Code Arrangement

gan/main.py

@@ -1,46 +1,45 @@
 import mnist
-from tqdm import tqdm
 
 import argparse
 
 import mlx.core as mx
 import mlx.nn as nn
 import mlx.optimizers as optim
+
+from tqdm import tqdm
 import numpy as np
+import matplotlib.pyplot as plt
+
 
 # Generator Block
 def GenBlock(in_dim:int,out_dim:int):
     return nn.Sequential(
         nn.Linear(in_dim,out_dim),
-        nn.BatchNorm(out_dim),
+        nn.BatchNorm(out_dim, 0.8),
-        nn.ReLU()
+        nn.LeakyReLU(0.2)
     )
 
-# Generator Layer
+# Generator Model
 class Generator(nn.Module):
 
-    def __init__(self, z_dim:int = 10, im_dim:int = 784, hidden_dim: int =128):
+    def __init__(self, z_dim:int = 32, im_dim:int = 784, hidden_dim: int = 256):
         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 * 4,im_dim),
-            nn.Linear(hidden_dim * 8,im_dim),
-            nn.Sigmoid()
         )
         
-    def forward(self, noise):
+    def __call__(self, noise):
+        x = self.gen(noise)
+        return mx.tanh(x)
 
-        return self.gen(noise) 
+# make 2D noise with shape n_samples x z_dim
-
+def get_noise(n_samples:list[int], z_dim:int)->list[int]:
-
+    return mx.random.normal(shape=(n_samples, z_dim))
-# return random n,m normal distribution
-def get_noise(n_samples:int, z_dim:int)->list:
-    return np.random.randn(n_samples,z_dim)
 
 #---------------------------------------------#
 
@@ -48,13 +47,14 @@ def get_noise(n_samples:int, z_dim:int)->list:
 def DisBlock(in_dim:int,out_dim:int):
     return nn.Sequential(
         nn.Linear(in_dim,out_dim),
-        nn.LeakyReLU(negative_slope=0.2)
+        nn.LeakyReLU(negative_slope=0.2),
+        nn.Dropout(0.3),
     )
 
-# Discriminator Layer
+# Discriminator Model
 class Discriminator(nn.Module):
 
-    def __init__(self,im_dim:int = 784, hidden_dim:int = 128):
+    def __init__(self,im_dim:int = 784, hidden_dim:int = 256):
         super(Discriminator, self).__init__()
 
         self.disc = nn.Sequential(
@@ -63,12 +63,47 @@ class Discriminator(nn.Module):
             DisBlock(hidden_dim * 2, hidden_dim),
             
             nn.Linear(hidden_dim,1),
+            nn.Sigmoid()
         )
         
-    def forward(self, noise):
+    def __call__(self, noise):
-
         return self.disc(noise)
     
+# Discriminator Loss
+def disc_loss(gen, disc, real, num_images, z_dim):
+    
+    noise =  mx.array(get_noise(num_images, z_dim))
+    fake_images = gen(noise)
+        
+    fake_disc = disc(fake_images)
+    
+    fake_labels = mx.zeros((fake_images.shape[0],1))
+            
+    fake_loss = mx.mean(nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True))
+        
+    real_disc = mx.array(disc(real))
+    real_labels = mx.ones((real.shape[0],1))
+    
+    real_loss = mx.mean(nn.losses.binary_cross_entropy(real_disc,real_labels,with_logits=True))
+    
+    disc_loss = (fake_loss + real_loss) / 2.0
+
+    return disc_loss
+
+# Genearator Loss
+def gen_loss(gen, disc, num_images, z_dim):
+
+    noise = mx.array(get_noise(num_images, z_dim))
+    
+    fake_images = gen(noise)
+    fake_disc = mx.array(disc(fake_images))
+
+    fake_labels = mx.ones((fake_images.shape[0],1))
+            
+    gen_loss = nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True)
+    
+    return mx.mean(gen_loss)
+
 def main(args:dict):
     seed = 42
     criterion = nn.losses.binary_cross_entropy
@@ -78,7 +113,7 @@ def main(args:dict):
     batch_size = 128
     lr = 0.00001
     
-    np.random.seed(seed)
+    mx.random.seed(seed)
 
     # Load the data
     train_images, train_labels, test_images, test_labels = map(