, real_images], axis=0)\n",
"\n",
" # Assemble labels discriminating real from fake images\n",
" labels = tf.concat(\n",
" [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0\n",
" labels = ops.concatenate(\n",
" [ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0\n",
" )\n",
" # Add random noise to the labels - important trick!\n",
" labels += 0.05 * tf.random.uniform(tf.shape(labels))\n",
Expand All @@ -259,10 +265,12 @@
" )\n",
"\n",
" # Sample random points in the latent space\n",
" random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n",
" random_latent_vectors = keras.random.normal(\n",
" shape=(batch_size, self.latent_dim), seed=seed_generator\n",
" )\n",
"\n",
" # Assemble labels that say \"all real images\"\n",
" misleading_labels = tf.zeros((batch_size, 1))\n",
" misleading_labels = ops.zeros((batch_size, 1))\n",
"\n",
" # Train the generator (note that we should *not* update the weights\n",
" # of the discriminator)!\n",
Expand Down Expand Up @@ -306,7 +314,9 @@
" self.latent_dim = latent_dim\n",
"\n",
" def on_epoch_end(self, epoch, logs=None):\n",
" random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))\n",
" random_latent_vectors = keras.random.normal(\n",
" shape=(self.num_img, self.latent_dim), seed=seed_generator\n",
" )\n",
" generated_images = self.model.generator(random_latent_vectors)\n",
" generated_images *= 255\n",
" generated_images.numpy()\n",
Expand Down Expand Up @@ -389,4 +399,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
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