mlx-examples/gan/playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlx.core as mx\n",
    "import mlx.nn as nn\n",
    "import mlx.optimizers as optim\n",
    "\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAN Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator 👨🏻‍🎨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GenBlock(in_dim:int,out_dim:int):\n",
    "   \n",
    "    return nn.Sequential(\n",
    "        nn.Linear(in_dim,out_dim),\n",
    "        nn.BatchNorm(out_dim),\n",
    "        nn.ReLU()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "\n",
    "    def __init__(self, z_dim:int = 10, im_dim:int = 784, hidden_dim: int =128):\n",
    "        super(Generator, self).__init__()\n",
    "        # Build the neural network\n",
    "        self.gen = nn.Sequential(\n",
    "            GenBlock(z_dim, hidden_dim),\n",
    "            GenBlock(hidden_dim, hidden_dim * 2),\n",
    "            GenBlock(hidden_dim * 2, hidden_dim * 4),\n",
    "            GenBlock(hidden_dim * 4, hidden_dim * 8),\n",
    "\n",
    "\n",
    "            nn.Linear(hidden_dim * 8,im_dim),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def __call__(self, noise):\n",
    "\n",
    "        return self.gen(noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (gen): Sequential(\n",
       "    (layers.0): Sequential(\n",
       "      (layers.0): Linear(input_dims=100, output_dims=128, bias=True)\n",
       "      (layers.1): BatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (layers.2): ReLU()\n",
       "    )\n",
       "    (layers.1): Sequential(\n",
       "      (layers.0): Linear(input_dims=128, output_dims=256, bias=True)\n",
       "      (layers.1): BatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (layers.2): ReLU()\n",
       "    )\n",
       "    (layers.2): Sequential(\n",
       "      (layers.0): Linear(input_dims=256, output_dims=512, bias=True)\n",
       "      (layers.1): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (layers.2): ReLU()\n",
       "    )\n",
       "    (layers.3): Sequential(\n",
       "      (layers.0): Linear(input_dims=512, output_dims=1024, bias=True)\n",
       "      (layers.1): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (layers.2): ReLU()\n",
       "    )\n",
       "    (layers.4): Linear(input_dims=1024, output_dims=784, bias=True)\n",
       "    (layers.5): Sigmoid()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen = Generator(100)\n",
    "gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_noise(n_samples, z_dim):\n",
    "    return np.random.randn(n_samples,z_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator 🕵🏻‍♂️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DisBlock(in_dim:int,out_dim:int):\n",
    "    return nn.Sequential(\n",
    "        nn.Linear(in_dim,out_dim),\n",
    "        nn.LeakyReLU(negative_slope=0.2)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self,im_dim:int = 784, hidden_dim:int = 128):\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        self.disc = nn.Sequential(\n",
    "            DisBlock(im_dim, hidden_dim * 4),\n",
    "            DisBlock(hidden_dim * 4, hidden_dim * 2),\n",
    "            DisBlock(hidden_dim * 2, hidden_dim),\n",
    "\n",
    "            nn.Linear(hidden_dim,1),\n",
    "        )\n",
    "        \n",
    "    def __call__(self, noise):\n",
    "\n",
    "        return self.disc(noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discriminator(\n",
       "  (disc): Sequential(\n",
       "    (layers.0): Sequential(\n",
       "      (layers.0): Linear(input_dims=784, output_dims=512, bias=True)\n",
       "      (layers.1): LeakyReLU()\n",
       "    )\n",
       "    (layers.1): Sequential(\n",
       "      (layers.0): Linear(input_dims=512, output_dims=256, bias=True)\n",
       "      (layers.1): LeakyReLU()\n",
       "    )\n",
       "    (layers.2): Sequential(\n",
       "      (layers.0): Linear(input_dims=256, output_dims=128, bias=True)\n",
       "      (layers.1): LeakyReLU()\n",
       "    )\n",
       "    (layers.3): Linear(input_dims=128, output_dims=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc = Discriminator()\n",
    "disc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training 🏋🏻‍♂️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set your parameters\n",
    "criterion = nn.losses.binary_cross_entropy\n",
    "n_epochs = 200\n",
    "z_dim = 64\n",
    "display_step = 500\n",
    "batch_size = 128\n",
    "lr = 0.00001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[123], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m gen \u001b[38;5;241m=\u001b[39m Generator(z_dim)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m gen_opt \u001b[38;5;241m=\u001b[39m optim\u001b[38;5;241m.\u001b[39mAdam(learning_rate\u001b[38;5;241m=\u001b[39mlr)\n\u001b[1;32m      5\u001b[0m disc \u001b[38;5;241m=\u001b[39m Discriminator()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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((fake_images.shape[0],1))\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((real.shape[0],1))\n",
    "\n",
    "    # print('Shapes.....',real_disc.shape,real_labels.shape)\n",
    "    real_loss = nn.losses.binary_cross_entropy(real_disc,real_labels)\n",
    "\n",
    "    disc_loss = (fake_loss + real_loss) / 2\n",
    "\n",
    "    return disc_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "    \n",
    "    fake_disc = disc(fake_images)\n",
    "\n",
    "    fake_labels = mx.ones((fake_images.shape[0],1))\n",
    "    \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": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images, _, test_images, _ = map(\n",
    "    mx.array, getattr(mnist, 'mnist')()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_iterate(batch_size:int, ipt:list):\n",
    "    perm = mx.array(np.random.permutation(len(ipt)))\n",
    "    for s in range(0, ipt.size, batch_size):\n",
    "        ids = perm[s : s + batch_size]\n",
    "        yield ipt[ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show batch of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 400x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in batch_iterate(16, train_images):\n",
    "    fig,axes = plt.subplots(4, 4, figsize=(4, 4))\n",
    "\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        img = mx.array(X[i]).reshape(28,28)\n",
    "        ax.imshow(img,cmap='gray')\n",
    "        ax.axis('off')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 139/60000 [00:00<01:22, 726.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
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      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n",
      "real shape ,  (784,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3662/60000 [00:54<14:01, 66.96it/s] \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[108], line 40\u001b[0m\n\u001b[1;32m     37\u001b[0m G_loss,G_grads \u001b[38;5;241m=\u001b[39m G_loss_grad(gen, disc, batch_size, z_dim)\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Update optimizer\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[43mgen_opt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mG_grads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# Update gradients\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \n\u001b[1;32m     44\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;66;03m#     mean_discriminator_loss = 0\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;66;03m# cur_step += 1\u001b[39;00m\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/optimizers/optimizers.py:29\u001b[0m, in \u001b[0;36mOptimizer.update\u001b[0;34m(self, model, gradients)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate\u001b[39m(\u001b[38;5;28mself\u001b[39m, model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmlx.nn.Module\u001b[39m\u001b[38;5;124m\"\u001b[39m, gradients: \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     21\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Apply the gradients to the parameters of the model and update the\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    model with the new parameters.\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;124;03m                          via :func:`mlx.nn.value_and_grad`.\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m     model\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_gradients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgradients\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/optimizers/optimizers.py:88\u001b[0m, in \u001b[0;36mOptimizer.apply_gradients\u001b[0;34m(self, gradients, parameters)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     87\u001b[0m \u001b[38;5;66;03m# Apply the update\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_single\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradients\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:48\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m---> 48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:49\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m---> 49\u001b[0m         k: \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:48\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m---> 48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:49\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m---> 49\u001b[0m         k: \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:43\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m     42\u001b[0m     TreeType \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(tree)\n\u001b[0;32m---> 43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mTreeType\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:44\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m     42\u001b[0m     TreeType \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(tree)\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[0;32m---> 44\u001b[0m         \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:48\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m---> 48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:49\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m---> 49\u001b[0m         k: \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:43\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m     42\u001b[0m     TreeType \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(tree)\n\u001b[0;32m---> 43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mTreeType\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:44\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m     42\u001b[0m     TreeType \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(tree)\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[0;32m---> 44\u001b[0m         \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:48\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m---> 48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:49\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m TreeType(\n\u001b[1;32m     44\u001b[0m         tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[i] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, child \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tree)\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tree, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m---> 49\u001b[0m         k: \u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchild\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_leaf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_leaf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(tree, \u001b[38;5;241m*\u001b[39mrest)\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/utils.py:53\u001b[0m, in \u001b[0;36mtree_map\u001b[0;34m(fn, tree, is_leaf, *rest)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m     49\u001b[0m         k: tree_map(fn, child, \u001b[38;5;241m*\u001b[39m(r[k] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest), is_leaf\u001b[38;5;241m=\u001b[39mis_leaf)\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, child \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m     51\u001b[0m     }\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrest\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/optimizers/optimizers.py:426\u001b[0m, in \u001b[0;36mAdam.apply_single\u001b[0;34m(self, gradient, parameter, state)\u001b[0m\n\u001b[1;32m    424\u001b[0m v \u001b[38;5;241m=\u001b[39m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    425\u001b[0m m \u001b[38;5;241m=\u001b[39m b1 \u001b[38;5;241m*\u001b[39m m \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m b1) \u001b[38;5;241m*\u001b[39m gradient\n\u001b[0;32m--> 426\u001b[0m v \u001b[38;5;241m=\u001b[39m b2 \u001b[38;5;241m*\u001b[39m v \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m b2) \u001b[38;5;241m*\u001b[39m \u001b[43mmx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msquare\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    427\u001b[0m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mm\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m m\n\u001b[1;32m    428\u001b[0m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m v\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 8\n",
    "cur_step = 0\n",
    "mean_generator_loss = 0\n",
    "mean_discriminator_loss = 0\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(10):\n",
    "  \n",
    "    # Dataloader returns the batches\n",
    "    # for real in tqdm(batch_iterate(batch_size, train_images)):\n",
    "    \n",
    "    for real in tqdm(train_images):\n",
    "\n",
    "    \n",
    "        # real = real.reshape(-1)\n",
    "        \n",
    "        # Flatten the batch of real images from the dataset\n",
    "        \n",
    "        # plt.imshow(real[0].reshape(28,28))\n",
    "        # print(len(real))\n",
    "        # break\n",
    "                \n",
    "        D_loss,D_grads = D_loss_grad(gen, disc, real, batch_size, z_dim)\n",
    "\n",
    "        # Update optimizer\n",
    "        disc_opt.update(disc, D_grads)\n",
    "        \n",
    "        # Update gradients\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",
    "\n",
    "        # # Keep track of the average discriminator loss\n",
    "        # mean_discriminator_loss += disc_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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes..... (1,) (784, 1)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Inputs shape (1,) does not match targets shape (784, 1).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[90], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \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[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",
      "Cell \u001b[0;32mIn[89], line 14\u001b[0m, in \u001b[0;36mdisc_loss\u001b[0;34m(gen, disc, real, num_images, z_dim)\u001b[0m\n\u001b[1;32m     11\u001b[0m real_labels \u001b[38;5;241m=\u001b[39m mx\u001b[38;5;241m.\u001b[39mones((real\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mShapes.....\u001b[39m\u001b[38;5;124m'\u001b[39m,real_disc\u001b[38;5;241m.\u001b[39mshape,real_labels\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m---> 14\u001b[0m real_loss \u001b[38;5;241m=\u001b[39m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlosses\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbinary_cross_entropy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreal_disc\u001b[49m\u001b[43m,\u001b[49m\u001b[43mreal_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mwith_logits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m disc_loss \u001b[38;5;241m=\u001b[39m (fake_loss \u001b[38;5;241m+\u001b[39m real_loss) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m disc_loss\n",
      "File \u001b[0;32m~/miniforge3/lib/python3.10/site-packages/mlx/nn/losses.py:155\u001b[0m, in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(inputs, targets, weights, with_logits, reduction)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;124;03mComputes the binary cross entropy loss.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;124;03m    array(0.510826, dtype=float32)\u001b[39;00m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inputs\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m targets\u001b[38;5;241m.\u001b[39mshape:\n\u001b[0;32m--> 155\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    156\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInputs shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minputs\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not match targets shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtargets\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    157\u001b[0m     )\n\u001b[1;32m    159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_logits:\n\u001b[1;32m    160\u001b[0m     loss \u001b[38;5;241m=\u001b[39m mx\u001b[38;5;241m.\u001b[39mlogaddexp(\u001b[38;5;241m0.0\u001b[39m, inputs) \u001b[38;5;241m-\u001b[39m inputs \u001b[38;5;241m*\u001b[39m targets\n",
      "\u001b[0;31mValueError\u001b[0m: Inputs shape (1,) does not match targets shape (784, 1)."
     ]
    }
   ],
   "source": [
    "disc_loss(gen, disc, real, batch_size, z_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 784)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "real.reshape((-1,len(real))).shape"
   ]
  }
 ],
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