Initial commit

ML/TensorFlow/more_advanced/DCGAN/main.py

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+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tqdm import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+
+dataset = keras.preprocessing.image_dataset_from_directory(
+    directory="celeb_dataset", label_mode=None, image_size=(64, 64), batch_size=32,
+    shuffle=True, seed=None, validation_split=None,
+).map(lambda x: x / 255.0)
+
+discriminator = keras.Sequential(
+    [
+        keras.Input(shape=(64, 64, 3)),
+        layers.Conv2D(64, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(alpha=0.2),
+        layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Flatten(),
+        layers.Dropout(0.2),
+        layers.Dense(1, activation="sigmoid"),
+    ],
+    name="discriminator",
+)
+print(discriminator.summary())
+
+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(alpha=0.2),
+        layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(alpha=0.2),
+        layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(alpha=0.2),
+        layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"),
+    ],
+    name="generator",
+)
+generator.summary()
+
+opt_gen = keras.optimizers.Adam(1e-4)
+opt_disc = keras.optimizers.Adam(1e-4)
+loss_fn = keras.losses.BinaryCrossentropy()
+
+for epoch in range(10):
+    for idx, (real) in enumerate(tqdm(dataset)):
+        batch_size = real.shape[0]
+        with tf.GradientTape() as gen_tape:
+            random_latent_vectors = tf.random.normal(shape = (batch_size, latent_dim))
+            fake = generator(random_latent_vectors)
+
+        if idx % 100 == 0:
+            img = keras.preprocessing.image.array_to_img(fake[0])
+            img.save("gen_images/generated_img_%03d_%d.png" % (epoch, idx))
+
+        ### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
+        with tf.GradientTape() as disc_tape:
+            loss_disc_real = loss_fn(tf.ones((batch_size, 1)), discriminator(real))
+            loss_disc_fake = loss_fn(tf.zeros((batch_size, 1)), discriminator(fake))
+            loss_disc = (loss_disc_real + loss_disc_fake)/2
+
+        grads = disc_tape.gradient(loss_disc, discriminator.trainable_weights)
+        opt_disc.apply_gradients(
+            zip(grads, discriminator.trainable_weights)
+        )
+
+        ### Train Generator: min log(1 - D(G(z))) <-> max log(D(G(z))
+        with tf.GradientTape() as gen_tape:
+            fake = generator(random_latent_vectors)
+            output = discriminator(fake)
+            loss_gen = loss_fn(tf.ones(batch_size, 1), output)
+
+        grads = gen_tape.gradient(loss_gen, generator.trainable_weights)
+        opt_gen.apply_gradients(zip(grads, generator.trainable_weights))
+
+
+
+
+

ML/TensorFlow/more_advanced/DCGAN/train.py

@@ -0,0 +1,87 @@
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tqdm import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+dataset = keras.preprocessing.image_dataset_from_directory(
+    directory="celeb_dataset", label_mode=None, image_size=(64, 64), batch_size=32,
+    shuffle=True
+).map(lambda x: x/255.0)
+
+discriminator = keras.Sequential(
+    [
+        keras.Input(shape=(64,64,3)),
+        layers.Conv2D(64, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Flatten(),
+        layers.Dropout(0.2),
+        layers.Dense(1, activation="sigmoid"),
+    ]
+)
+
+print(discriminator.summary())
+
+latent_dim = 128
+generator = keras.Sequential(
+    [
+        layers.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(0.2),
+        layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"),
+        layers.LeakyReLU(0.2),
+        layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"),
+    ]
+)
+generator.summary()
+
+opt_gen = keras.optimizers.Adam(1e-4)
+opt_disc = keras.optimizers.Adam(1e-4)
+loss_fn = keras.losses.BinaryCrossentropy()
+
+for epoch in range(10):
+    for idx, real in enumerate(tqdm(dataset)):
+        batch_size = real.shape[0]
+        random_latent_vectors = tf.random.normal(shape=(batch_size, latent_dim))
+        fake = generator(random_latent_vectors)
+
+        if idx % 100 == 0:
+            img = keras.preprocessing.image.array_to_img(fake[0])
+            img.save(f"generated_images/generated_img{epoch}_{idx}_.png")
+
+        ### Train Discriminator: max log(D(x)) + log(1 - D(G(z))
+        with tf.GradientTape() as disc_tape:
+            loss_disc_real = loss_fn(tf.ones((batch_size, 1)), discriminator(real))
+            loss_disc_fake = loss_fn(tf.zeros(batch_size, 1), discriminator(fake))
+            loss_disc = (loss_disc_real + loss_disc_fake)/2
+
+        grads = disc_tape.gradient(loss_disc, discriminator.trainable_weights)
+        opt_disc.apply_gradients(
+            zip(grads, discriminator.trainable_weights)
+        )
+
+        ### Train Generator min log(1 - D(G(z)) <-> max log(D(G(z))
+        with tf.GradientTape() as gen_tape:
+            fake = generator(random_latent_vectors)
+            output = discriminator(fake)
+            loss_gen = loss_fn(tf.ones(batch_size, 1), output)
+
+        grads = gen_tape.gradient(loss_gen, generator.trainable_weights)
+        opt_gen.apply_gradients(
+            zip(grads, generator.trainable_weights)
+        )
+