Add GAN model 25/7

gan/main.py

@@ -0,0 +1,138 @@
+import mnist
+from tqdm import tqdm
+
+import argparse
+
+import mlx.core as mx
+import mlx.nn as nn
+import mlx.optimizers as optim
+import numpy as np
+
+# Generator Block
+def GenBlock(in_dim:int,out_dim:int):
+    return nn.Sequential(
+        nn.Linear(in_dim,out_dim),
+        nn.BatchNorm(out_dim),
+        nn.ReLU()
+    )
+
+# Generator Layer
+class Generator(nn.Module):
+
+    def __init__(self, z_dim:int = 10, im_dim:int = 784, hidden_dim: int =128):
+        super(Generator, self).__init__()
+        # Build the neural network
+        self.gen = nn.Sequential(
+            GenBlock(z_dim, hidden_dim),
+            GenBlock(hidden_dim, hidden_dim * 2),
+            GenBlock(hidden_dim * 2, hidden_dim * 4),
+            GenBlock(hidden_dim * 4, hidden_dim * 8),
+
+
+            nn.Linear(hidden_dim * 8,im_dim),
+            nn.Sigmoid()
+        )
+        
+    def forward(self, noise):
+
+        return self.gen(noise) 
+
+
+# return random n,m normal distribution
+def get_noise(n_samples:int, z_dim:int)->list:
+    return np.random.randn(n_samples,z_dim)
+
+#---------------------------------------------#
+
+# Discriminator Block
+def DisBlock(in_dim:int,out_dim:int):   
+    return nn.Sequential(
+        nn.Linear(in_dim,out_dim),
+        nn.LeakyReLU(negative_slope=0.2)
+    )
+
+# Discriminator Layer
+class Discriminator(nn.Module):
+
+    def __init__(self,im_dim:int = 784, hidden_dim:int = 128):
+        super(Discriminator, self).__init__()
+
+        self.disc = nn.Sequential(
+            DisBlock(im_dim, hidden_dim * 4),
+            DisBlock(hidden_dim * 4, hidden_dim * 2),
+            DisBlock(hidden_dim * 2, hidden_dim),
+
+            nn.Linear(hidden_dim,1),
+        )
+        
+    def forward(self, noise):
+
+        return self.disc(noise)
+
+def main(args:dict):
+    seed = 42
+    criterion = nn.losses.binary_cross_entropy
+    n_epochs = 200
+    z_dim = 64
+    display_step = 500
+    batch_size = 128
+    lr = 0.00001
+    
+    np.random.seed(seed)
+
+    # Load the data
+    train_images, train_labels, test_images, test_labels = map(
+        mx.array, getattr(mnist, args.dataset)()
+    )
+    
+    gen = Generator(z_dim)
+    gen_opt = optim.Adam(learning_rate=lr)
+    disc = Discriminator()
+    disc_opt = optim.Adam(learning_rate=lr)
+    
+    # use partial function
+    def disc_loss(gen, disc, criterion, real, num_images, z_dim):
+        noise = get_noise(num_images, z_dim,device)
+        fake_images = gen(noise)
+        
+        fake_disc = disc(fake_images.detach())
+        fake_labels = mx.zeros(fake_images.size(0),1)
+        fake_loss = criterion(fake_disc,fake_labels)
+        
+        real_disc = disc(real)
+        real_labels = mx.ones(real.size(0),1)
+        real_loss = criterion(real_disc,real_labels)
+
+        disc_loss = (fake_loss + real_loss) / 2
+
+        return disc_loss
+    
+    def gen_loss(gen, disc, criterion, num_images, z_dim):
+
+        noise = get_noise(num_images, z_dim,device)
+        fake_images = gen(noise)
+        
+        fake_disc = disc(fake_images)
+        fake_labels = mx.ones(fake_images.size(0),1)
+        
+        gen_loss = criterion(fake_disc,fake_labels)
+
+        return gen_loss
+
+    # training
+    
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Train a simple GAN on MNIST with MLX.")
+    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="mnist",
+        choices=["mnist", "fashion_mnist"],
+        help="The dataset to use.",
+    )
+    args = parser.parse_args()
+    if not args.gpu:
+        mx.set_default_device(mx.cpu)
+    main(args)

gan/mnist.py

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+# Copyright © 2023 Apple Inc.
+
+import gzip
+import os
+import pickle
+from urllib import request
+
+import numpy as np
+
+
+def mnist(
+    save_dir="/tmp",
+    base_url="https://raw.githubusercontent.com/fgnt/mnist/master/",
+    filename="mnist.pkl",
+):
+    """
+    Load the MNIST dataset in 4 tensors: train images, train labels,
+    test images, and test labels.
+
+    Checks `save_dir` for already downloaded data otherwise downloads.
+
+    Download code modified from:
+      https://github.com/hsjeong5/MNIST-for-Numpy
+    """
+
+    def download_and_save(save_file):
+        filename = [
+            ["training_images", "train-images-idx3-ubyte.gz"],
+            ["test_images", "t10k-images-idx3-ubyte.gz"],
+            ["training_labels", "train-labels-idx1-ubyte.gz"],
+            ["test_labels", "t10k-labels-idx1-ubyte.gz"],
+        ]
+
+        mnist = {}
+        for name in filename:
+            out_file = os.path.join("/tmp", name[1])
+            request.urlretrieve(base_url + name[1], out_file)
+        for name in filename[:2]:
+            out_file = os.path.join("/tmp", name[1])
+            with gzip.open(out_file, "rb") as f:
+                mnist[name[0]] = np.frombuffer(f.read(), np.uint8, offset=16).reshape(
+                    -1, 28 * 28
+                )
+        for name in filename[-2:]:
+            out_file = os.path.join("/tmp", name[1])
+            with gzip.open(out_file, "rb") as f:
+                mnist[name[0]] = np.frombuffer(f.read(), np.uint8, offset=8)
+        with open(save_file, "wb") as f:
+            pickle.dump(mnist, f)
+
+    save_file = os.path.join(save_dir, filename)
+    if not os.path.exists(save_file):
+        download_and_save(save_file)
+    with open(save_file, "rb") as f:
+        mnist = pickle.load(f)
+
+    def preproc(x):
+        return x.astype(np.float32) / 255.0
+
+    mnist["training_images"] = preproc(mnist["training_images"])
+    mnist["test_images"] = preproc(mnist["test_images"])
+    return (
+        mnist["training_images"],
+        mnist["training_labels"].astype(np.uint32),
+        mnist["test_images"],
+        mnist["test_labels"].astype(np.uint32),
+    )
+
+
+def fashion_mnist(save_dir="/tmp"):
+    return mnist(
+        save_dir,
+        base_url="http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/",
+        filename="fashion_mnist.pkl",
+    )
+
+
+if __name__ == "__main__":
+    train_x, train_y, test_x, test_y = mnist()
+    assert train_x.shape == (60000, 28 * 28), "Wrong training set size"
+    assert train_y.shape == (60000,), "Wrong training set size"
+    assert test_x.shape == (10000, 28 * 28), "Wrong test set size"
+    assert test_y.shape == (10000,), "Wrong test set size"