diff --git a/gan/playground.ipynb b/gan/playground.ipynb
index 2dc3d24d..98b23839 100644
--- a/gan/playground.ipynb
+++ b/gan/playground.ipynb
@@ -250,13 +250,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [],
    "source": [
     "gen = Generator(z_dim)\n",
+    "mx.eval(gen.parameters())\n",
     "gen_opt = optim.Adam(learning_rate=lr)\n",
+    "\n",
     "disc = Discriminator()\n",
+    "mx.eval(disc.parameters())\n",
     "disc_opt = optim.Adam(learning_rate=lr)"
    ]
   },
@@ -269,22 +272,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def disc_loss(gen, disc, criterion, real, num_images, z_dim):\n",
+    "def disc_loss(gen, disc, real, num_images, z_dim):\n",
     "    noise =  mx.array(get_noise(num_images, z_dim))\n",
     "    fake_images = gen(noise)\n",
     "    \n",
     "    fake_disc = disc(fake_images)\n",
     "    \n",
     "    fake_labels = mx.zeros((len(fake_images),1))\n",
-    "    fake_loss = criterion(fake_disc,fake_labels)\n",
+    "    fake_loss = nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True)\n",
     "    \n",
     "    real_disc = disc(real)\n",
     "    real_labels = mx.ones((len(real),1))\n",
-    "    real_loss = criterion(real_disc,real_labels)\n",
+    "    real_loss = nn.losses.binary_cross_entropy(real_disc,real_labels,with_logits=True)\n",
     "\n",
     "    disc_loss = (fake_loss + real_loss) / 2\n",
     "\n",
@@ -293,11 +296,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def gen_loss(gen, disc, criterion, num_images, z_dim):\n",
+    "def gen_loss(gen, disc, num_images, z_dim):\n",
     "\n",
     "    noise = mx.array(get_noise(num_images, z_dim))\n",
     "    fake_images = gen(noise)\n",
@@ -306,14 +309,14 @@
     "\n",
     "    fake_labels = mx.ones((fake_images.size(0),1))\n",
     "    \n",
-    "    gen_loss = criterion(fake_disc,fake_labels)\n",
+    "    gen_loss = nn.losses.binary_cross_entropy(fake_disc,fake_labels,with_logits=True)\n",
     "\n",
     "    return gen_loss"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,12 +347,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x400 with 16 Axes>"
       ]
@@ -371,26 +374,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "0it [00:00, ?it/s]\n",
       "0it [00:00, ?it/s]\n"
      ]
     },
     {
      "ename": "TypeError",
-     "evalue": "'array' object is not callable",
+     "evalue": "'bool' object is not callable",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[167], line 21\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_epochs):\n\u001b[1;32m     10\u001b[0m   \n\u001b[1;32m     11\u001b[0m     \u001b[38;5;66;03m# Dataloader returns the batches\u001b[39;00m\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m real \u001b[38;5;129;01min\u001b[39;00m tqdm(batch_iterate(batch_size, train_images)):\n\u001b[1;32m     13\u001b[0m \n\u001b[1;32m     14\u001b[0m         \u001b[38;5;66;03m# Flatten the batch of real images from the dataset\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     19\u001b[0m \n\u001b[1;32m     20\u001b[0m         \u001b[38;5;66;03m# Calculate discriminator loss\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m         disc_loss \u001b[38;5;241m=\u001b[39m \u001b[43mdisc_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreal\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_dim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     23\u001b[0m         \u001b[38;5;66;03m# Update gradients\u001b[39;00m\n\u001b[1;32m     24\u001b[0m         \u001b[38;5;66;03m# disc_loss.backward(retain_graph=True)\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \n\u001b[1;32m     26\u001b[0m         \u001b[38;5;66;03m# Update optimizer\u001b[39;00m\n\u001b[1;32m     27\u001b[0m         mx\u001b[38;5;241m.\u001b[39meval(disc\u001b[38;5;241m.\u001b[39mparameters())\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'array' object is not callable"
+      "Cell \u001b[0;32mIn[203], line 28\u001b[0m\n\u001b[1;32m     23\u001b[0m disc_opt\u001b[38;5;241m.\u001b[39mupdate(disc, D_grads)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Update gradients\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m G_loss,G_grads \u001b[38;5;241m=\u001b[39m \u001b[43mG_loss_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_dim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# Update optimizer\u001b[39;00m\n\u001b[1;32m     31\u001b[0m gen_opt\u001b[38;5;241m.\u001b[39mupdate(gen, G_grads)\n",
+      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/nn/utils.py:34\u001b[0m, in \u001b[0;36mvalue_and_grad.<locals>.wrapped_value_grad_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(fn)\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_value_grad_fn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 34\u001b[0m     value, grad \u001b[38;5;241m=\u001b[39m \u001b[43mvalue_grad_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainable_parameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m value, grad\n",
+      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/nn/utils.py:28\u001b[0m, in \u001b[0;36mvalue_and_grad.<locals>.inner_fn\u001b[0;34m(params, *args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner_fn\u001b[39m(params, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     27\u001b[0m     model\u001b[38;5;241m.\u001b[39mupdate(params)\n\u001b[0;32m---> 28\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'bool' object is not callable"
      ]
     }
    ],
@@ -403,69 +407,50 @@
     "gen_loss = False\n",
     "error = False\n",
     "\n",
+    "D_loss_grad = nn.value_and_grad(disc, disc_loss)\n",
+    "G_loss_grad = nn.value_and_grad(gen, gen_loss)\n",
+    "\n",
+    "\n",
     "for epoch in range(n_epochs):\n",
     "  \n",
     "    # Dataloader returns the batches\n",
     "    for real in tqdm(batch_iterate(batch_size, train_images)):\n",
     "\n",
     "        # Flatten the batch of real images from the dataset\n",
-    "\n",
-    "        ### Update discriminator ###\n",
-    "        # Zero out the gradients before backpropagation\n",
-    "        # disc_opt.zero_grad()\n",
-    "\n",
-    "        # Calculate discriminator loss\n",
-    "        disc_loss = disc_loss(gen, disc, criterion, real, batch_size, z_dim)\n",
     "                \n",
-    "        # Update gradients\n",
-    "        # disc_loss.backward(retain_graph=True)\n",
+    "        D_loss,D_grads = D_loss_grad(gen, disc, real, batch_size, z_dim)\n",
     "\n",
     "        # Update optimizer\n",
-    "        mx.eval(disc.parameters())\n",
+    "        disc_opt.update(disc, D_grads)\n",
     "        \n",
-    "        break\n",
-    "        # For testing purposes, to keep track of the generator weights\n",
-    "        if test_generator:\n",
-    "            old_generator_weights = gen.gen[0][0].weight.detach().clone()\n",
+    "        # Update gradients\n",
+    "        \n",
+    "        \n",
+    "        G_loss,G_grads = G_loss_grad(gen, disc, batch_size, z_dim)\n",
+    "        \n",
+    "        # Update optimizer\n",
+    "        gen_opt.update(gen, G_grads)\n",
+    "        \n",
+    "        # Update gradients\n",
     "\n",
-    "        ### Update generator ###\n",
-    "        #     Hint: This code will look a lot like the discriminator updates!\n",
-    "        #     These are the steps you will need to complete:\n",
-    "        #       1) Zero out the gradients.\n",
-    "        #       2) Calculate the generator loss, assigning it to gen_loss.\n",
-    "        #       3) Backprop through the generator: update the gradients and optimizer.\n",
-    "        #### START CODE HERE ####\n",
-    "        gen_opt.zero_grad()\n",
-    "        gen_loss = get_gen_loss(gen, disc, criterion, cur_batch_size, z_dim, device)\n",
-    "        gen_loss.backward(retain_graph=True)\n",
-    "        gen_opt.step()\n",
-    "        #### END CODE HERE ####\n",
+    "        \n",
     "\n",
-    "        # For testing purposes, to check that your code changes the generator weights\n",
-    "        if test_generator:\n",
-    "            try:\n",
-    "                assert lr > 0.0000002 or (gen.gen[0][0].weight.grad.abs().max() < 0.0005 and epoch == 0)\n",
-    "                assert torch.any(gen.gen[0][0].weight.detach().clone() != old_generator_weights)\n",
-    "            except:\n",
-    "                error = True\n",
-    "                print(\"Runtime tests have failed\")\n",
+    "        # # Keep track of the average discriminator loss\n",
+    "        # mean_discriminator_loss += disc_loss.item() / display_step\n",
     "\n",
-    "        # Keep track of the average discriminator loss\n",
-    "        mean_discriminator_loss += disc_loss.item() / display_step\n",
+    "        # # Keep track of the average generator loss\n",
+    "        # mean_generator_loss += gen_loss.item() / display_step\n",
     "\n",
-    "        # Keep track of the average generator loss\n",
-    "        mean_generator_loss += gen_loss.item() / display_step\n",
-    "\n",
-    "        ### Visualization code ###\n",
-    "        if cur_step % display_step == 0 and cur_step > 0:\n",
-    "            print(f\"Step {cur_step}: Generator loss: {mean_generator_loss}, discriminator loss: {mean_discriminator_loss}\")\n",
-    "            fake_noise = get_noise(cur_batch_size, z_dim, device=device)\n",
-    "            fake = gen(fake_noise)\n",
-    "            show_tensor_images(fake)\n",
-    "            show_tensor_images(real)\n",
-    "            mean_generator_loss = 0\n",
-    "            mean_discriminator_loss = 0\n",
-    "        cur_step += 1\n"
+    "        # ### Visualization code ###\n",
+    "        # if cur_step % display_step == 0 and cur_step > 0:\n",
+    "        #     print(f\"Step {cur_step}: Generator loss: {mean_generator_loss}, discriminator loss: {mean_discriminator_loss}\")\n",
+    "        #     fake_noise = get_noise(cur_batch_size, z_dim, device=device)\n",
+    "        #     fake = gen(fake_noise)\n",
+    "        #     show_tensor_images(fake)\n",
+    "        #     show_tensor_images(real)\n",
+    "        #     mean_generator_loss = 0\n",
+    "        #     mean_discriminator_loss = 0\n",
+    "        # cur_step += 1\n"
    ]
   }
  ],