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<div class='k-main'> <div class='k-main-top'> <script> function displayDropdownMenu() { e = document.getElementById("nav-menu"); if (e.style.display == "block") { e.style.display = "none"; } else { e.style.display = "block"; document.getElementById("dropdown-nav").style.display = "block"; } } function resetMobileUI() { if (window.innerWidth <= 840) { document.getElementById("nav-menu").style.display = "none"; document.getElementById("dropdown-nav").style.display = "block"; } else { document.getElementById("nav-menu").style.display = "block"; document.getElementById("dropdown-nav").style.display = "none"; } var navmenu = document.getElementById("nav-menu"); var menuheight = navmenu.clientHeight; var kmain = document.getElementById("k-main-id"); kmain.style.minHeight = (menuheight + 100) + 'px'; } window.onresize = resetMobileUI; window.addEventListener("load", (event) => { resetMobileUI() }); </script> <div id='dropdown-nav' onclick="displayDropdownMenu();"> <svg viewBox="-20 -20 120 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2019/04/29 Last modified: 2023/12/21 Description: A simple DCGAN trained using <code>fit()</code> by overriding <code>train_step</code> on CelebA images. <div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div> <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/generative/ipynb/dcgan_overriding_train_step.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/generative/dcgan_overriding_train_step.py">GitHub source</a> <hr /> <h2 id="setup">Setup</h2> <div class="codehilite"><pre><code>import keras import tensorflow as tf from keras import layers from keras import ops import matplotlib.pyplot as plt import os import gdown from zipfile import ZipFile </code></pre></div> <hr /> <h2 id="prepare-celeba-data">Prepare CelebA data</h2> We'll use face images from the CelebA dataset, resized to 64x64. <div class="codehilite"><pre><code>os.makedirs("celeba_gan") url = "https://drive.google.com/uc?id=1O7m1010EJjLE5QxLZiM9Fpjs7Oj6e684" output = "celeba_gan/data.zip" gdown.download(url, output, quiet=True) with ZipFile("celeba_gan/data.zip", "r") as zipobj: zipobj.extractall("celeba_gan") </code></pre></div> Create a dataset from our folder, and rescale the images to the [0-1] range: <div class="codehilite"><pre><code>dataset = keras.utils.image_dataset_from_directory( "celeba_gan", label_mode=None, image_size=(64, 64), batch_size=32 ) dataset = dataset.map(lambda x: x / 255.0) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Found 202599 files. </code></pre></div> </div> Let's display a sample image: <div class="codehilite"><pre><code>for x in dataset: plt.axis("off") plt.imshow((x.numpy() * 255).astype("int32")[0]) break </code></pre></div> <img alt="png" src="/img/examples/generative/dcgan_overriding_train_step/dcgan_overriding_train_step_8_0.png" /> <hr /> <h2 id="create-the-discriminator">Create the discriminator</h2> It maps a 64x64 image to a binary classification score. <div class="codehilite"><pre><code>discriminator = keras.Sequential( [ keras.Input(shape=(64, 64, 3)), layers.Conv2D(64, kernel_size=4, strides=2, padding="same"), layers.LeakyReLU(negative_slope=0.2), layers.Conv2D(128, kernel_size=4, strides=2, padding="same"), layers.LeakyReLU(negative_slope=0.2), layers.Conv2D(128, kernel_size=4, strides=2, padding="same"), layers.LeakyReLU(negative_slope=0.2), layers.Flatten(), layers.Dropout(0.2), layers.Dense(1, activation="sigmoid"), ], name="discriminator", ) discriminator.summary() </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "discriminator" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 32, 32, 64) │ 3,136 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ leaky_re_lu (LeakyReLU) │ (None, 32, 32, 64) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_1 (Conv2D) │ (None, 16, 16, 128) │ 131,200 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ leaky_re_lu_1 (LeakyReLU) │ (None, 16, 16, 128) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_2 (Conv2D) │ (None, 8, 8, 128) │ 262,272 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ leaky_re_lu_2 (LeakyReLU) │ (None, 8, 8, 128) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ flatten (Flatten) │ (None, 8192) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dropout (Dropout) │ (None, 8192) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense (Dense) │ (None, 1) │ 8,193 │ └─────────────────────────────────┴───────────────────────────┴────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 404,801 (1.54 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 404,801 (1.54 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 0 (0.00 B) </pre> <hr /> <h2 id="create-the-generator">Create the generator</h2> It mirrors the discriminator, replacing <code>Conv2D</code> layers with <code>Conv2DTranspose</code> layers. <div class="codehilite"><pre><code>latent_dim = 128 generator = keras.Sequential( [ keras.Input(shape=(latent_dim,)), layers.Dense(8 * 8 * 128), layers.Reshape((8, 8, 128)), layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding="same"), layers.LeakyReLU(negative_slope=0.2), layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"), layers.LeakyReLU(negative_slope=0.2), layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"), layers.LeakyReLU(negative_slope=0.2), layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"), ], name="generator", ) generator.summary() </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "generator" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ dense_1 (Dense) │ (None, 8192) │ 1,056,768 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ reshape (Reshape) │ (None, 8, 8, 128) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_transpose │ (None, 16, 16, 128) │ 262,272 │ │ (Conv2DTranspose) │ │ │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ leaky_re_lu_3 (LeakyReLU) │ (None, 16, 16, 128) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_transpose_1 │ (None, 32, 32, 256) │ 524,544 │ │ (Conv2DTranspose) │ │ │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ leaky_re_lu_4 (LeakyReLU) │ (None, 32, 32, 256) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_transpose_2 │ (None, 64, 64, 512) │ 2,097,664 │ │ (Conv2DTranspose) │ │ │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ leaky_re_lu_5 (LeakyReLU) │ (None, 64, 64, 512) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_3 (Conv2D) │ (None, 64, 64, 3) │ 38,403 │ └─────────────────────────────────┴───────────────────────────┴────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 3,979,651 (15.18 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 3,979,651 (15.18 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 0 (0.00 B) </pre> <hr /> <h2 id="override-trainstep">Override <code>train_step</code></h2> <div class="codehilite"><pre><code>class GAN(keras.Model): def __init__(self, discriminator, generator, latent_dim): super().__init__() self.discriminator = discriminator self.generator = generator self.latent_dim = latent_dim self.seed_generator = keras.random.SeedGenerator(1337) def compile(self, d_optimizer, g_optimizer, loss_fn): super().compile() self.d_optimizer = d_optimizer self.g_optimizer = g_optimizer self.loss_fn = loss_fn self.d_loss_metric = keras.metrics.Mean(name="d_loss") self.g_loss_metric = keras.metrics.Mean(name="g_loss") @property def metrics(self): return [self.d_loss_metric, self.g_loss_metric] def train_step(self, real_images): # Sample random points in the latent space batch_size = ops.shape(real_images)[0] random_latent_vectors = keras.random.normal( shape=(batch_size, self.latent_dim), seed=self.seed_generator ) # Decode them to fake images generated_images = self.generator(random_latent_vectors) # Combine them with real images combined_images = ops.concatenate([generated_images, real_images], axis=0) # Assemble labels discriminating real from fake images 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)) # Train the discriminator with tf.GradientTape() as tape: predictions = self.discriminator(combined_images) d_loss = self.loss_fn(labels, predictions) grads = tape.gradient(d_loss, self.discriminator.trainable_weights) self.d_optimizer.apply_gradients( zip(grads, self.discriminator.trainable_weights) ) # Sample random points in the latent space random_latent_vectors = keras.random.normal( shape=(batch_size, self.latent_dim), seed=self.seed_generator ) # Assemble labels that say "all real images" misleading_labels = ops.zeros((batch_size, 1)) # Train the generator (note that we should *not* update the weights # of the discriminator)! with tf.GradientTape() as tape: predictions = self.discriminator(self.generator(random_latent_vectors)) g_loss = self.loss_fn(misleading_labels, predictions) grads = tape.gradient(g_loss, self.generator.trainable_weights) self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights)) # Update metrics self.d_loss_metric.update_state(d_loss) self.g_loss_metric.update_state(g_loss) return { "d_loss": self.d_loss_metric.result(), "g_loss": self.g_loss_metric.result(), } </code></pre></div> <hr /> <h2 id="create-a-callback-that-periodically-saves-generated-images">Create a callback that periodically saves generated images</h2> <div class="codehilite"><pre><code>class GANMonitor(keras.callbacks.Callback): def __init__(self, num_img=3, latent_dim=128): self.num_img = num_img self.latent_dim = latent_dim self.seed_generator = keras.random.SeedGenerator(42) def on_epoch_end(self, epoch, logs=None): random_latent_vectors = keras.random.normal( shape=(self.num_img, self.latent_dim), seed=self.seed_generator ) generated_images = self.model.generator(random_latent_vectors) generated_images *= 255 generated_images.numpy() for i in range(self.num_img): img = keras.utils.array_to_img(generated_images[i]) img.save("generated_img_%03d_%d.png" % (epoch, i)) </code></pre></div> <hr /> <h2 id="train-the-endtoend-model">Train the end-to-end model</h2> <div class="codehilite"><pre><code>epochs = 1 # In practice, use ~100 epochs gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim) gan.compile( d_optimizer=keras.optimizers.Adam(learning_rate=0.0001), g_optimizer=keras.optimizers.Adam(learning_rate=0.0001), loss_fn=keras.losses.BinaryCrossentropy(), ) gan.fit( dataset, epochs=epochs, callbacks=[GANMonitor(num_img=10, latent_dim=latent_dim)] ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code> 2/6332 [37m━━━━━━━━━━━━━━━━━━━━ 9:54 94ms/step - d_loss: 0.6792 - g_loss: 0.7880 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1704214667.959762 1319 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 6332/6332 ━━━━━━━━━━━━━━━━━━━━ 557s 84ms/step - d_loss: 0.5616 - g_loss: 1.4099 <keras.src.callbacks.history.History at 0x7f251d32bc40> </code></pre></div> </div> Some of the last generated images around epoch 30 (results keep improving after that): <img alt="results" src="https://i.imgur.com/h5MtQZ7l.png" /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#dcgan-to-generate-face-images'>DCGAN to generate face images</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#prepare-celeba-data'>Prepare CelebA data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#create-the-discriminator'>Create the discriminator</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#create-the-generator'>Create the generator</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#override-trainstep'>Override <code>train_step</code></a> </div> <div class='k-outline-depth-2'> ◆ <a href='#create-a-callback-that-periodically-saves-generated-images'>Create a callback that periodically saves generated images</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#train-the-endtoend-model'>Train the end-to-end model</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>