vit_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version 3.11.8 (main, Feb 25 2024, 16:39:33) [GCC 11.4.0]\n",
      "Keras Version: 3.0.4 Backend: jax\n",
      "Jax Version: 0.4.23 Jax backend device: gpu\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import time\n",
    "import pickle\n",
    "from dataclasses import dataclass\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "from typing import Sequence, Any\n",
    "import os\n",
    "\n",
    "import jax\n",
    "from jax import (\n",
    "    Array,\n",
    "    numpy as jnp,\n",
    "    random as jrand\n",
    ")\n",
    "try:\n",
    "    from flash_attn_jax import flash_mha\n",
    "    USE_FLASH_ATT = True\n",
    "except:\n",
    "    USE_FLASH_ATT = False\n",
    "import keras as nn; nn.utils.set_random_seed(42)\n",
    "nn.mixed_precision.set_dtype_policy(\"mixed_float16\") \n",
    "# https://github.com/keras-team/keras/pull/19621#event-12626049549 fixes use of float16 images with nn.ops.image.extract_patches\n",
    "\n",
    "print(\"Python Version\", sys.version); del sys\n",
    "print(f\"Keras Version: {nn.__version__} Backend: {nn.backend.backend()}\")\n",
    "print(f\"Jax Version: {jax.__version__} Jax backend device: {jax.default_backend()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28, 1), (60000,), (10000, 28, 28, 1), (10000,))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train, y_train), (x_val, y_val) = nn.datasets.mnist.load_data()\n",
    "x_train, x_val = [ds[..., None]/255.0 for ds in [x_train, x_val]]\n",
    "H, W = x_train.shape[1:-1]\n",
    "NUM_CLASSES = len(np.unique(y_train.squeeze()).tolist())\n",
    "x_train.shape, y_train.shape, x_val.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_dataset(images, labels, cmap=None):\n",
    "    grid_size = int(len(images)**0.5)\n",
    "    fig, axs = plt.subplots(nrows=grid_size, ncols=grid_size, figsize=(7, 7))\n",
    "\n",
    "    # Flatten the subplots into a 1D array\n",
    "    axs = axs.ravel()\n",
    "\n",
    "    # Loop through the images and plot them on the subplots\n",
    "    for i in range(len(images)):\n",
    "        axs[i].imshow(images[i], cmap=cmap)\n",
    "        axs[i].set_title(f'Label: {labels[i]}')\n",
    "        axs[i].axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_dataset(x_train[:25], y_train[:25], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GetDataset:\n",
    "    def __init__(self, batch_size, datasets:tuple):\n",
    "        self.ds = datasets # (X, y)\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        self.len = len(self.ds[0])\n",
    "\n",
    "    def __iter__(self):\n",
    "        rng = np.random.default_rng()\n",
    "        while True:\n",
    "            idx = rng.integers(low=0, high=self.len, size=self.batch_size)\n",
    "            yield self.ds[0][idx], self.ds[1][idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def patchify(images:Array, patch_size:int):\n",
    "    B, H, W, C = images.shape\n",
    "    num_patches_h = H // patch_size\n",
    "    num_patches_w = W // patch_size\n",
    "    patches = nn.ops.image.extract_patches(images, size=patch_size)\n",
    "    patches = patches.reshape((\n",
    "            B,\n",
    "            num_patches_h * num_patches_w, # N = (H*W)/P**2\n",
    "            patch_size * patch_size * C, # P*2 * C\n",
    "    ))\n",
    "    return patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vvy/.local/lib/python3.11/site-packages/keras/src/backend/jax/numpy.py:845: UserWarning: Explicitly requested dtype float64 requested in eye is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  return jnp.eye(N, M=M, k=k, dtype=dtype)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 196 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_patches(x:Array, patch_size:int):\n",
    "    patches = patchify(x.astype(float), patch_size)\n",
    "    n = int(np.sqrt(patches.shape[1]))\n",
    "    plt.figure(figsize=(5,5))\n",
    "    for i, patch in enumerate(patches[0]):\n",
    "        ax = plt.subplot(n, n, i + 1)\n",
    "        patch_img = jnp.reshape(patch, (patch_size, patch_size, 1))\n",
    "        plt.imshow(patch_img, cmap=\"gray\")\n",
    "        plt.axis(\"off\")\n",
    "\n",
    "visualize_patches(x_train[:1], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "config(patch_size=2, H=28, W=28, N=196, d_model=64, num_heads=8, num_layers=5, num_classes=10, dropout_rate=0, use_flash_att=True, maxlen=197, batch_size=64, num_steps=7000, eval_steps=500, checkpoint_dir='weights/mnist', eval_freq=300, num_grad_accumalation_steps=1, patience=5, beta1=0.9, beta2=0.999, weight_decay=0, clipnorm=None, min_lr=1e-07, max_lr=0.001, decay_steps=7000, warmup_steps=100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@dataclass\n",
    "class config:\n",
    "    # ViT specifics\n",
    "    patch_size:int = 2\n",
    "    H:int = H\n",
    "    W:int = W\n",
    "    N:int = H*W//patch_size**2  # maxlen in transformer\n",
    "    assert N*patch_size**2 == H*W\n",
    "\n",
    "    # Transformer specifics\n",
    "    d_model:int = 64\n",
    "    num_heads:int = 8\n",
    "    num_layers:int = 5\n",
    "    num_classes:int = NUM_CLASSES\n",
    "    dropout_rate:float = 0\n",
    "    assert d_model % 2 == 0\n",
    "    assert d_model % num_heads == 0\n",
    "\n",
    "    use_flash_att:int = USE_FLASH_ATT\n",
    "    maxlen:int = N + 1\n",
    "    \n",
    "    # Tranining Args\n",
    "    batch_size:int = 64\n",
    "    num_steps:int = 7000\n",
    "    eval_steps:int = 500\n",
    "    checkpoint_dir:str = \"weights/mnist\"\n",
    "    eval_freq:int = 300\n",
    "    num_grad_accumalation_steps:int = 1\n",
    "    patience:int = 5\n",
    "\n",
    "    ## Optimizers Args\n",
    "    beta1:float = 0.9\n",
    "    beta2:float = 0.999\n",
    "    weight_decay:float = 0\n",
    "    clipnorm:float|None = None\n",
    "\n",
    "    ## learning rate schedule\n",
    "    min_lr:float = 1e-7\n",
    "    max_lr:float = 1e-3\n",
    "    decay_steps:int = num_steps\n",
    "    warmup_steps:int = 100\n",
    "\n",
    "\n",
    "config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "iter_ds = lambda ds: iter(GetDataset(config.batch_size, ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ViT Architechture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbedding:\n",
    "    \"\"\"```\n",
    "    Sinusoidal Fixed Positional Embeddings\n",
    "    Args:\n",
    "        maxlen:int\n",
    "        dim:int\n",
    "    sinusoidal_embeddings: \n",
    "        pos_emb: (1, maxlen, dim)\n",
    "    ```\"\"\"\n",
    "    def __init__(self, maxlen:int, dim:int):\n",
    "        p, i = jnp.meshgrid(jnp.arange(float(maxlen)), jnp.arange(dim/2)*2)\n",
    "        theta = (p/1e4**(i/dim)).T\n",
    "    \n",
    "        self.pos_emb = jnp.stack([jnp.sin(theta), jnp.cos(theta)], axis=-1)\n",
    "        self.pos_emb = self.pos_emb.reshape((maxlen, dim))[None] # (1, maxlen, dim)\n",
    "    \n",
    "    def sinusoidal_embeddings(self):\n",
    "        return self.pos_emb # (1, maxlen, dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Attention(nn.Layer):\n",
    "    \"\"\"```\n",
    "    Multi-head Attention\n",
    "    Args:\n",
    "        causal:bool\n",
    "        config\n",
    "    Input:\n",
    "        x: shape(B, N, d_model)\n",
    "        training: bool\n",
    "    Output:N\n",
    "        linear_att_out: shape(B, N, d_model)\n",
    "    ```\"\"\"\n",
    "    def __init__(\n",
    "            self,\n",
    "            causal:bool,\n",
    "            config:config,\n",
    "            **kwargs\n",
    "    ):\n",
    "        super().__init__(**kwargs)\n",
    "        assert config.d_model % config.num_heads == 0\n",
    "        self.flash = config.use_flash_att\n",
    "        self.causal = causal\n",
    "        self.num_heads = config.num_heads\n",
    "        self.dim = config.d_model//config.num_heads\n",
    "        \n",
    "        self.wq = nn.layers.Dense(config.d_model, use_bias=False)\n",
    "        self.wk = nn.layers.Dense(config.d_model, use_bias=False)\n",
    "        self.wv = nn.layers.Dense(config.d_model, use_bias=False)\n",
    "        self.dropout = nn.layers.Dropout(config.dropout_rate)\n",
    "\n",
    "        self.wo = nn.layers.Dense(config.d_model)\n",
    "        if causal and (not config.use_flash_att): # when causal and not using flash att\n",
    "            self.causal_mask = jnp.triu(jnp.full(shape=(1, 1, config.maxlen, config.maxlen), fill_value=-jnp.inf), k=1)\n",
    "\n",
    "    def call(\n",
    "            self,\n",
    "            x:Array, # (B, T, d_model)\n",
    "            training:bool\n",
    "    ):\n",
    "        B, T, d_model = x.shape\n",
    "\n",
    "        # compute q, k, v\n",
    "        q = self.wq(x) # (B, T, d_model)\n",
    "        k = self.wk(x) # (B, T, d_model)\n",
    "        v = self.wv(x) # (B, T, d_model)\n",
    "\n",
    "        # compute attention weights\n",
    "        if self.flash:\n",
    "            shape = (B, T, self.num_heads, self.dim)\n",
    "            q, k, v = (\n",
    "                q.reshape(shape).astype(jnp.float16), \n",
    "                k.reshape(shape).astype(jnp.float16), \n",
    "                v.reshape(shape).astype(jnp.float16)\n",
    "            ) # (B, T, h, dim)\n",
    "            att_out = flash_mha(q, k, v, softmax_scale=None, is_causal=self.causal).astype(jnp.float32) # (B, T, h, dim)\n",
    "        else:\n",
    "            shape = (B, self.num_heads, T, self.dim)\n",
    "            q, k, v = q.reshape(shape), k.reshape(shape), v.reshape(shape) # (B, h, T, dim)\n",
    "            att_wei = (q @ jnp.matrix_transpose(k))/self.dim**0.5 # (B, h, T, T) <= (B, h, T, dim) @ (B, h, T, dim).transpose(-1, -2)\n",
    "            # causal mask\n",
    "            if self.causal:\n",
    "                att_wei += self.causal_mask[:, :, :T, :T] # (B, h, T, T)\n",
    "            att_wei = jax.nn.softmax(att_wei, axis=-1) # (B, h, T, T)\n",
    "            # apply attention weights to v\n",
    "            att_out = att_wei @ v # (B, h, T, T) @ (B, h, T, dv) => (B, h, T, dv)\n",
    "\n",
    "        # combine heads\n",
    "        att_out = att_out.reshape((B, T, d_model)) # (B, T, h*dim) ==> (B, T, d_model)\n",
    "\n",
    "        # linear of att_out\n",
    "        linear_att_out = self.wo(att_out)\n",
    "        linear_att_out = self.dropout(linear_att_out, training=training) # (B, T, d_model)\n",
    "        return linear_att_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerBlock(nn.Model):\n",
    "    \"\"\"```\n",
    "    TransformerBlock\n",
    "    Args:\n",
    "        causal:bool\n",
    "        config\n",
    "    Inputs: \n",
    "        inputs: shape(B, T, d_model)\n",
    "    Outputs:\n",
    "        outputs: shape(B, T, d_model)\n",
    "    ```\"\"\"\n",
    "    def __init__(\n",
    "            self, \n",
    "            causal:bool, \n",
    "            config:config,\n",
    "            **kwargs\n",
    "    ):\n",
    "        super().__init__(**kwargs)\n",
    "        dff_in = 4*config.d_model\n",
    "        self.norm1 = nn.layers.LayerNormalization(epsilon=1e-5)\n",
    "        self.mha = Attention(causal, config)\n",
    "\n",
    "        self.ffn = nn.Sequential([\n",
    "            nn.layers.Dense(int(dff_in)),\n",
    "            nn.layers.Activation(lambda x: nn.activations.gelu(x, approximate=True)),\n",
    "            nn.layers.Dense(config.d_model),\n",
    "            nn.layers.Dropout(config.dropout_rate)\n",
    "        ])\n",
    "        self.norm2 = nn.layers.LayerNormalization(epsilon=1e-5)\n",
    "        \n",
    "    def call(self, x:Array, training:bool):\n",
    "        z = x + self.mha(self.norm1(x), training=training)\n",
    "        y = z + self.ffn(self.norm2(z), training=training)\n",
    "        return y # (B, T, d_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VIT(nn.Model):\n",
    "    def __init__(self, config:config, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.patchify = lambda x: patchify(x, config.patch_size)\n",
    "        self.P = config.patch_size\n",
    "        self.N = config.H*config.W//config.patch_size\n",
    "\n",
    "        self.pos_embed = PositionalEmbedding(\n",
    "            maxlen=config.maxlen,\n",
    "            dim=config.d_model\n",
    "        ).sinusoidal_embeddings() # (1, 1+N, d_model)\n",
    "\n",
    "        self.proj_flattened_patches = nn.layers.Dense(\n",
    "            config.d_model\n",
    "        )\n",
    "        self.class_emb = self.add_weight(shape=(1, config.d_model))\n",
    "\n",
    "        self.encoder_layers = [\n",
    "            TransformerBlock(causal=False, config=config)\n",
    "            for _ in range(config.num_layers)\n",
    "        ]\n",
    "        self.norm = nn.layers.LayerNormalization(epsilon=1e-5)\n",
    "        self.mlp_head = nn.layers.Dense(config.num_classes)\n",
    "\n",
    "    def call(self, x:Array, training:bool=True): # (B, H, W, C)\n",
    "        x = self.patchify(x) # (B, N, (P**2)*C)\n",
    "        x = self.proj_flattened_patches(x) # (B, N, d_model)\n",
    "        x = jnp.concatenate(\n",
    "            (\n",
    "                jnp.broadcast_to(self.class_emb.value, (x.shape[0], 1, x.shape[-1])),\n",
    "                x\n",
    "            ),\n",
    "            axis=1\n",
    "        ) # (B, 1+N, d_model)\n",
    "        x += self.pos_embed # (B, 1+N, d_model)\n",
    "\n",
    "        for encoder_layer in self.encoder_layers:\n",
    "            x = encoder_layer(x, training=training) # (B, 1+N, d_model)\n",
    "\n",
    "        class_rep = x[:, 0, :] # (B, d_model)\n",
    "        x = self.norm(class_rep) # (B, d_model)\n",
    "        x = self.mlp_head(x) # (B, num_classes)\n",
    "        return x # logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(model:nn.Model, input_shape:tuple|None=None, input_tensor:Array|None=None):\n",
    "    assert not all([input_shape is None, input_tensor is None])\n",
    "    if input_tensor is None:\n",
    "        input_tensor = jrand.uniform(\n",
    "            jrand.PRNGKey(32344), \n",
    "            shape=input_shape,\n",
    "        )\n",
    "    blabla = model(\n",
    "        input_tensor,\n",
    "        training=True\n",
    "    ); del blabla\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"vit\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"vit\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">    Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ ?                         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block               │ ?                         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,792</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_1             │ ?                         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,792</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_2             │ ?                         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,792</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_3             │ ?                         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,792</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_4             │ ?                         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">49,792</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ layer_normalization_10          │ ?                         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalization</span>)            │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ dense_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
       "└─────────────────────────────────┴───────────────────────────┴────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ ?                         │        \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block               │ ?                         │     \u001b[38;5;34m49,792\u001b[0m │\n",
       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_1             │ ?                         │     \u001b[38;5;34m49,792\u001b[0m │\n",
       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_2             │ ?                         │     \u001b[38;5;34m49,792\u001b[0m │\n",
       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_3             │ ?                         │     \u001b[38;5;34m49,792\u001b[0m │\n",
       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ transformer_block_4             │ ?                         │     \u001b[38;5;34m49,792\u001b[0m │\n",
       "│ (\u001b[38;5;33mTransformerBlock\u001b[0m)              │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ layer_normalization_10          │ ?                         │        \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mLayerNormalization\u001b[0m)            │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ dense_31 (\u001b[38;5;33mDense\u001b[0m)                │ ?                         │        \u001b[38;5;34m650\u001b[0m │\n",
       "└─────────────────────────────────┴───────────────────────────┴────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">250,122</span> (977.04 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m250,122\u001b[0m (977.04 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">250,122</span> (977.04 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m250,122\u001b[0m (977.04 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model(VIT(config), input_tensor=x_train[:2])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save weights ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_object(dir_suffix_ftype:str, obj:Any):\n",
    "    \"\"\"\n",
    "    dir_suffix_ftype: directory suffix and file type separated by \"|\"\n",
    "    obj: Anything which is to be stored\n",
    "    \"\"\"\n",
    "    dir, suffix, ftype = dir_suffix_ftype.split(\"|\"); path = os.path.join(dir, \"\".join([suffix, f\".{ftype}\"]))\n",
    "    os.makedirs(name=dir, exist_ok=True)\n",
    "    with open(path, \"wb\") as file:\n",
    "        pickle.dump(obj=obj, file=file, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    return path\n",
    "\n",
    "def load_object(path:str):\n",
    "    with open(path, \"rb\") as file:\n",
    "        obj = pickle.load(file)\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lr Schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = nn.optimizers.schedules.CosineDecay(\n",
    "    initial_learning_rate=config.min_lr,\n",
    "    decay_steps=config.decay_steps,\n",
    "    warmup_steps=config.warmup_steps,\n",
    "    warmup_target=config.max_lr,\n",
    "    alpha=config.min_lr/config.max_lr\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = jnp.arange(1, config.num_steps+1)\n",
    "lrs = jax.vmap(learning_rate)(steps)\n",
    "\n",
    "plt.plot(steps, lrs); del lrs, steps\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Learning Rate\")\n",
    "plt.title(\"Learning Rate vs Steps\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iterator, val_iterator = iter_ds((x_train, y_train)), iter_ds((x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = nn.optimizers.LossScaleOptimizer(\n",
    "    nn.optimizers.AdamW(\n",
    "        learning_rate=learning_rate,\n",
    "        beta_1=config.beta1,\n",
    "        beta_2=config.beta2,\n",
    "        clipnorm=config.clipnorm,\n",
    "        weight_decay=config.weight_decay\n",
    "    )\n",
    ")\n",
    "loss_fn = nn.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "\n",
    "@jax.jit\n",
    "def get_accuracy(y_true:Array, logits:Array): # (B, T, num_classes), (B, T)\n",
    "    batched_num_correct = (logits.argmax(-1)==y_true).sum(-1)/y_true.shape[-1] # (B,)\n",
    "    accuracy = batched_num_correct.mean()\n",
    "    return accuracy\n",
    "\n",
    "@partial(jax.jit, static_argnums=-1)\n",
    "def compute_loss(trainable_variables:list, non_trainable_variables:list, X_batch:Array, y_batch:Array, num_grad_accumalation_steps:int):\n",
    "    logits, non_trainable_variables = model.stateless_call(\n",
    "        trainable_variables,  non_trainable_variables,\n",
    "        X_batch\n",
    "    )\n",
    "    loss = loss_fn(y_batch, logits)\n",
    "    accuracy = get_accuracy(y_batch, logits)\n",
    "    unscaled_loss = loss/num_grad_accumalation_steps\n",
    "    scaled_loss = optimizer.scale_loss(unscaled_loss)\n",
    "    return scaled_loss, (unscaled_loss, accuracy, non_trainable_variables)\n",
    "grad_fn = jax.value_and_grad(compute_loss, has_aux=True)\n",
    "\n",
    "@partial(jax.jit, static_argnums=-1)\n",
    "def mini_step(train_state:Sequence[list], X_batch:Array, y_batch:Array, num_grad_accumalation_steps:int):\n",
    "    trainable_variables, non_trainable_variables = train_state\n",
    "\n",
    "    (_, aux), scaled_grad = grad_fn(\n",
    "        trainable_variables, non_trainable_variables, X_batch, y_batch,\n",
    "        num_grad_accumalation_steps\n",
    "    )\n",
    "    (unscaled_loss, accuracy, non_trainable_variables) = aux\n",
    "    return scaled_grad, (unscaled_loss, accuracy), (trainable_variables, non_trainable_variables)\n",
    "\n",
    "@jax.jit\n",
    "def update_params(grads:list, trainable_variables:list, optimizer_variables:list):\n",
    "    trainable_variables, optimizer_variables = optimizer.stateless_apply( # also handles scaled grads if LossScalerOPtimizer is used\n",
    "        optimizer_variables, grads, trainable_variables\n",
    "    ) # returns updated trainable_variables\n",
    "    return trainable_variables, optimizer_variables\n",
    "\n",
    "def evaluate(train_state:Sequence[list]):\n",
    "    trainable_variables, non_trainable_variables = train_state\n",
    "    mean_losses = []; mean_accuracies = []\n",
    "    for eval_batch_iter in [train_iterator, val_iterator]:\n",
    "        X_batch, y_batch = next(eval_batch_iter)\n",
    "        losses = jnp.empty(config.eval_steps)\n",
    "        accuracies = jnp.empty_like(losses)\n",
    "\n",
    "        for eval_step in range(config.eval_steps):\n",
    "            _, (unscaled_loss, accuracy, non_trainable_variables) = compute_loss(\n",
    "                trainable_variables, non_trainable_variables,\n",
    "                jnp.array(X_batch), jnp.array(y_batch), 1\n",
    "            )\n",
    "            losses = losses.at[eval_step].set(unscaled_loss)\n",
    "            accuracies = accuracies.at[eval_step].set(accuracy)\n",
    "            X_batch, y_batch = next(eval_batch_iter)\n",
    "        mean_losses.append(losses.mean())\n",
    "        mean_accuracies.append(accuracies.mean())\n",
    "    return mean_losses, mean_accuracies # ([train_loss, val_loss], [train_accuracy, val_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(return_best_states:bool):\n",
    "    step = 1; wait = 0\n",
    "    training_losses = {\"train\": []}\n",
    "\n",
    "    trainable_variables = model.trainable_variables; optimizer.build(trainable_variables)\n",
    "    non_trainable_variables = model.non_trainable_variables\n",
    "    optimizer_variables = optimizer.variables\n",
    "\n",
    "    best_step = 1\n",
    "    best_train_state = (trainable_variables, non_trainable_variables)\n",
    "    best_val_loss = 1e8\n",
    "\n",
    "    t0 = time.time()\n",
    "    print(\"Training about to start...\")\n",
    "    X_batch, y_batch = next(train_iterator)\n",
    "    # TODO: Optimize Train Loop to reduce time per step\n",
    "    while True:\n",
    "        # condition to terminate\n",
    "        if step > config.num_steps or wait > config.patience:\n",
    "            print(f\"Early Stopping at Step {step}.\" if wait > config.patience else \"Training Terminated.\")\n",
    "            break\n",
    "        \n",
    "        # train model\n",
    "        grads = jax.tree_util.tree_map(jnp.zeros_like, trainable_variables)\n",
    "        for _ in range(config.num_grad_accumalation_steps):\n",
    "            grad, (loss, accuracy), (trainable_variables, non_trainable_variables) = mini_step(\n",
    "                (trainable_variables, non_trainable_variables), \n",
    "                jnp.array(X_batch), jnp.array(y_batch),\n",
    "                config.num_grad_accumalation_steps\n",
    "            )\n",
    "            grads = jax.tree_util.tree_map(\n",
    "                lambda g1, g2: jnp.add(g1, g2), grads, jax.tree_util.tree_map(jnp.asarray, grad)\n",
    "            ) # sum grads for grad accumation\n",
    "            X_batch, y_batch = next(train_iterator)\n",
    "        grad = None # save memory\n",
    "\n",
    "        loss = loss*config.num_grad_accumalation_steps # loss from last mini-step\n",
    "\n",
    "        trainable_variables, optimizer_variables = update_params(\n",
    "            grads, trainable_variables, optimizer_variables\n",
    "        )\n",
    "        grads = None # save memory\n",
    "\n",
    "        if step % config.eval_freq == 0 or step == config.num_steps:\n",
    "            print(\"Estimating Losses...\")\n",
    "            mean_losses, mean_accuracies = evaluate((trainable_variables, non_trainable_variables))\n",
    "            print(\n",
    "                f\"\\t| Training Loss: {mean_losses[0]:.4f} || Training Accuracy: {mean_accuracies[0]:.4f} |\" \n",
    "                f\"| Validation Loss: {mean_losses[1]:.4f} || Validation Accuracy: {mean_accuracies[1]:.4f} |\"\n",
    "            )\n",
    "            \n",
    "            _ = save_object(\n",
    "                config.checkpoint_dir+f\"|trstate{step}|weights\",\n",
    "                obj=best_train_state\n",
    "            )\n",
    "            print(f\"Saved weights of step {step}.\")\n",
    "\n",
    "            if mean_losses[1] < best_val_loss:\n",
    "                best_val_loss = mean_losses[1]\n",
    "                best_train_state = (trainable_variables, non_trainable_variables)\n",
    "                best_step = step\n",
    "                wait = 0\n",
    "            else:\n",
    "                wait += 1\n",
    "\n",
    "        # time\n",
    "        t1 = time.time()\n",
    "        dt = t1-t0; t0 = t1\n",
    "\n",
    "        # print the essentials\n",
    "        print(\n",
    "            f\"| Step: {step} || Loss: {loss:.4f} || Accuracy: {accuracy:.4f} |\"\n",
    "            f\"| LR: {learning_rate(step):e} || dt: {dt*1000:.2f}ms |\"\n",
    "        )\n",
    "        training_losses[\"train\"].append(loss.tolist())\n",
    "        step += 1\n",
    "    \n",
    "    if return_best_states:\n",
    "        print(f\"Best Weights are from Step {best_step}\")\n",
    "        print(\"With an Estimated Validation Loss of\", best_val_loss)\n",
    "        return best_train_state, training_losses\n",
    "\n",
    "    return (trainable_variables, non_trainable_variables), training_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_train_states, train_losses = train(return_best_states=True) # ~5mins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Estimating Losses...\n",
    "\t| Training Loss: 0.0197 || Training Accuracy: 0.9942 || Validation Loss: 0.0607 || Validation Accuracy: 0.9815 |\n",
    "Saved weights of step 7000.\n",
    "| Step: 7000 || Loss: 0.0028 || Accuracy: 1.0000 || LR: 6.034003e-07 || dt: 1564.83ms |\n",
    "Training Terminated.\n",
    "Best Weights are from Step 6900\n",
    "With an Estimated Validation Loss of 0.056833696\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = save_object(\n",
    "        config.checkpoint_dir+f\"|trstate|weights\",\n",
    "        obj=best_train_states\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+6NSpk1qMQH77Fb3WFD799FP89NNP+PLLLzXOFfTkFZ6YDZS+B0MfRYdJhBC4du0aWrZsCeBJ+3h4eBi9fXx9fREdHa1xvGCIt+jnqSwqVaqE+vXr48KFC6pjupLJ+vXrY//+/ejSpYteidq1a9cghFC739WrVwFAbdVk5cqVMW7cOIwbNw5paWno1q0b5syZw+SmHOOwFJV7Bf8bLvy/35ycHCxfvlyqkNTY29ujV69eCA4OVluRc+3aNezZs8co79G+fXt4eXlh5cqVasNHe/bsweXLlxEYGAggf0VSVlaW2rX169eHu7u76rrHjx9r9CQU9AwUNzTVq1cvODk54bvvvlO7fs2aNUhOTlbFUGD48OHIzs7Ghg0bsHfvXgwbNkztfEBAADw8PPDFF19onftz//59nbGURv369fHKK6/ghx9+UBumA/ITiKpVq2qsajLlZ2zjxo1ITU1Vvf71118RFxenmqfVrl071K9fH998843a3KUCZWmf/v37IyIiAsePH1cdS09Px6pVq+Dn54emTZsafM9z587hwYMHGsdv376NS5cuqQ2DFey/UzSZHDZsGPLy8rQOYeXm5mqUv3fvnmp1FwCkpKRg48aNaN26tao3sPCqRiC/t6tBgwZ6zQEiy8WeGyr3OnfujEqVKmHMmDGYPHkyZDIZNm3aZFHDQnPmzEFISAi6dOmCt99+G3l5eVi6dCmaN2+OqKgove6hUCi0dttXrlwZ77zzDr788kuMGzcO3bt3x4gRI5CQkIAlS5bAz88P//vf/wDk/6+1Z8+eGDZsGJo2bQoHBwfs2LEDCQkJePnllwEAGzZswPLlyzF48GDUr18fqamp+PHHH+Hh4YH+/fvrjK9atWqYMWMGgoKC0LdvXwwYMADR0dFYvnw5nn76aY0NGdu2bYsGDRrgk08+QXZ2ttqQFJCfUKxYsQKvvvoq2rZti5dffhnVqlVDbGws/vrrL3Tp0gVLly7Vq+309cknn2DTpk2Ijo5Gs2bN1M698cYbWLBgAd544w20b98ehw8fVvUCmELlypXRtWtXjBs3DgkJCVi8eDEaNGigWsJtZ2eH1atXo1+/fmjWrBnGjRuHmjVr4u7duzh48CA8PDywc+fOUr339OnT8fPPP6Nfv36YPHkyKleujA0bNuDmzZv47bffSjX8FxoaitmzZ2PAgAHo1KkT3NzccOPGDaxduxbZ2dlqewW1a9cOADB58mQEBATA3t4eL7/8Mrp374633noL8+fPR1RUFPr06QNHR0fExMRg+/btWLJkCV566SXVfZ566im8/vrrOHXqFLy9vbF27VokJCRg3bp1qjJNmzZFjx490K5dO1SuXBmRkZH49ddfMWnSpFK1HVkIqZZpERVH11LwwstzC/v7779Fp06dhIuLi6hRo4b48MMPxb59+wQAcfDgQVU5XUvBtS2Nho4lvkXLaFsG6+vrq7a8VwghwsLCRJs2bYSTk5OoX7++WL16tZg2bZpwdnbW0QpPFCx11vZVv359Vblt27aJNm3aCLlcLipXrixGjRol/v33X9X5Bw8eiIkTJ4rGjRuLChUqCE9PT9GxY0fxyy+/qMqcOXNGjBgxQtSpU0fI5XLh5eUlnn/+eREZGVlinELkL/1u3LixcHR0FN7e3uLtt98Wjx8/1lr2k08+EQBEgwYNdN7v4MGDIiAgQHh6egpnZ2dRv359MXbsWLV4SloqX1ThpeBFFbR10c9aRkaGeP3114Wnp6dwd3cXw4YNE4mJiTo/J/fv39e4r7YYi36uC5aC//zzz2LGjBnCy8tLuLi4iMDAQHH79m2N68+ePStefPFFUaVKFSGXy4Wvr68YNmyYCAsLKzGm4ly/fl289NJLomLFisLZ2Vl06NBB7Nq1S6Ocrn8DRd24cUPMmjVLdOrUSXh5eQkHBwdRrVo1ERgYKA4cOKBWNjc3V7z77ruiWrVqQiaTafy7W7VqlWjXrp1wcXER7u7uokWLFuLDDz8U9+7dU5Xx9fUVgYGBYt++faJly5ZCLpeLxo0bi+3bt6vd67PPPhMdOnQQFStWFC4uLqJx48bi888/Fzk5OXq3FVkemRAW9N9bIhszaNAgLkMlMgE/Pz80b94cu3btkjoUkgDn3BCZSeGdZYH8CaO7d+9Gjx49pAmIiMhKcc4NkZnUq1cPY8eOVe35smLFCjg5OeHDDz+UOjQiIqvC5IbITPr27Yuff/4Z8fHxkMvl8Pf3xxdffKGxWRsREZUN59wQERGRVeGcGyIiIrIqTG6IiIjIqtjcnBulUol79+7B3d1d5xbfREREZFmEEEhNTUWNGjVK3EjS5pKbe/fuoXbt2lKHQURERKVw584d1KpVq9gyNpfcuLu7A8hvHA8PD6PeW6FQICQkRLUluK2x9foDbANbrz/ANmD9bbv+gOnaICUlBbVr11b9HS+OzSU3BUNRHh4eJkluXF1d4eHhYZMfaluvP8A2sPX6A2wD1t+26w+Yvg30mVLCCcVERERkVSRNblasWIGWLVuqelH8/f2xZ8+eYq/Zvn07GjduDGdnZ7Ro0QK7d+82U7RERERUHkia3NSqVQsLFizA6dOnERkZieeeew4DBw7ExYsXtZY/duwYRowYgddffx1nz57FoEGDMGjQIFy4cMHMkRMREZGlkjS5eeGFF9C/f380bNgQTz31FD7//HO4ubnhxIkTWssvWbIEffv2xQcffIAmTZpg3rx5aNu2LZYuXWrmyImIiMhSWcyE4ry8PGzfvh3p6enw9/fXWub48eOYOnWq2rGAgAAEBwfrvG92djays7NVr1NSUgDkT3hSKBRlD7yQgvsZ+77lha3XH2Ab2Hr9AbYB62/b9QdM1waG3E/yZ0udP38e/v7+yMrKgpubG7Zs2YL+/ftrLevk5IQNGzZgxIgRqmPLly9HUFAQEhIStF4zZ84cBAUFaRzfsmULXF1djVMJIiIiMqmMjAyMHDkSycnJJa52lrznplGjRoiKikJycjJ+/fVXjBkzBocOHULTpk2Ncv8ZM2ao9fYUrJPv06ePSZaCh4aGonfv3ja5BNDW6w+wDWy9/gDbgPW37foDpmuDgpEXfUie3Dg5OaFBgwYAgHbt2uHUqVNYsmQJfvjhB42yPj4+Gj00CQkJ8PHx0Xl/uVwOuVyucdzR0dFkHzxT3rs8sPX6A2wDW68/wDZg/W27/oDx28CQe1ncPjdKpVJtjkxh/v7+CAsLUzsWGhqqc44OERER2R5Je25mzJiBfv36oU6dOkhNTcWWLVsQHh6Offv2AQBGjx6NmjVrYv78+QCAKVOmoHv37li4cCECAwOxdetWREZGYtWqVVJWg4iIiCyIpMlNYmIiRo8ejbi4OHh6eqJly5bYt28fevfuDQCIjY1Ve/Jn586dsWXLFnz66af4+OOP0bBhQwQHB6N58+ZSVYGIiIgsjKTJzZo1a4o9Hx4ernFs6NChGDp0qIkiIiIiovLO4ubclGc5uUrkSbqwnoiIiCRfLWUtsnPz0HFBOJxhjxcCpY6GiIjIdjG5MZLrielIy85FGkp+FDsRERGZDoeljETGnIaIiMgiMLkxAYmfaEFERGTTmNwYSeGeGyVzGyIiIskwuTESWaG5Nuy5ISIikg6TGxNgakNERCQdJjdGwmEpIiIiy8DkxkjUFktxWIqIiEgyTG6M5FF6jup79twQERFJh8mNkdx+lKH6XnDWDRERkWSY3BiJnazwaikJAyEiIrJxTG6MxI4TiomIiCwCkxsjsVN7/gKzGyIiIqkwuTGSwrkNh6WIiIikw+TGSAr33HBYioiISDpMboxEreeGw1JERESSYXJjJOrPlpIwECIiIhvH5MZI7NR6boiIiEgqTG6MRH2xFNMbIiIiqTC5MRpOKCYiIrIETG5MgLkNERGRdJjcGIn6PjdMb4iIiKTC5MYEmNoQERFJh8mNCbDjhoiISDpMbozEv34V1fccliIiIpIOkxsj8XB2VH3P1IaIiEg6TG6MyNkxvznZcUNERCQdJjdGVPDwTD5bioiISDpMboyoYDU4N/EjIiKSDpMbYyrIbpjcEBERSYbJjREVPBmcw1JERETSYXJjRAVPBueEYiIiIukwuTGigkcwcM4NERGRdJjcGJFqWIpdN0RERJJhcmNEBT03TG2IiIikw+TGFJjdEBERSYbJjRFxEz8iIiLpMbkxIk4oJiIikh6TGyNS7eHH5IaIiEgyTG6MSMZhKSIiIskxuTEi9twQERFJj8mNEcm4QzEREZHkmNwYEYeliIiIpMfkxog4LEVERCQ9JjdGxB2KiYiIpCdpcjN//nw8/fTTcHd3h5eXFwYNGoTo6Ohir1m/fj1kMpnal7Ozs5kiLp5qWIpdN0RERJKRNLk5dOgQJk6ciBMnTiA0NBQKhQJ9+vRBenp6sdd5eHggLi5O9XX79m0zRVw8DksRERFJz0HKN9+7d6/a6/Xr18PLywunT59Gt27ddF4nk8ng4+Nj6vAMxmEpIiIi6Uma3BSVnJwMAKhcuXKx5dLS0uDr6wulUom2bdviiy++QLNmzbSWzc7ORnZ2tup1SkoKAEChUEChUBgp8nyxjzIBABlZOUa/d3lQUGdbrHsBW28DW68/wDZg/W27/oDp2sCQ+8mEhUwQUSqVGDBgAJKSknD06FGd5Y4fP46YmBi0bNkSycnJ+Oabb3D48GFcvHgRtWrV0ig/Z84cBAUFaRzfsmULXF1djVqHKcfzc8WWlZV4vZHSqPcmIiKyZRkZGRg5ciSSk5Ph4eFRbFmLSW7efvtt7NmzB0ePHtWapOiiUCjQpEkTjBgxAvPmzdM4r63npnbt2njw4EGJjWOohjNDAAC+lV2w/3/PGPXe5YFCoUBoaCh69+4NR0dHqcORhK23ga3XH2AbsP62XX/AdG2QkpKCqlWr6pXcWMSw1KRJk7Br1y4cPnzYoMQGABwdHdGmTRtcu3ZN63m5XA65XK71OlN98O6n5djshxowbduWF7beBrZef4BtwPrbdv0B47eBIfeSdLWUEAKTJk3Cjh07cODAAdStW9fge+Tl5eH8+fOoXr26CSIsnYycPByJuS91GERERDZJ0uRm4sSJ+Omnn7Blyxa4u7sjPj4e8fHxyMzMVJUZPXo0ZsyYoXo9d+5chISE4MaNGzhz5gxeeeUV3L59G2+88YYUVVDj6fKkI2zDsVvSBUJERGTDJB2WWrFiBQCgR48easfXrVuHsWPHAgBiY2NhZ/ckB3v8+DHGjx+P+Ph4VKpUCe3atcOxY8fQtGlTc4WtU7MaHjh2/ZHUYRAREdk0SZMbfeYyh4eHq71etGgRFi1aZKKIykam2saPiIiIpMJnSxmRXaHcxjLWoBEREdkeJjdGZCdjzw0REZHUmNwYUeHcJjnTdnenJCIikhKTGyMq3HMTefsxfj/zLx6n50gYERERke2xiE38rEVMYpra66m/nIO9nQzXv+gvUURERES2hz03RnTncabGsTwlZxYTERGZE5MbIiIisipMboiIiMiqMLkhIiIiq8LkhoiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqTG6IiIjIqjC5MQM+goGIiMh8mNyYQdjlBKlDICIishlMbswgNStX6hCIiIhsBpMbM8gTHJYiIiIyFyY3RuTtIdd6PORivJkjISIisl1Mbozoyxebaz2+/3KimSMhIiKyXUxujMjRXiZ1CERERDaPyY0RcWoNERGR9JjcGJG9HXtuiIiIpMbkxoja1K6Ihh5KeDg7SB0KERGRzWJyY0T2djJMaqbE6U+ekzoUIiIim8XkhoiIiKwKkxsiIiKyKkxuzCQ5QyF1CERERDaByY2JNK3uofZ61ZHrEkVCRERkW5jcmMiOiZ3VXufkKiWKhIiIyLYwuTERuYO92uvYRxkSRUJERGRbmNyYiSKP2xcTERGZA5MbM+HmxURERObB5MZsmN0QERGZA5MbE2pVu6LUIRAREdkcJjcmNOv5pqrvOSxFRERkHkxuTEgm0/49ERERmQ6TGzORcc4NERGRWTC5MRP23BAREZkHkxszYXJDRERkHkxuTEhw3z4iIiKzY3JjQqJQdmPHrhsiIiKzYHJjQspCPTcNvdylC4SIiMiGMLkxIWWhnptalVwkjISIiMh2SJrczJ8/H08//TTc3d3h5eWFQYMGITo6usTrtm/fjsaNG8PZ2RktWrTA7t27zRCt4ZSFum44/YaIiMg8JE1uDh06hIkTJ+LEiRMIDQ2FQqFAnz59kJ6ervOaY8eOYcSIEXj99ddx9uxZDBo0CIMGDcKFCxfMGLl+mtX0VH2v5OxiIiIis3CQ8s337t2r9nr9+vXw8vLC6dOn0a1bN63XLFmyBH379sUHH3wAAJg3bx5CQ0OxdOlSrFy50uQxG8LTxRHtfSsh8vZjdt0QERGZiaTJTVHJyckAgMqVK+ssc/z4cUydOlXtWEBAAIKDg7WWz87ORnZ2tup1SkoKAEChUEChUJQxYnUF9yt8Xw/n/CZW5OYa/f0sjbb62xpbbwNbrz/ANmD9bbv+gOnawJD7yYSwjPESpVKJAQMGICkpCUePHtVZzsnJCRs2bMCIESNUx5YvX46goCAkJCRolJ8zZw6CgoI0jm/ZsgWurq7GCb4YP16xw4XHdhheLw+dvS2iqYmIiMqdjIwMjBw5EsnJyfDw8Ci2rMX03EycOBEXLlwoNrEpjRkzZqj19KSkpKB27dro06dPiY1jKIVCgdDQUPTu3RuOjo4AgF1JUbjwOBHNmjdH/6drG/X9LI22+tsaW28DW68/wDZg/W27/oDp2qBg5EUfFpHcTJo0Cbt27cLhw4dRq1atYsv6+Pho9NAkJCTAx8dHa3m5XA65XK5x3NHR0WQfvML3LuiriU/JsZkPuinbtryw9Taw9foDbAPW37brDxi/DQy5l6SrpYQQmDRpEnbs2IEDBw6gbt26JV7j7++PsLAwtWOhoaHw9/c3VZhlsv9yIgBgefh1iSMhIiKyDZL23EycOBFbtmzBH3/8AXd3d8THxwMAPD094eKSv+nd6NGjUbNmTcyfPx8AMGXKFHTv3h0LFy5EYGAgtm7disjISKxatUqyehAREZHlkLTnZsWKFUhOTkaPHj1QvXp11de2bdtUZWJjYxEXF6d63blzZ2zZsgWrVq1Cq1at8OuvvyI4OBjNmzeXogpERERkYSTtudFnoVZ4eLjGsaFDh2Lo0KEmiIiIiIjKOz5bioiIiKwKkxsiIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqTG6IiIjIqjC5MbGP+jYGAHi62PYzRoiIiMyFyY2J1a6c/xgJIQTSs3MljoaIiMj6Mbkxk5SsXLzw/VGpwyAiIrJ6TG5M7Pj1h6rvbzxIlzASIiIi28DkxsTuJmVKHQIREZFNYXJjYsqSnw1KRERERsTkxsRkUgdARERkY5jcmJgdsxsiIiKzYnJjYjIZsxsiIiJzYnJDREREVoXJjYmx34aIiMi8mNyYGEeliIiIzIvJjckxuyEiIjInJjcmxp4bIiIi82JyY2LMbYiIiMzL4OQmMzMTGRkZqte3b9/G4sWLERISYtTArAV7boiIiMzL4ORm4MCB2LhxIwAgKSkJHTt2xMKFCzFw4ECsWLHC6AGWdzL23RAREZmVwcnNmTNn8MwzzwAAfv31V3h7e+P27dvYuHEjvvvuO6MHWN6x54aIiMi8DE5uMjIy4O7uDgAICQnBiy++CDs7O3Tq1Am3b982eoDlHZMbIiIi8zI4uWnQoAGCg4Nx584d7Nu3D3369AEAJCYmwsPDw+gBlnccliIiIjIvg5ObWbNm4f3334efnx86duwIf39/APm9OG3atDF6gOUecxsiIiKzcjD0gpdeegldu3ZFXFwcWrVqpTres2dPDB482KjBWQPmNkREROZlcHIDAD4+PvDx8QEApKSk4MCBA2jUqBEaN25s1OCIiIiIDGXwsNSwYcOwdOlSAPl73rRv3x7Dhg1Dy5Yt8dtvvxk9wPLOjjOKiYiIzMrg5Obw4cOqpeA7duyAEAJJSUn47rvv8Nlnnxk9wPKOuQ0REZF5GZzcJCcno3LlygCAvXv3YsiQIXB1dUVgYCBiYmKMHmB517OJt9QhEBER2RSDk5vatWvj+PHjSE9Px969e1VLwR8/fgxnZ2ejB1je9WzsJXUIRERENsXgCcXvvfceRo0aBTc3N/j6+qJHjx4A8oerWrRoYez4yj0OSxEREZmXwcnNO++8gw4dOuDOnTvo3bs37OzyO3/q1avHOTdacEIxERGReZVqKXj79u3Rvn17CCEghIBMJkNgYKCxYyMiIiIymMFzbgBg48aNaNGiBVxcXODi4oKWLVti06ZNxo7NKrDjhoiIyLwM7rn59ttvMXPmTEyaNAldunQBABw9ehQTJkzAgwcP8L///c/oQZZnHJYiIiIyL4OTm++//x4rVqzA6NGjVccGDBiAZs2aYc6cOUxuimBqQ0REZF4GD0vFxcWhc+fOGsc7d+6MuLg4owRlTWTsuSEiIjIrg5ObBg0a4JdfftE4vm3bNjRs2NAoQVkTO+Y2REREZmXwsFRQUBCGDx+Ow4cPq+bc/P333wgLC9Oa9Ng69twQERGZl8E9N0OGDMHJkydRtWpVBAcHIzg4GFWrVkVERAQGDx5sihiJiIiI9FaqfW7atWuHn376Se1YYmIivvjiC3z88cdGCYyIiIioNEq1z402cXFxmDlzprFuR0RERFQqRktuSuPw4cN44YUXUKNGDchkMgQHBxdbPjw8HDKZTOMrPj7ePAETERGRxZM0uUlPT0erVq2wbNkyg66Ljo5GXFyc6svLi0/eJiIionylmnNjLP369UO/fv0Mvs7LywsVK1Y0fkBERERU7umd3EydOrXY8/fv3y9zMPpq3bo1srOz0bx5c8yZM0e1JJ2IiIhI7+Tm7NmzJZbp1q1bmYIpSfXq1bFy5Uq0b98e2dnZWL16NXr06IGTJ0+ibdu2Wq/Jzs5Gdna26nVKSgoAQKFQQKFQGDW+gvsVd19jv6cl0af+1s7W28DW6w+wDVh/264/YLo2MOR+MiGEMOq7l5JMJsOOHTswaNAgg67r3r076tSpo/Op5HPmzEFQUJDG8S1btsDV1bU0oRpsyvEnOeQS/1yzvCcREZE1ycjIwMiRI5GcnAwPD49iy0o658YYOnTogKNHj+o8P2PGDLUhtZSUFNSuXRt9+vQpsXEMpVAoEBoait69e8PR0VF1fMrxENX3/fv3N+p7WhJd9bcltt4Gtl5/gG3A+tt2/QHTtUHByIs+yn1yExUVherVq+s8L5fLIZfLNY47Ojqa7INX3L1t4cNuyrYtL2y9DWy9/gDbgPW37foDxm8DQ+4laXKTlpaGa9euqV7fvHkTUVFRqFy5MurUqYMZM2bg7t272LhxIwBg8eLFqFu3Lpo1a4asrCysXr0aBw4cQEhIiK63ICIiIhsjaXITGRmJZ599VvW6YPhozJgxWL9+PeLi4hAbG6s6n5OTg2nTpuHu3btwdXVFy5YtsX//frV7EBERkW2TNLnp0aMHipvPvH79erXXH374IT788EMTR0VERETlWamSm6SkJERERCAxMRFKpVLt3OjRo40SGBEREVFpGJzc7Ny5E6NGjUJaWho8PDwgk8lU52QyGZMbIiIikpTBz5aaNm0aXnvtNaSlpSEpKQmPHz9WfT169MgUMRIRERHpzeDk5u7du5g8ebLZNsAjIiIiMoTByU1AQAAiIyNNEQsRERFRmRk85yYwMBAffPABLl26hBYtWmhsqjNgwACjBUdERERkKIOTm/HjxwMA5s6dq3FOJpMhLy+v7FFZsUfpOahcwUnqMIiIiKyWwcNSSqVS5xcTm5JN+yVK6hCIiIismsHJDZXN5bhUqUMgIiKyanoNS3333Xd488034ezsjO+++67YspMnTzZKYNaq0LZAREREZAJ6JTeLFi3CqFGj4OzsjEWLFuksJ5PJmNyUgLkNERGRaemV3Ny8eVPr90RERESWhnNuzEzGcSkiIiKTKtWDM//991/8+eefiI2NRU5Ojtq5b7/91iiBWau7SZlIzVLA3dmx5MJERERkMIOTm7CwMAwYMAD16tXDlStX0Lx5c9y6dQtCCLRt29YUMVqd19afwvYJnaUOg4iIyCoZPCw1Y8YMvP/++zh//jycnZ3x22+/4c6dO+jevTuGDh1qihitzqlbj6UOgYiIyGoZnNxcvnwZo0ePBgA4ODggMzMTbm5umDt3Lr788kujB0hERERkCIOTmwoVKqjm2VSvXh3Xr19XnXvw4IHxIiMiIiIqBYPn3HTq1AlHjx5FkyZN0L9/f0ybNg3nz5/H77//jk6dOpkiRiIiIiK9GZzcfPvtt0hLSwMABAUFIS0tDdu2bUPDhg25UoqIiIgkZ1Byk5eXh3///RctW7YEkD9EtXLlSpMERkRERFQaBs25sbe3R58+ffD4MVf7EBERkWUyeEJx8+bNcePGDVPEYlMGLfsbPx5mOxIRERmbwcnNZ599hvfffx+7du1CXFwcUlJS1L5IP1F3kvD57stSh0FERGR19J5zM3fuXEybNg39+/cHAAwYMEDtOUlCCMhkMuTl5Rk/SiIiIiI96Z3cBAUFYcKECTh48KAp4yEiIiIqE72TGyEEAKB79+4mC4aIiIiorAyac1N4GIqIiIjIEhm0z81TTz1VYoLz6NGjMgVkjWpVcsG/jzOlDoOIiMgmGJTcBAUFwdPT01SxWK2pvZ/C1F/OSR0GERGRTTAouXn55Zfh5eVlqlis1n/TlYiIiMgM9J5zw/k2pcfchoiIyHz0Tm4Eux9KTcm2IyIiMhu9h6WUSqUp47BuzG2IiIjMxuDHL5DhBLMbIiIis2FyYwYuTgbN2yYiIqIyYHJjBv2a+0gdAhERkc1gcmMGjvZsZiIiInPhX10iIiKyKkxuiIiIyKowuSEiIiKrwuSGiIiIrAqTGyIiIrIqTG6IiIjIqjC5ISIiIqvC5MZMGnm7Sx0CERGRTWByYya1KrlIHQIREZFNkDS5OXz4MF544QXUqFEDMpkMwcHBJV4THh6Otm3bQi6Xo0GDBli/fr3J4zQGpeDDM4mIiMxB0uQmPT0drVq1wrJly/Qqf/PmTQQGBuLZZ59FVFQU3nvvPbzxxhvYt2+fiSMlIiKi8kLSx1X369cP/fr107v8ypUrUbduXSxcuBAA0KRJExw9ehSLFi1CQECAqcI0ioycPKlDICIisgmSJjeGOn78OHr16qV2LCAgAO+9957Oa7Kzs5Gdna16nZKSAgBQKBRQKBRGja/gftrum5OrPbkxdgxSKq7+tsLW28DW6w+wDVh/264/YLo2MOR+5Sq5iY+Ph7e3t9oxb29vpKSkIDMzEy4umpN258+fj6CgII3jISEhcHV1NUmcoaGhGseSkuwByDSO79692yQxSElb/W2NrbeBrdcfYBuw/rZdf8D4bZCRkaF32XKV3JTGjBkzMHXqVNXrlJQU1K5dG3369IGHh4dR30uhUCA0NBS9e/eGo6Oj2rmNdyNwMzVJ45r+/fsbNQYpFVd/W2HrbWDr9QfYBqy/bdcfMF0bFIy86KNcJTc+Pj5ISEhQO5aQkAAPDw+tvTYAIJfLIZfLNY47Ojqa7IOn7d52dpq9NgVlrY0p27a8sPU2sPX6A2wD1t+26w8Yvw0MuVe52ufG398fYWFhasdCQ0Ph7+8vUURERERkaSRNbtLS0hAVFYWoqCgA+Uu9o6KiEBsbCyB/SGn06NGq8hMmTMCNGzfw4Ycf4sqVK1i+fDl++eUX/O9//5MifCIiIrJAkiY3kZGRaNOmDdq0aQMAmDp1Ktq0aYNZs2YBAOLi4lSJDgDUrVsXf/31F0JDQ9GqVSssXLgQq1evtvhl4ADAPfyIiIjMQ9I5Nz169IAo5q++tt2He/TogbNnz5owKtNgbkNERGQe5WrOTXmmK4nLUnBzPyIiImNicmMmunpu/oi6a9Y4iIiIrB2TGzPRNfqWqxRQ5CmLHZ4jIiIi/TG5kVhaVi7azgvF2HWnpA6FiIjIKjC5kVjY5USkZuXi0NX7UodCRERkFZjcmEnTGsZ91AMRERFpx+TGTOxl2h+/EHHrkZkjISIism5MbsxE6LHTTezDDJyJfWyGaIiIiKwXkxsL0u3rg3hx+THcfJCuOpalyEPw2bt4mJYtYWRERETlB5MbC3Q57slj3b/aG433tkVh2A/HJYyIiIio/GByY+H2XIgDAFy/n15CSSIiIgKY3JiNIXv0aZ96TERERPpgcmOBuFcxERFR6TG5MZNeTbylDoGIiMgmMLkxkx6NquG3t/31Klt4CItDVERERIZhcmMmMpkM7XwrSx0GERGR1WNyY4FuPeTKKCIiotJicmOBvt4XrdrIT6bjsQ1ERESkHZMbCxVx86HUIRAREZVLTG4slCH74hAREdETTG4sFHMbIiKi0mFyQ0RERFaFyQ0RERFZFSY3FopzboiIiErHQeoASLuPd5yHp4uj1GEQERGVO0xuLNjELWekDoGIiKjc4bAUERERWRUmN2a25Y2OUodARERk1ZjcmJmLk73UIRAREVk1Jjdm1qyGp9QhEBERWTUmN2bm5MAmJyIiMiX+pSUiIiKrwuSGiIiIrAqTGyIiIrIqTG7KkasJqVKHQEREZPGY3JQjW07GGlT+zqMM9F18GNsj75goIiIiIsvD5KYckcmKP5+lyEN6dq7qddDOi7gSn4oPfv3HxJERERFZDiY35YgMurMbIQRazglBs9n7kKXIAwCkZ+eZKzQiIiKLweRGAm91q2f0e+YqBXLylACAfx9nGv3+RERE5QWTGwnM6N+kVNcVNyxVwogVERGRzWByYyVEMa/0NW/XJYzfGAkhSnc9ERGRJWByU45l5+bhj6i7eJCWrXa8tLnJmqM3EXopAef+TTZCdERERNJwkDoA0l/RoafF+2OwIvw6ACD6s75q5+48ysDxGw9L9T6K/+buEBERlUfsuSlHis652XchXvV9o0/3qr4/G5uEZ746aK6wiIiILAqTGyv04W/c14aIiGwXk5tyZMfZe6W+9uK9ZEzZehZ3HmUYMSIiIiLLYxHJzbJly+Dn5wdnZ2d07NgREREROsuuX78eMplM7cvZ2dmM0Uqn6MRhQwR+dxR/RN3DhJ9OGzEiIiIiyyN5crNt2zZMnToVs2fPxpkzZ9CqVSsEBAQgMTFR5zUeHh6Ii4tTfd2+fduMEZdvF++lIPC7I7hwV/eKKK4EJyKi8kzy5Obbb7/F+PHjMW7cODRt2hQrV66Eq6sr1q5dq/MamUwGHx8f1Ze3t7cZIzaOZxpWLfM90nNySy6kxcV7KXht/akyv39Rj9Jz8PlZeywPv2H0exMREelL0uQmJycHp0+fRq9evVTH7Ozs0KtXLxw/flzndWlpafD19UXt2rUxcOBAXLx40RzhWpyElNIPUyVlKowYSb4fj95CYpYMi8KuGf3eRERE+pJ0n5sHDx4gLy9Po+fF29sbV65c0XpNo0aNsHbtWrRs2RLJycn45ptv0LlzZ1y8eBG1atXSKJ+dnY3s7CdJQEpKCgBAoVBAoTDuH/iC++lz39LuAlxw7+zcsu9FoyvO3NzcUrVNRvaTa1LSs+DiZF/q2MorQz4D1sjW6w+wDVh/264/YLo2MOR+5W4TP39/f/j7+6ted+7cGU2aNMEPP/yAefPmaZSfP38+goKCNI6HhITA1dXVJDGGhoaWWCYzyQ6l6TjbvXs3AGDr9dJdX0CZl6e61xP5H4cTJ47j/iXD73nz9pOYft21D1VsY563Vvp8BqyZrdcfYBuw/rZdf8D4bZCRof9qX0mTm6pVq8Le3h4JCQlqxxMSEuDj46PXPRwdHdGmTRtcu6Z9KGTGjBmYOnWq6nVKSgpq166NPn36wMPDo/TBa6FQKBAaGorevXvD0dGx2LKhv/wDPIgvtow2/fv3BwBMmRlSqhgL2Nvbo3//ALVjU47n37Njp07o4FfZ4Hse3XEeSIwDAPR4tgdqVzJN8mjJDPkMWCNbrz/ANmD9bbv+gOnaoGDkRR+SJjdOTk5o164dwsLCMGjQIACAUqlEWFgYJk2apNc98vLycP78edUf/aLkcjnkcrnGcUdHR5N98PS6d3GP+C7h3sai614O9g6leh87uyfDUI4OpW/fxfuvwk3ugDeeqVeq6y2BKT9f5YGt1x9gG7D+tl1/wPhtYMi9JB+Wmjp1KsaMGYP27dujQ4cOWLx4MdLT0zFu3DgAwOjRo1GzZk3Mnz8fADB37lx06tQJDRo0QFJSEr7++mvcvn0bb7zxhpTVMJh1Lrcue6XuJmVi8f4YAMC4LnVhb1e6JJCIiGyX5MnN8OHDcf/+fcyaNQvx8fFo3bo19u7dq5pkHBsbCzu7J3NLHj9+jPHjxyM+Ph6VKlVCu3btcOzYMTRt2lSqKpSKspTZzei1EZj/YgsjR6Pu54hYdKxXBdcS03DgSgJG+/vB2dE8k4MzS7m8vby6mpCKG/fT0be5fsOwRERUMsmTGwCYNGmSzmGo8PBwtdeLFi3CokWLzBCVaZU2uTl89T6mm/jZUcFR97D45Tbo9e0hAEBKZi7eD2ikUe5aYhrCoxPxSiff/5KfJ70sxuiZ2nshHoEtq5f9Rhasz6LDAIDtE/zxdCnmORERkSbJN/GzVcoy/PFPLMP+NgUMefuzdx5rPd7r20P47K/LWHnoupb7lz27mbjlTJnvYQoX7iYj+Oxdo97zcpz+E+WIiKh4FtFzY4sK73Ozc1JXvLD0qN7XZufmmSIknWSQ4XF6DjxdHGGnZQ7M4v0x2HbqDjydbePj9Pz3+T8rH09ndKpXReJoiIioKNv4a2SBCvfctKjladC1tx4a78neH2w/h/N3k9GqVkWdZaLuJKHNvFD0aeqNVaPbA9DchDAuOQtxhR5XZZ0TptXFJKapJTfJmQq48l8UEZHk+KtYIqWdc2Ns20//CwC4Ep+qs0xadv4k35BLT/Yj+jT4QrH3lap2uXlKONibf7R157l7ePfns5jYox6eMvu7ExFRYZxzIxGpc5ucEh7fkJtX/PnNJ2ONGY5R3Lifhqaz9uGL3Ze1nr+WmIqtEbFQlmXCkw6f7DgPAFjGh4YSEUmOyY1ELKXnRpehP+h+cKk+SvPsLEWeEgeuJJb6Pb8NvYqcPCVWHdaeYPT69jCm/34ev575t9TvoYuslJsyEhGR8TG5kUj3p6oBANzl+SODS0e2kTIcDWdjk8p0fc9vD6mGs/T1/YFr+GK39gemlmTpgRjs+idOr7Ln7iSV6j00WHiCSkRkq5jcSGRsZz98P6IN9k/rDgB4vmUNiSMyLiGAX07dKbbM9ftpeGPDKUT9l2yUZXn1NyFXS30tERFZFyY3EnGwt8MLrWrA20O6R2ev//umSe9f0tDb+A2R2H85EYOW/Q1A++O25vx50ehxGW0EiUNRREQWicmNDZuz85Kk73/7UclL2tcfu2X09/3pRCwmbjlTqnlBRERk+ZjckNkkZyrUXpsyuRi07G/8VcwcnL/+iVNb/p6bp8RPJ27jWqLuJfHFscVOnJCL8Ri9NgKJqVlSh0JEpIbJDZnFZ7suoVVQCLb8t4R89ZEbahsZGjvRibqTVOLjG/IKBfBzRCw+Db6AXt8eNmoc1uzNTadx+Op9zJW4B5CIqCgmNxZk42sdpA6hRDNL2LxPl9VH8+f3fPzffjCf/aW+F83otREwd+dH4d6Wsq4OK6xwnpalyEO6llVjiSlZVjMs9ig9R+oQiIjUMLmxIO39KkkdQok2nbiN5eHXDLpGUWRDwKMxDzTKHNFyzNTO/5uMnyNiIYQo875DikKbIj4q9FzTpz/fj2az9yFL8eR5YGuO3kSHL8KwaH9Mmd6TiIi04+MXLIhdOZm48dXeaL3LLt5/Fd+Fqf8R/1zHDsIl7ZpsbNN/z+9F8vFw1nhcxIW7yXh782l81Lex7mX6OhKiwkdTs/J7bW7cT0fTGh4AgHm78odxiraLoYQQ3DyQiEgL9tyQyfx6+l8s3h+Dok87uByXorX8veTSTUwta1IUHHUX5+8mqx17Z/MZ3HmUiUlbzup+37z8iv37OAPpOcU/qT3scgKuxGuvd2lM3HIG/ZYc0egVM5Vj1x/grU2RSEjh5GEisnxMbiyItf0nvLiHcRqi8B/w5EwF5u+5jMtxKRBCYO+FeDz16Z4y3f+PqHu4cT9d7VjhYSRdCnpg3tp0Wu34pccyZCny1ObULAy9ir6Lj5QpzsIKVnuduvXIaPcszsgfT2LfxQTM+K+3i4jIkjG5sSAys0+pLR8afrIHnwafx92kTMzbdQk/HLqBfkuO4OMdFzDhp9Ml36CMdv1zr9jzl4r0RP12yx7Td1y0yqcz3EvKlDoEFSGESR6CSkTlH5MbC2LH3Eann07EosuCA/j19JOHXv4cYbonkxf+k1l4aErb87K0/dj+Oh9v0CTl5Qevqz1+IjdPiaMxDwx+PhcAKJUCO87+ixv301THsnPzEF9o2O/AlQQs2HNFbTm8IY5dN/8E8KLGbzyNnt8eQnZuyb1sRGRbmNxYEE4ONb7nvz+CC3eT9RpmKo5SKfDi8r/RfPY+zXM68gND0ob4lCy8ty1K9fqHwzfwypqTeHXNSY2ySRnFL73+89w9/G/bOTy38JDqWJ9Fh9FpfhhiEvKHCl9bH4mVh65j5zn1XqnTtx/hva1nS9yYb+SPT+KSqodq/+UE3HyQjlM3H0sTABFZLK6WsiBMbYzvwt0UvLD0qEHXKJUC91Oz1Y6FXIrHGQP3winLH/1fIvMfOlp4/53cPCUOXb2Pz//SvtqswOnbmn/sbz/Mf9TF3gvxaOjtrjoeX2SC8JAVxwEAf5y7h/D3e8C3SoVSxW+ojJxcrD16EwHNfNTiIyIqDfbcWBB23JiGEIYlGgWJRWE3HqRrKVn8Sq3oMkyo1vZR+PHITby+IVJnLAWEAX1Guj5yQgDdvw7XOJ6hZVWYoZ/b3efjMPnns8gsdK+FIVfxTchV9F5k+A7RhtSXiGwDe24sCIelLIO2SbO69vbZUMyDPQ3tMSqs6GdBCKE16Sqrwm+jz5L6WD0edlqSdzbnPxajgZcbJvdsCAA4G2udQ0tKpYAdJ9ORkSmVAg/Tc1DNXS51KBaLPTcWyoG/ECWTZ0A3T0wpH7Spy51HGchS5Gn0qIzfeBo3S+ixKVBc+EVPFazQm/7bP1qX1MfrsffQsesPSzWn6WFadsmF9FBcfe8lZeKNDafw9zXzToC++FiGhjND0GjmHlwosoeSNVDkKcs8j41K781NkXj68/0WMbHfUjG5sVBb3+yEv6c/h3kDmyFsWnepw7Epyw5e17ussSfTPvPVQfT69pDGeNH+ywk6r/l63xUs3n9V9brwKi1FnhLXEp+smioab0HPzdZT2nuFpv/+j15xrwjXv820KUszFnftJzvOY//lRIxarTkx25RWXbEHACjyhCR7A8U+zMDotRE4ZqKkruuXB9C8yGNFyHz2X04EAKz975l9pInJjYUSAGpWdMGr/n6oU9lV6nBIh2PXHxr9nv8+ztR7cvnDtBwsO3gdi/fHICMnF0II/BxxR+28tgd36uvuY/32tYmOTy3TkmxDksR7SZn4NvRJMlewWaK2P7TxKcbpHSrOjftpSM5QmPx9DPHetrM4fPU+RpooqUtIyUauUuB6oe0GiCwJkxsLVfiXvT3n4lisuyba1E7f+VeF58koBXBUy//UC9+q6DCaseZ57b0Yj0af7sXmiLLPC+q/5AhSs/KThYycXMz644Ja9/sra05qPJcr5GI8Gs/ci9VHbqgdN/Xw7s0H6Xhu4SG0mhuis4wUE54TzJDUAdJtAyCF0EsJFjfEaEvtbygmNxaq8Nb9nJBoe0rzI9957h5eXROhdqzoH9Zd/8QhOfNJL4OxP1lzdupepl50N+G7SZm49SAdSRk5alFeikvBTyfyN2hcdvAaNh6/rbavTtFHZQgAU385BwD47K/LmLDpNMati4AQAg72pavhpXsp6PbVQfwRdbfYcidvPOm5OxidaPD7cFinfLh4LxnjN0bi+e9Lv0jAFCwxtwm7nIB5f12BmR57pxOTGwtliR9aMp/Cj+IQxfz3rPBzt3TN7RizVj3hWVPGcfrYhyWvmLqfmo1Vh6/jUXr+hoN7zseh1dwQhBdKAPZfTkSPb8LRem6oxvUF9QqPvq9XTIVTmL0X43Ew+j7ikrPgaFfyr7i07FyNJGPy1rOIfZSBKVujIITAsoPXcCSm+FjGrcufuJxb5Lf6hbvaH5j65d4raDxzLyLN9HwwKr3r9/WbzE/A6xsisfFELE7cl/Y/5UxuLBS7G63T8VLM0SmYPKjN4v0xOs8VeFxkPkjhoSyZLP+p5rpo+xguCSv5Pd/86Sy+2H0FE/9b9v325jNIzcrF2HWnSry2wL+PM3DxXslPUj9545HWR12EXIxHhI7E4UFaNmb8fh4nbjxE89n70OHz/WrnCyc7oZcS8PW+aI1eMUBzj59Pgy+gyZz9GuW0PQOrYBL2F7uL35TR0hROtg15xEhZRMenYuy6CJy7k1Sm+0TcfIRJW84g0cCn25vrz7QQAkdjHui1SrGgvDHKmEJSNpMbIpsx4scTepUr/Ecz6o7uPWCK7jBclLbEaOWhJyubZAC6fnlQr5j0dTVZhgv/JSXHb+iZzBX5BSwEcO6OfvMbVh66jnQtmwvO2XlJ5zVTfzmHnyNi8fKq/J9HSlYuXvj+KA5fvY/VR26ohXOnyKTqnFwlJmw6jY3Hb2n8J0TXcv3u3xzElK1PnlEWcjG+pGohPDoRl+NKTu5KkpGTi3c2ny5xiE1fxf2tTM/OxQtLj2FnrHH/tLyy5iTCo+9j4LK/SyyrKGY8ZNgPx7HrnzhMt9Cn24dH38cra06i0/wwvcqXlLasCL8O//kHcMcI+1OVN0xuLMwLrWqgSXUPtPerpHZ88fDWmN6vsURRkbldKbS7sSFL04uaGXyh2PMlTSh+nJ6j8VT0kibILrtkr19wavfUVDS0l1Yc0/poCX0V/gV/+KrmENP5u8kYvTYCn/11udiJ4sFn72LvxXjM+uOiAe+diT+inrTjm5s0n2afnKlAyMV45OQqcTUhFWPXnUK/JUf0fg9d1hy5id3n4zFla5Te1xT3v32hVk793PbIO7iSkIb9d0v/p0Xbexd9HIouf/0Th4af7MGOs/8WW+72Q8OGmYp+FnefjzNK4lmUtv2YjsTc12voMi45U23bByB/6DM+JQtf7dO+Cak1Y3JjYb4f0Qa7J3eFo736j2ZQm5qY0L2+RFGRtSppQuvD9By1p6IDMHhCmD7DLrr23yks8vZjDFlxzLA3L+SZrw7ikJakpiThRSYK3zLwD2NJCoYgXl1zEm9uOo1F+6/iepE/Ukdi7uOZrw6UatO2RyU8aLWoj3ecR6ugEL3+t1/0o5BbyqfMA/lJzdh1ERi+6kSph1ImbskfBv3ftnPFlrt+P121Ii9LkYc8A+KOuPkI72w+Y5TEsyQP0rLx6poIvLTyuNbzhZvJf/4B9Pr2EB5o2RxTqqEpKTG5sUD6LM+d9XxTM0RC1m7+nisGX/P7WcOGN1YdvlFimfNaltiaYsR+84nbBl9zJOZJQnHnUQaWF9qw0NDEAQDOFHnUxL3kLHwfFoN//s1vgx1n7mqUf3VNBO48ysTIH0+qrXbTh8yAlrxwNxlbTsYiJSsXAYsP44dD15GRo75PUuE/lEX/aJb0uys7Nw+nbj3SuidSpiIP4dH3EXHzkUm2WLhWZBuEpQevISVLgcYz92JACY9KKdyGV+KN02OTpcjTaL+iKUjhREVbgqItZbmaoH3X9CxFHv75N6nYROdxeg5G/ngCv58pvuercEyWmjgxuSlnIj7pid/e9sdrXeviyry+UodDZBLHrz/Eur9vGf2+IZcSyvTYhw9+Ve8R0PXMseK8uFyz92lhoU0J07Nz8U1ItM7yrYJCtE5Q1iUtW/9kaGehIciMnDzM33MFC0OuqpUp7p2LS23+vvYAjT7di6Erj6uW7ptTr2/VH8qamJKNY9fy54SVNHHd2FuNPUzLRuOZe9W2ONBm17k41fd5SqHXz13XM+LGb4zEgKV/F/s8vCVhMTh2/aHeP583NkQi8LujGisELQGTm3LGy90Z7XwrAwCcHQ2f20BUHhy/8VDnSqeyClhs+JPHC5y4UbaY/Kb/VWKZ1OzcEpceK5RKCCEwfmMkJv98FveSMnX+UfslUvN/4RfvJeNGkd2Fdf0PPLLIPKfCxUSR64rbn+ndn58Mb/71T5zugiagbYhth549kEKIYlc57r0Qh+Xh1zSOp2bl6twdfO9/E8pLmnC/9OCT+45bfwq9vj2k9nPWtzdHJpOpeiA3Htfde5lkYE9k2JVEXIpLUS0gsCRMbojIpjxIM3woydLk5CoR+ygDoZcS8Oe5e+i84ABazNmn17VXE1IR+N1RPLfwkNr9ei86jB8OaQ4hFtdpkZiShac/D8OC/4Y3ixuW0vaHOCVLgXtJmbhxP82k2188tzBc63F9hr/+PHcPm4oZzpzw0xl8tTcap28/wsO0bAz54QR+uWGHtp8fQLPZ+wyaz1NcWx+JeYAbD9LVhjVv3E/HphO31VeIlfB2Nx6kY9gPx0ucv1V0IQEAHLv+AAOXHsWFu8lq8/UscZtZJjfl3KeBTQAAVd2cJI6EiMylxZwQjbk32Tp6bgqLvPUI/QtNhM3NU0KpFDh2/YHGShtdCq+WWxF+HQ/SsrHy0HVk5uQhaOeTFWRKZf58DF1zhJYdvIaWc0LQecEBPLfwkGrDR1NQ5Gn/i18wqbiwS/dSsPrIDdVQS8hF9YfW6krC7jzKxMc7zuOff1Pwd8KTP63ans+m6x7FPSBXm7tJmZgZfEG93f+7eeFksmjyEXHzUYlDYhoLCQCM/PEkzv2bjDFrIzT2hrI0DlIHQGXzxjP18HzLGjhwJREf79Dcu+GNrnXRp5kPhv2gfbY9EZVPA5bq3vMlU8cquKKrbhp8sqfE9yncGaPIUyK3UKJQuFNixaHraq/HbTiNah7O+CPqHn5/p7NGh8LXRZYnx2oZOgq7nABXp+L/TCmVAnZ2Mpz/V31S+skbD1Gvmhu2n9b9vDNtk637f5ef/Dna22FMZz+dbVnUe9uitB7v9vVBnJjREz6ezgCA1Udu4NfT2ifs3tJj9+8v92ouAih4XAkA/HbmX1SQO8Cu0A/uz3OavTBl8dCEiaixMLmxAj6ezhjYugZ+PHJDYxOxT/9bVfXz+E5wd3Yo9tkoV+b1ReOZe00aKxGZzs5z99DIx92ovSBnY5OwMCQa3x+4BpkMqFLhSS9x4V6c2CJL5I8Vmp8058+LJa7y+rTQnkxC5A95vb4hUqNcYkoWqrrJkaHIwwvfH8XNB+loWctTtdqswPBVJW+YuWj/VZ3nCh6SmWDgbsbajFkbgQVDWqCqmxyf/VW2HanPxiYVe373+XjsPh+PTvUql3ivPefjEJ+ShXFd6pYpJkvE5MZKVJA74MC07kjPyUPz2flj7+8+10B13r9+FY1rHO1leLFNLWyLzP+fDScoE5Vv7/58Fk95uxn9vt8fyJ/YKoT6nKXCz82yK2a+TdHEQ5ui/zHTNTeqwxdhaFOnIhJTslVzZvS5v6EE8lcoGWMuUHRCKgZrWSUH5G8o2e2pamV/kyL0mfz+9n+PR/GvXwWP0nNwNUF9aPLX0/9i9ZEb+HF0+2L/Pryy+iTqe7mhmru8bEEbEZMbKyKTySB3eDLW+2LbWjrLftK/CcZ18cP+y4mq5IaIyr+if6DMJUvL3jWmUlLvhTEIAQR+d0Rtt3BTGL02AsdnPIfqni5F3t98+8f8cOiG1tVj72/PXxL+/PdHi+15S83ORVQZn/tlbJxQbGUK/9/JXsv/pFrU9AQA9GvhAwd7zR9/YIvqpgqNiKzY7vMlPy9LX0IAb/2kOSRlSrl5SvxWaC5MnlJp8sSmQEJKtupBqk/e33zJTUnL4g3dONISsOfGyjjY22G0vy9Ss3JRu7KLxvngiV2Qlp0LTxdHAIB/vfzhqvrVKgAAlo1qi1p7LmssCX29a12sOXpT9VruYKfX6gwiIkN1/+agSZeGazN6bYTanj7BUdon4eo7wdgQ5+4kaUwUjtPzyeCWKlP7Fj9mw54bKzR3YHMsGt5a654T9nYyVWIDAJ6ujrg0NwD73uv2pEyR6+pVq4BJzz6Zv9OqlifmDWyu8/0rV3DUeQ4AWtWuqPX4+GfqYnj72sVeS0TWT4od/Y9df6hzI8TCFpTikSUlmf2n5kNYn/nqoNHfx5yOJEibXjC5Ibg6OagNUQ0tlGCM7FgHB6b1QKVCKySWvNwGL7Wrhe0T/HFs+nMa91vzajscfL+HzvcLfqez1uMfBDSGs+OTOLZP8DekGiUa2bGOUe9HRLZt6YEYqUMgHTgsRRrqVq2Ac7P7wF3uALtC+6mf/LgnHqblwK9q/hDW0375Sw2Ht6+NbZF34O0hh5vIQtPq7pDLnXBrQaDW7eZlMhlWvdoOb246XeQ4YG/3JLkpuH9RvZp4G7zZVavaFfHF4BZ497kGiE/OQps6lfTaCp+ISJdvQnQvJSdpWUTPzbJly+Dn5wdnZ2d07NgRERERxZbfvn07GjduDGdnZ7Ro0QK7d+82U6S2w9PFUS2xAQBvD2c0reGhUfbLl1ri1oJAHP2gOyY3z1O7bmrvpzCkbS1seaMjqrnL8ePo9gCA3k29MbhNTVW5etUqwMFOBm8P9aWEvZt6a7zfG8/UxYgO+b1Li4e31lmHzW90VH1fsDdHdU8XtKlTSa3crOebYuUrbdWG64iIqPySvOdm27ZtmDp1KlauXImOHTti8eLFCAgIQHR0NLy8vDTKHzt2DCNGjMD8+fPx/PPPY8uWLRg0aBDOnDmD5s11zwMhaUzu2VD1fcTHPVXzgGQyGb56qSVqV3ZF5/pV8LRfZchkMozp7Icr8ano2ST/Z7/q1XZIycrFtF+iEJ+ShZEdfNGpXhV0rFsZ0/o0Ulv6XsPTGY4Odrj93y6fzWp4YPXo9lj7903MG6T7s/FCqxqo5i5HQDMf3HyQjtFrIzDa3xeNfDyQlJGDKVujAGhOqgaAD/s2Qq8m3uiz6MnDGL1dBBIyLfFpK0REtkEmzLmYXouOHTvi6aefxtKlSwEASqUStWvXxrvvvovp06drlB8+fDjS09Oxa9cu1bFOnTqhdevWWLlyZYnvl5KSAk9PTyQnJ8PDQ7MXoiwUCgV2796N/v37w9HR9noBpKh/bp5StYX8lXl9IXeww5KwGChFfq9RcS7dS0F6Tq7O4a8C207Fon41N7T3q4wsRR7uJWViyIpjeJyhwO7Jz6BpDQ/8HBGLGb/nP/5iiX8u+vfvj3XHY/HFbuNPPiQiKg9i5vUx6t8CQ/5+S9pzk5OTg9OnT2PGjBmqY3Z2dujVqxeOH9f+LKTjx49j6tSpascCAgIQHBystXx2djays7NVr1NS8nfUVCgUUCiMu3a/4H7Gvm95IVX9I2b0gBCAPZTIzVViYve6esXRsJqLXuVebF1dVc4eQO2KchyY+gwepGXDr4oLFAoFBrX0RtzjDLSv44HHV09BoVBgTMfaSEjOxJq/858ovP9/XdFr0VFUcnVEliIPjvZ2SMnKxe8TOqKJjztkMhnsCw3pnb+bjGuJ6fjw9wvw9pCjbe2KCIu+j+db+OBMbBLs7WSo4GSPf+6mYMeETqhXzRWj1kTiwr0nu8ZuGtcea4/dwsHoB2hd2xPrx7TD/L1XsS1S+7NthrStgYGtqmP0uvz5UBvHtcPMPy7jtpbn/gBAm9qeaOTjjq2n8u+39fV2eHnNaY1ybz7jh1VHbuls4wpye9jJZFgwuBkm/nxOa5mvhzTHB79d0DjepX4V/H39oc57F2boFgaNvd1wRaJN8YjKO1P9jdWHpD039+7dQ82aNXHs2DH4+z9ZGfPhhx/i0KFDOHlS86mlTk5O2LBhA0aMGKE6tnz5cgQFBSEhQXOS6Zw5cxAUFKRxfMuWLXB1dTVSTYh0S8zMf8CgT6GPm1IAdnqOXN1NB6o4A86Fdj8XIn97eG33yM4DHOwARR7g7ADkCSAmWQY/NwHn//47k6sE0hSAuxNgL8u/n67d8wvKejjlbxKZqgAiH8jg6QS0qSJgV+T6lBzA0Q5IUQBV5PmxFEjOAQ7H26GzlxJV8p8jiHQFUHj3gMxcICkn/32/OZ8fcK8aSrzgm5+U5PxXP+V/beBol//+2UrgarIM9d0F7mbIcCRehiF+SjjZA4+ygTwl4OueX/ZOOnA/Swa5HdC0kkBWHhB8yw6dvZWo7grsvmMHuX3++95IlaGik4BCCdRwzW9zmSy/Lv+my+Bklx9vfQ+ByvInP5+LSTIo8oCnPAXWx9ihvrvAw2wZ2lYR8HASuPhYhuOJdniUnd9wg3zz8DhHhuQcIOrhk0bzdRPoU1OJhp4CM07ZI0/IUNNVoI6bQHpufjtdS5EhRylDx2pK5Akg8sGT6zt7K3Hsv2W5TSsqEZsuQ2Zufj06eQkciX9S1tleICvvyQehkacS0cn5599olIej8TJcSbZDBQcBuT1UsRtbNx8ljibIoBQc3i2valUQ+KClcfcEysjIwMiRIy2/58YcZsyYodbTk5KSgtq1a6NPnz4mGZYKDQ1F7969bXZYypbrD9hOG7ys47g+9R+h9ah2bw0zODSV9wws/1Kh7weX/m1VbfC/Yb1UbTC0DPcrbPALRrpRKX2kRxlb+Tegi63XHzBdGxSMvOhD0uSmatWqsLe31+hxSUhIgI+Pj9ZrfHx8DCovl8shl2s+zMvR0dFkHzxT3rs8sPX6A2wDW68/wDZg/W27/oDx28CQe0m6FNzJyQnt2rVDWFiY6phSqURYWJjaMFVh/v7+auUBIDQ0VGd5IiIisi2SD0tNnToVY8aMQfv27dGhQwcsXrwY6enpGDduHABg9OjRqFmzJubPnw8AmDJlCrp3746FCxciMDAQW7duRWRkJFatWiVlNYiIiMhCSJ7cDB8+HPfv38esWbMQHx+P1q1bY+/evfD2zt+8LTY2FnaFdq3t3LkztmzZgk8//RQff/wxGjZsiODgYO5xQ0RERAAsILkBgEmTJmHSpElaz4WHh2scGzp0KIYONdYUPSIiIrImFvH4BSIiIiJjYXJDREREVoXJDREREVkVJjdERERkVZjcEBERkVVhckNERERWhckNERERWRUmN0RERGRVmNwQERGRVbGIHYrNSQgBwLBHp+tLoVAgIyMDKSkpNvk0WFuvP8A2sPX6A2wD1t+26w+Yrg0K/m4X/B0vjs0lN6mpqQCA2rVrSxwJERERGSo1NRWenp7FlpEJfVIgK6JUKnHv3j24u7tDJpMZ9d4pKSmoXbs27ty5Aw8PD6Peuzyw9foDbANbrz/ANmD9bbv+gOnaQAiB1NRU1KhRQ+2B2trYXM+NnZ0datWqZdL38PDwsNkPNcD6A2wDW68/wDZg/W27/oBp2qCkHpsCnFBMREREVoXJDREREVkVJjdGJJfLMXv2bMjlcqlDkYSt1x9gG9h6/QG2Aetv2/UHLKMNbG5CMREREVk39twQERGRVWFyQ0RERFaFyQ0RERFZFSY3REREZFWY3BjJsmXL4OfnB2dnZ3Ts2BERERFSh1Qqhw8fxgsvvIAaNWpAJpMhODhY7bwQArNmzUL16tXh4uKCXr16ISYmRq3Mo0ePMGrUKHh4eKBixYp4/fXXkZaWplbmn3/+wTPPPANnZ2fUrl0bX331lamrppf58+fj6aefhru7O7y8vDBo0CBER0erlcnKysLEiRNRpUoVuLm5YciQIUhISFArExsbi8DAQLi6usLLywsffPABcnNz1cqEh4ejbdu2kMvlaNCgAdavX2/q6ullxYoVaNmypWoDLn9/f+zZs0d13trrX9SCBQsgk8nw3nvvqY5ZexvMmTMHMplM7atx48aq89ZefwC4e/cuXnnlFVSpUgUuLi5o0aIFIiMjVeet+Xehn5+fxs9fJpNh4sSJAMrJz19QmW3dulU4OTmJtWvXiosXL4rx48eLihUrioSEBKlDM9ju3bvFJ598In7//XcBQOzYsUPt/IIFC4Snp6cIDg4W586dEwMGDBB169YVmZmZqjJ9+/YVrVq1EidOnBBHjhwRDRo0ECNGjFCdT05OFt7e3mLUqFHiwoUL4ueffxYuLi7ihx9+MFc1dQoICBDr1q0TFy5cEFFRUaJ///6iTp06Ii0tTVVmwoQJonbt2iIsLExERkaKTp06ic6dO6vO5+bmiubNm4tevXqJs2fPit27d4uqVauKGTNmqMrcuHFDuLq6iqlTp4pLly6J77//Xtjb24u9e/eatb7a/Pnnn+Kvv/4SV69eFdHR0eLjjz8Wjo6O4sKFC0II669/YREREcLPz0+0bNlSTJkyRXXc2ttg9uzZolmzZiIuLk71df/+fdV5a6//o0ePhK+vrxg7dqw4efKkuHHjhti3b5+4du2aqow1/y5MTExU+9mHhoYKAOLgwYNCiPLx82dyYwQdOnQQEydOVL3Oy8sTNWrUEPPnz5cwqrIrmtwolUrh4+Mjvv76a9WxpKQkIZfLxc8//yyEEOLSpUsCgDh16pSqzJ49e4RMJhN3794VQgixfPlyUalSJZGdna0q89FHH4lGjRqZuEaGS0xMFADEoUOHhBD59XV0dBTbt29Xlbl8+bIAII4fPy6EyE8Q7ezsRHx8vKrMihUrhIeHh6rOH374oWjWrJnaew0fPlwEBASYukqlUqlSJbF69Wqbqn9qaqpo2LChCA0NFd27d1clN7bQBrNnzxatWrXSes4W6v/RRx+Jrl276jxva78Lp0yZIurXry+USmW5+flzWKqMcnJycPr0afTq1Ut1zM7ODr169cLx48cljMz4bt68ifj4eLW6enp6omPHjqq6Hj9+HBUrVkT79u1VZXr16gU7OzucPHlSVaZbt25wcnJSlQkICEB0dDQeP35sptroJzk5GQBQuXJlAMDp06ehUCjU2qBx48aoU6eOWhu0aNEC3t7eqjIBAQFISUnBxYsXVWUK36OgjKV9ZvLy8rB161akp6fD39/fpuo/ceJEBAYGasRpK20QExODGjVqoF69ehg1ahRiY2MB2Eb9//zzT7Rv3x5Dhw6Fl5cX2rRpgx9//FF13pZ+F+bk5OCnn37Ca6+9BplMVm5+/kxuyujBgwfIy8tT+yECgLe3N+Lj4yWKyjQK6lNcXePj4+Hl5aV23sHBAZUrV1Yro+0ehd/DEiiVSrz33nvo0qULmjdvDiA/PicnJ1SsWFGtbNE2KKl+usqkpKQgMzPTFNUxyPnz5+Hm5ga5XI4JEyZgx44daNq0qc3Uf+vWrThz5gzmz5+vcc4W2qBjx45Yv3499u7dixUrVuDmzZt45plnkJqaahP1v3HjBlasWIGGDRti3759ePvttzF58mRs2LABgG39LgwODkZSUhLGjh0LoPx8/m3uqeBE+po4cSIuXLiAo0ePSh2K2TVq1AhRUVFITk7Gr7/+ijFjxuDQoUNSh2UWd+7cwZQpUxAaGgpnZ2epw5FEv379VN+3bNkSHTt2hK+vL3755Re4uLhIGJl5KJVKtG/fHl988QUAoE2bNrhw4QJWrlyJMWPGSBydea1Zswb9+vVDjRo1pA7FIOy5KaOqVavC3t5eY6Z4QkICfHx8JIrKNArqU1xdfXx8kJiYqHY+NzcXjx49Uiuj7R6F30NqkyZNwq5du3Dw4EHUqlVLddzHxwc5OTlISkpSK1+0DUqqn64yHh4eFvHHw8nJCQ0aNEC7du0wf/58tGrVCkuWLLGJ+p8+fRqJiYlo27YtHBwc4ODggEOHDuG7776Dg4MDvL29rb4NiqpYsSKeeuopXLt2zSY+A9WrV0fTpk3VjjVp0kQ1NGcrvwtv376N/fv344033lAdKy8/fyY3ZeTk5IR27dohLCxMdUypVCIsLAz+/v4SRmZ8devWhY+Pj1pdU1JScPLkSVVd/f39kZSUhNOnT6vKHDhwAEqlEh07dlSVOXz4MBQKhapMaGgoGjVqhEqVKpmpNtoJITBp0iTs2LEDBw4cQN26ddXOt2vXDo6OjmptEB0djdjYWLU2OH/+vNovttDQUHh4eKh+Yfr7+6vdo6CMpX5mlEolsrOzbaL+PXv2xPnz5xEVFaX6at++PUaNGqX63trboKi0tDRcv34d1atXt4nPQJcuXTS2gLh69Sp8fX0B2MbvQgBYt24dvLy8EBgYqDpWbn7+RpmWbOO2bt0q5HK5WL9+vbh06ZJ48803RcWKFdVmipcXqamp4uzZs+Ls2bMCgPj222/F2bNnxe3bt4UQ+csfK1asKP744w/xzz//iIEDB2pd/timTRtx8uRJcfToUdGwYUO15Y9JSUnC29tbvPrqq+LChQti69atwtXVVfLlj0II8fbbbwtPT08RHh6uthQyIyNDVWbChAmiTp064sCBAyIyMlL4+/sLf39/1fmCZZB9+vQRUVFRYu/evaJatWpal0F+8MEH4vLly2LZsmUWswx2+vTp4tChQ+LmzZvin3/+EdOnTxcymUyEhIQIIay//toUXi0lhPW3wbRp00R4eLi4efOm+Pvvv0WvXr1E1apVRWJiohDC+usfEREhHBwcxOeffy5iYmLE5s2bhaurq/jpp59UZaz9d2FeXp6oU6eO+OijjzTOlYefP5MbI/n+++9FnTp1hJOTk+jQoYM4ceKE1CGVysGDBwUAja8xY8YIIfKXQM6cOVN4e3sLuVwuevbsKaKjo9Xu8fDhQzFixAjh5uYmPDw8xLhx40RqaqpamXPnzomuXbsKuVwuatasKRYsWGCuKhZLW90BiHXr1qnKZGZminfeeUdUqlRJuLq6isGDB4u4uDi1+9y6dUv069dPuLi4iKpVq4pp06YJhUKhVubgwYOidevWwsnJSdSrV0/tPaT02muvCV9fX+Hk5CSqVasmevbsqUpshLD++mtTNLmx9jYYPny4qF69unBychI1a9YUw4cPV9vjxdrrL4QQO3fuFM2bNxdyuVw0btxYrFq1Su28tf8u3LdvnwCgUSchysfPXyaEEMbpAyIiIiKSHufcEBERkVVhckNERERWhckNERERWRUmN0RERGRVmNwQERGRVWFyQ0RERFaFyQ0RERFZFSY3REREZFWY3BCRxbh//z7efvtt1KlTB3K5HD4+PggICMDff/8NAJDJZAgODpY2SCKyeA5SB0BEVGDIkCHIycnBhg0bUK9ePSQkJCAsLAwPHz6UOjQiKkfYc0NEFiEpKQlHjhzBl19+iWeffRa+vr7o0KEDZsyYgQEDBsDPzw8AMHjwYMhkMtVrAPjjjz/Qtm1bODs7o169eggKCkJubq7qvEwmw4oVK9CvXz+4uLigXr16+PXXX1Xnc3JyMGnSJFSvXh3Ozs7w9fXF/PnzzVV1IjIyJjdEZBHc3Nzg5uaG4OBgZGdna5w/deoUAGDdunWIi4tTvT5y5AhGjx6NKVOm4NKlS/jhhx+wfv16fP7552rXz5w5E0OGDMG5c+cwatQovPzyy7h8+TIA4LvvvsOff/6JX375BdHR0di8ebNa8kRE5QsfnElEFuO3337D+PHjkZmZibZt26J79+54+eWX0bJlSwD5PTA7duzAoEGDVNf06tULPXv2xIwZM1THfvrpJ3z44Ye4d++e6roJEyZgxYoVqjKdOnVC27ZtsXz5ckyePBkXL17E/v37IZPJzFNZIjIZ9twQkcUYMmQI7t27hz///BN9+/ZFeHg42rZti/Xr1+u85ty5c5g7d66q58fNzQ3jx49HXFwcMjIyVOX8/f3VrvP391f13IwdOxZRUVFo1KgRJk+ejJCQEJPUj4jMg8kNEVkUZ2dn9O7dGzNnzsSxY8cwduxYzJ49W2f5tLQ0BAUFISoqSvV1/vx5xMTEwNnZWa/3bNu2LW7evIl58+YhMzMTw4YNw0svvWSsKhGRmTG5ISKL1rRpU6SnpwMAHB0dkZeXp3a+bdu2iI6ORoMGDTS+7Oye/Io7ceKE2nUnTpxAkyZNVK89PDwwfPhw/Pjjj9i2bRt+++03PHr0yIQ1IyJT4VJwIrIIDx8+xNChQ/Haa6+hZcuWcHd3R2RkJL766isMHDgQAODn54ewsDB06dIFcrkclSpVwqxZs/D888+jTp06eOmll2BnZ4dz587hwoUL+Oyzz1T33759O9q3b4+uXbti8+bNiIiIwJo1awAA3377LapXr442bdrAzs4O27dvh4+PDypWrChFUxBRWQkiIguQlZUlpk+fLtq2bSs8PT2Fq6uraNSokfj0009FRkaGEEKIP//8UzRo0EA4ODgIX19f1bV79+4VnTt3Fi4uLsLDw0N06NBBrFq1SnUegFi2bJno3bu3kMvlws/PT2zbtk11ftWqVaJ169aiQoUKwsPDQ/Ts2VOcOXPGbHUnIuPiaikisnraVlkRkfXinBsiIiKyKkxuiIiIyKpwQjERWT2OvhPZFvbcEBERkVVhckNERERWhckNERERWRUmN0RERGRVmNwQERGRVWFyQ0RERFaFyQ0RERFZFSY3REREZFWY3BAREZFV+T9ldmYZJrw3nQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses[\"train\"])\n",
    "plt.title(\"Training Loss over Number of Steps\")\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Train Loss\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seePredictions(model:nn.Model, train_states:list, xs:Array, ys:Array):\n",
    "    trainable_variables, non_trainable_variables = train_states\n",
    "    for x, y in zip(xs, ys): # (B, H, W, C)\n",
    "        logits, non_trainable_variables = model.stateless_call(\n",
    "            trainable_variables, non_trainable_variables, \n",
    "            x[None] # (1, H, W, C=1)\n",
    "        ) # (1, num_classes)\n",
    "        class_pred = logits.argmax(-1).squeeze()\n",
    "        print(\"True Class:\", y.tolist())\n",
    "        print(\"Predicted Class:\", class_pred.tolist())\n",
    "        plt.imshow(x, cmap=\"gray\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vvy/.local/lib/python3.11/site-packages/keras/src/backend/jax/numpy.py:845: UserWarning: Explicitly requested dtype float64 requested in eye is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
      "  return jnp.eye(N, M=M, k=k, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 7\n",
      "Predicted Class: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 2\n",
      "Predicted Class: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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VKSsrKzITAwD6hbCugAoKCrR7924dOnRIcXFxwdd1vF6vhg8fLq/Xq+eff15btmxRYmKi4uPj9cILLygrK4t3wAEAQoQVoJ07d0qSFixYEHL7rl27tGbNGknSr371Kw0aNEgrVqxQa2urcnNz9Zvf/CYiwwIA+o8Y55yzHuJOgUBAXq/Xegz0cb/4xS+6tW/z5s1h7zlz5kzYe+bMmRP2nvb29rD3AJb8fr/i4+O7vJ/PggMAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJbv2LqEBPGjFiRNh7nn766ShM0rn3338/7D18sjXAFRAAwAgBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIPI0Wv19bWFvaea9eudeux/vznP4e959e//nW3HgsY6LgCAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMxDjnnPUQdwoEAvJ6vdZjAAAekN/vV3x8fJf3cwUEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATIQVoKKiIs2ePVtxcXFKTk7W0qVLVVVVFXLMggULFBMTE7LWr18f0aEBAH1fWAEqLy9XQUGBTpw4oaNHj6qtrU2LFi1Sc3NzyHFr167V5cuXg2v79u0RHRoA0PcNCefgkpKSkK+Li4uVnJysyspKZWdnB28fMWKEfD5fZCYEAPRLD/QakN/vlyQlJiaG3P7uu+8qKSlJ06dPV2FhoW7evNnl92htbVUgEAhZAIABwHVTe3u7++Y3v+nmzp0bcvtvf/tbV1JS4s6dO+f++Mc/uocfftgtW7asy++zbds2J4nFYrFY/Wz5/f57dqTbAVq/fr0bP368q6+vv+dxpaWlTpKrrq7u9P6Wlhbn9/uDq76+3vyksVgsFuvB1/0CFNZrQJ/buHGjjhw5ouPHj2vMmDH3PDYzM1OSVF1drUmTJt11v8fjkcfj6c4YAIA+LKwAOef0wgsv6MCBAyorK1N6evp995w9e1aSlJqa2q0BAQD9U1gBKigo0O7du3Xo0CHFxcWpoaFBkuT1ejV8+HDV1NRo9+7devrppzVq1CidO3dOmzdvVnZ2tmbOnBmV/wAAQB8Vzus+6uL3fLt27XLOOVdXV+eys7NdYmKi83g8bvLkye7ll1++7+8B7+T3+81/b8lisVisB1/3+9kf8/9h6TUCgYC8Xq/1GACAB+T3+xUfH9/l/XwWHADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADARK8LkHPOegQAQATc7+d5rwvQ9evXrUcAAETA/X6ex7hedsnR0dGhS5cuKS4uTjExMSH3BQIBjR07VvX19YqPjzea0B7n4TbOw22ch9s4D7f1hvPgnNP169eVlpamQYO6vs4Z0oMzfSmDBg3SmDFj7nlMfHz8gH6CfY7zcBvn4TbOw22ch9usz4PX673vMb3uV3AAgIGBAAEATPSpAHk8Hm3btk0ej8d6FFOch9s4D7dxHm7jPNzWl85Dr3sTAgBgYOhTV0AAgP6DAAEATBAgAIAJAgQAMNFnArRjxw5NmDBBw4YNU2Zmpk6dOmU9Uo977bXXFBMTE7KmTZtmPVbUHT9+XIsXL1ZaWppiYmJ08ODBkPudc9q6datSU1M1fPhw5eTk6MKFCzbDRtH9zsOaNWvuen7k5eXZDBslRUVFmj17tuLi4pScnKylS5eqqqoq5JiWlhYVFBRo1KhReuihh7RixQo1NjYaTRwdX+Y8LFiw4K7nw/r1640m7lyfCNC+ffu0ZcsWbdu2TR9//LEyMjKUm5urK1euWI/W4x577DFdvnw5uP72t79ZjxR1zc3NysjI0I4dOzq9f/v27XrzzTf19ttv6+TJkxo5cqRyc3PV0tLSw5NG1/3OgyTl5eWFPD/27NnTgxNGX3l5uQoKCnTixAkdPXpUbW1tWrRokZqbm4PHbN68WYcPH9b+/ftVXl6uS5cuafny5YZTR96XOQ+StHbt2pDnw/bt240m7oLrA+bMmeMKCgqCX7e3t7u0tDRXVFRkOFXP27Ztm8vIyLAew5Qkd+DAgeDXHR0dzufzuZ///OfB25qampzH43F79uwxmLBnfPE8OOfc6tWr3ZIlS0zmsXLlyhUnyZWXlzvnbv9vP3ToULd///7gMf/85z+dJFdRUWE1ZtR98Tw459zXv/519+KLL9oN9SX0+iugW7duqbKyUjk5OcHbBg0apJycHFVUVBhOZuPChQtKS0vTxIkT9eyzz6qurs56JFO1tbVqaGgIeX54vV5lZmYOyOdHWVmZkpOTNXXqVG3YsEFXr161Himq/H6/JCkxMVGSVFlZqba2tpDnw7Rp0zRu3Lh+/Xz44nn43LvvvqukpCRNnz5dhYWFunnzpsV4Xep1H0b6RZ9++qna29uVkpIScntKSoo++eQTo6lsZGZmqri4WFOnTtXly5f1+uuva/78+Tp//rzi4uKsxzPR0NAgSZ0+Pz6/b6DIy8vT8uXLlZ6erpqaGv3gBz9Qfn6+KioqNHjwYOvxIq6jo0ObNm3S3LlzNX36dEm3nw+xsbFKSEgIObY/Px86Ow+S9J3vfEfjx49XWlqazp07p+9///uqqqrSn/70J8NpQ/X6AOF/8vPzg3+eOXOmMjMzNX78eL333nt6/vnnDSdDb7Bq1argn2fMmKGZM2dq0qRJKisr08KFCw0ni46CggKdP39+QLwOei9dnYd169YF/zxjxgylpqZq4cKFqqmp0aRJk3p6zE71+l/BJSUlafDgwXe9i6WxsVE+n89oqt4hISFBU6ZMUXV1tfUoZj5/DvD8uNvEiROVlJTUL58fGzdu1JEjR/Thhx+G/PMtPp9Pt27dUlNTU8jx/fX50NV56ExmZqYk9arnQ68PUGxsrGbNmqXS0tLgbR0dHSotLVVWVpbhZPZu3LihmpoapaamWo9iJj09XT6fL+T5EQgEdPLkyQH//Lh48aKuXr3ar54fzjlt3LhRBw4c0LFjx5Senh5y/6xZszR06NCQ50NVVZXq6ur61fPhfuehM2fPnpWk3vV8sH4XxJexd+9e5/F4XHFxsfvHP/7h1q1b5xISElxDQ4P1aD3qe9/7nisrK3O1tbXuo48+cjk5OS4pKclduXLFerSoun79ujtz5ow7c+aMk+R++ctfujNnzrj//Oc/zjnnfvrTn7qEhAR36NAhd+7cObdkyRKXnp7uPvvsM+PJI+te5+H69evupZdechUVFa62ttZ98MEH7mtf+5p75JFHXEtLi/XoEbNhwwbn9XpdWVmZu3z5cnDdvHkzeMz69evduHHj3LFjx9zp06ddVlaWy8rKMpw68u53Hqqrq92PfvQjd/r0aVdbW+sOHTrkJk6c6LKzs40nD9UnAuScc2+99ZYbN26ci42NdXPmzHEnTpywHqnHrVy50qWmprrY2Fj38MMPu5UrV7rq6mrrsaLuww8/dJLuWqtXr3bO3X4r9quvvupSUlKcx+NxCxcudFVVVbZDR8G9zsPNmzfdokWL3OjRo93QoUPd+PHj3dq1a/vd/0nr7L9fktu1a1fwmM8++8x997vfdV/5ylfciBEj3LJly9zly5ftho6C+52Huro6l52d7RITE53H43GTJ092L7/8svP7/baDfwH/HAMAwESvfw0IANA/ESAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAm/g+WuDaLjaRpZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 0\n",
      "Predicted Class: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 9\n",
      "Predicted Class: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 5\n",
      "Predicted Class: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 9\n",
      "Predicted Class: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 0\n",
      "Predicted Class: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 6\n",
      "Predicted Class: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 9\n",
      "Predicted Class: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 0\n",
      "Predicted Class: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 5\n",
      "Predicted Class: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 9\n",
      "Predicted Class: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 7\n",
      "Predicted Class: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 3\n",
      "Predicted Class: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 9\n",
      "Predicted Class: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 6\n",
      "Predicted Class: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 6\n",
      "Predicted Class: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 5\n",
      "Predicted Class: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 0\n",
      "Predicted Class: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 7\n",
      "Predicted Class: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 0\n",
      "Predicted Class: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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169frpptukiStW7dOV1xxhXbu3Klrr702cZ0DANLaOX0GFA6HJUk5OTmSpD179ujEiRMqKSmJHjNlyhSNGzdO9fX1fX6Nnp4eRSKRmAEAGPziDqDe3l4tW7ZM1113naZOnSpJam9vV2ZmpkaNGhVzbH5+vtrb2/v8OlVVVQoEAtExduzYeFsCAKSRuAOovLxcH3zwgTZs2HBODVRWViocDkdHa2vrOX09AEB6iOsHUSsqKvTGG29ox44dGjNmTHR7MBjU8ePH1dnZGXMX1NHRoWAw2OfX8vv98vv98bQBAEhjnu6AnHOqqKjQ5s2btX37dhUWFsbsnzFjhkaMGKGamprotoaGBrW0tKi4uDgxHQMABgVPd0Dl5eVav369tm7dqqysrOjnOoFAQCNHjlQgENA999yjFStWKCcnR9nZ2XrggQdUXFzME3AAgBieAmjt2rWSpNmzZ8dsX7dunZYsWSLp1FpaGRkZWrRokXp6elRaWqrnn38+Ic0CAAYPn3POWTfxvyKRiAKBgHUbQ0pGRnzPorz44ouea37wgx/Eda6B8uWXX3quOXz4sOeaiy66yHNNZmam55pUt2zZMs81zz33XOIbQVKEw2FlZ2f3u5+14AAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJuL6jagYXHp7e+Oqq62t9VyT6qthDx/u/a9Efn5+EjpJP5WVlZ5r+FUtQxt3QAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEz4nHPOuon/FYlEFAgErNvAN5CR4f3fL5dddpnnmn/+85+ea3BuHn30Uc81Tz/9tOeaeBfCRXoIh8PKzs7udz93QAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwMt24A6SuehSTb29s918ycOdNzzaWXXuq5RpJWrlzpuWbv3r2ea7Zv3+655qWXXvJcE68vv/zSc02KrWuMNMAdEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABM+l2IrCEYiEQUCAes2AADnKBwOKzs7u9/93AEBAEwQQAAAE54CqKqqStdcc42ysrKUl5enhQsXqqGhIeaY2bNny+fzxYylS5cmtGkAQPrzFEB1dXUqLy/Xzp079dZbb+nEiROaO3euurq6Yo6799571dbWFh2rV69OaNMAgPTn6Teibtu2LeZ1dXW18vLytGfPHs2aNSu6/fzzz1cwGExMhwCAQemcPgMKh8OSpJycnJjtr7zyinJzczV16lRVVlbq2LFj/X6Nnp4eRSKRmAEAGAJcnE6ePOluueUWd91118Vs//3vf++2bdvm9u/f715++WU3evRod+utt/b7dVatWuUkMRgMBmOQjXA4fMYciTuAli5d6saPH+9aW1vPeFxNTY2T5BobG/vc393d7cLhcHS0traaTxqDwWAwzn2cLYA8fQb0lYqKCr3xxhvasWOHxowZc8Zji4qKJEmNjY2aNGnSafv9fr/8fn88bQAA0pinAHLO6YEHHtDmzZtVW1urwsLCs9bs27dPklRQUBBXgwCAwclTAJWXl2v9+vXaunWrsrKy1N7eLkkKBAIaOXKkmpqatH79et188826+OKLtX//fi1fvlyzZs3StGnTkvIfAABIU14+91E/7/OtW7fOOedcS0uLmzVrlsvJyXF+v99NnjzZPfzww2d9H/B/hcNh8/ctGQwGg3Hu42zf+1mMFACQFCxGCgBISQQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEykXQM456xYAAAlwtu/nKRdAR44csW4BAJAAZ/t+7nMpdsvR29urgwcPKisrSz6fL2ZfJBLR2LFj1draquzsbKMO7TEPpzAPpzAPpzAPp6TCPDjndOTIEYVCIWVk9H+fM3wAe/pGMjIyNGbMmDMek52dPaQvsK8wD6cwD6cwD6cwD6dYz0MgEDjrMSn3FhwAYGgggAAAJtIqgPx+v1atWiW/32/diinm4RTm4RTm4RTm4ZR0moeUewgBADA0pNUdEABg8CCAAAAmCCAAgAkCCABgIm0CaM2aNZowYYLOO+88FRUV6b333rNuacA98cQT8vl8MWPKlCnWbSXdjh07NH/+fIVCIfl8Pm3ZsiVmv3NOK1euVEFBgUaOHKmSkhIdOHDAptkkOts8LFmy5LTrY968eTbNJklVVZWuueYaZWVlKS8vTwsXLlRDQ0PMMd3d3SovL9fFF1+sCy+8UIsWLVJHR4dRx8nxTeZh9uzZp10PS5cuNeq4b2kRQBs3btSKFSu0atUqvf/++5o+fbpKS0t16NAh69YG3JVXXqm2trbo+Pvf/27dUtJ1dXVp+vTpWrNmTZ/7V69erWeffVYvvPCCdu3apQsuuEClpaXq7u4e4E6T62zzIEnz5s2LuT5effXVAeww+erq6lReXq6dO3fqrbfe0okTJzR37lx1dXVFj1m+fLlef/11bdq0SXV1dTp48KBuu+02w64T75vMgyTde++9MdfD6tWrjTruh0sDM2fOdOXl5dHXJ0+edKFQyFVVVRl2NfBWrVrlpk+fbt2GKUlu8+bN0de9vb0uGAy6p59+Orqts7PT+f1+9+qrrxp0ODC+Pg/OObd48WK3YMECk36sHDp0yElydXV1zrlT/+9HjBjhNm3aFD3mX//6l5Pk6uvrrdpMuq/Pg3POffe733U//elP7Zr6BlL+Duj48ePas2ePSkpKotsyMjJUUlKi+vp6w85sHDhwQKFQSBMnTtTdd9+tlpYW65ZMNTc3q729Peb6CAQCKioqGpLXR21trfLy8nT55Zfr/vvv1+HDh61bSqpwOCxJysnJkSTt2bNHJ06ciLkepkyZonHjxg3q6+Hr8/CVV155Rbm5uZo6daoqKyt17Ngxi/b6lXKLkX7d559/rpMnTyo/Pz9me35+vv79738bdWWjqKhI1dXVuvzyy9XW1qYnn3xSN9xwgz744ANlZWVZt2eivb1dkvq8Pr7aN1TMmzdPt912mwoLC9XU1KRHH31UZWVlqq+v17Bhw6zbS7je3l4tW7ZM1113naZOnSrp1PWQmZmpUaNGxRw7mK+HvuZBkr7//e9r/PjxCoVC2r9/v372s5+poaFBf/nLXwy7jZXyAYT/V1ZWFv3ztGnTVFRUpPHjx+u1117TPffcY9gZUsGdd94Z/fNVV12ladOmadKkSaqtrdWcOXMMO0uO8vJyffDBB0Pic9Az6W8e7rvvvuifr7rqKhUUFGjOnDlqamrSpEmTBrrNPqX8W3C5ubkaNmzYaU+xdHR0KBgMGnWVGkaNGqXLLrtMjY2N1q2Y+eoa4Po43cSJE5Wbmzsor4+Kigq98cYbeuedd2J+fUswGNTx48fV2dkZc/xgvR76m4e+FBUVSVJKXQ8pH0CZmZmaMWOGampqott6e3tVU1Oj4uJiw87sHT16VE1NTSooKLBuxUxhYaGCwWDM9RGJRLRr164hf318+umnOnz48KC6Ppxzqqio0ObNm7V9+3YVFhbG7J8xY4ZGjBgRcz00NDSopaVlUF0PZ5uHvuzbt0+SUut6sH4K4pvYsGGD8/v9rrq62n344Yfuvvvuc6NGjXLt7e3WrQ2oBx980NXW1rrm5mb37rvvupKSEpebm+sOHTpk3VpSHTlyxO3du9ft3bvXSXK//e1v3d69e90nn3zinHPuqaeecqNGjXJbt251+/fvdwsWLHCFhYXuiy++MO48sc40D0eOHHEPPfSQq6+vd83Nze7tt9923/72t92ll17quru7rVtPmPvvv98FAgFXW1vr2traouPYsWPRY5YuXerGjRvntm/f7nbv3u2Ki4tdcXGxYdeJd7Z5aGxsdL/4xS/c7t27XXNzs9u6daubOHGimzVrlnHnsdIigJxz7rnnnnPjxo1zmZmZbubMmW7nzp3WLQ24O+64wxUUFLjMzEw3evRod8cdd7jGxkbrtpLunXfecZJOG4sXL3bOnXoU+/HHH3f5+fnO7/e7OXPmuIaGBtumk+BM83Ds2DE3d+5cd8kll7gRI0a48ePHu3vvvXfQ/SOtr/9+SW7dunXRY7744gv3k5/8xF100UXu/PPPd7feeqtra2uzazoJzjYPLS0tbtasWS4nJ8f5/X43efJk9/DDD7twOGzb+Nfw6xgAACZS/jMgAMDgRAABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwMT/ATSXwrB1LuE/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 3\n",
      "Predicted Class: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 3\n",
      "Predicted Class: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 7\n",
      "Predicted Class: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 2\n",
      "Predicted Class: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 7\n",
      "Predicted Class: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 2\n",
      "Predicted Class: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 7\n",
      "Predicted Class: 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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4yQEAKe2qfgcUDoclSTk5OZKk+vp6nTlzJuYKn9GjR2vo0KEX/Rjdjo4ORSKRmA0AkP7iDlBXV5eWLFmiyZMna+zYsZKk1tZWZWRkKDs7O+a1+fn5am1t7fb7VFVVKRAIRLfCwsJ4RwIApJC4A1ReXq69e/fqtddeu6oBKisrFQ6Ho9uhQ4eu6vsBAFJDXP8QdfHixXrrrbe0bds2DRkyJPp4MBhUZ2enjh8/HnMW1NbWpmAw2O338vv98vv98YwBAEhhns6AnHNavHixNm7cqPfee09FRUUxz0+cOFEDBgxQdXV19LGGhgYdPHhQJSUliZkYAJAWPJ0BlZeXa/369dq8ebMyMzOjv9cJBAIaNGiQAoGAHnnkEVVUVCgnJ0dZWVl69NFHVVJSwhVwAIAYngK0Zs0aSdLUqVNjHl+7dq3mzZsnSfrNb36jfv36ac6cOero6NCMGTP0u9/9LiHDAgDSh88556yH+LJIJKJAIGA9Bvqo86/gvBJ/+9vfPK+J52KbBx980POarq4uz2uARAmHw8rKyrro89wLDgBgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACbi+kRUIF0tX77c85ovfyrwlVqwYIHnNdzZGumGMyAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQ3I0VamjhxYlzrfvCDH3hes3LlSs9rPvnkE89rgHTDGRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIKbkSIt/fjHP45rXWdnp+c1r732Wlz7Avo6zoAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABPcjBRp6a677opr3e9//3vPa/773//GtS+gr+MMCABgggABAEx4ClBVVZVuvfVWZWZmKi8vT7Nnz1ZDQ0PMa6ZOnSqfzxezLVy4MKFDAwBSn6cA1dbWqry8XNu3b9e7776rM2fOaPr06Wpvb4953fz589XS0hLdVq1aldChAQCpz9NFCFu2bIn5et26dcrLy1N9fb2mTJkSffzaa69VMBhMzIQAgLR0Vb8DCofDkqScnJyYx1955RXl5uZq7Nixqqys1KlTpy76PTo6OhSJRGI2AED6i/sy7K6uLi1ZskSTJ0/W2LFjo48/+OCDGjZsmEKhkPbs2aNly5apoaFBGzZs6Pb7VFVV6amnnop3DABAioo7QOXl5dq7d68++OCDmMcXLFgQ/fO4ceNUUFCgadOmqampSSNHjrzg+1RWVqqioiL6dSQSUWFhYbxjAQBSRFwBWrx4sd566y1t27ZNQ4YMueRri4uLJUmNjY3dBsjv98vv98czBgAghXkKkHNOjz76qDZu3KiamhoVFRVdds3u3bslSQUFBXENCABIT54CVF5ervXr12vz5s3KzMxUa2urJCkQCGjQoEFqamrS+vXr9e1vf1s33HCD9uzZo6VLl2rKlCkaP358Uv4DAACpyVOA1qxZI+ncPzb9srVr12revHnKyMjQ1q1b9fzzz6u9vV2FhYWaM2eOli9fnrCBAQDpwfOP4C6lsLBQtbW1VzUQAKBv4G7YSEsffvhhXOu4awfQc7gZKQDABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgwucud4vrHhaJRBQIBKzHAABcpXA4rKysrIs+zxkQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE70uQL3s1nQAgDhd7u/zXhegEydOWI8AAEiAy/193uvuht3V1aUjR44oMzNTPp8v5rlIJKLCwkIdOnTokndYTXcch3M4DudwHM7hOJzTG46Dc04nTpxQKBRSv34XP8+5pgdnuiL9+vXTkCFDLvmarKysPv0G+x+Owzkch3M4DudwHM6xPg5X8rE6ve5HcACAvoEAAQBMpFSA/H6/VqxYIb/fbz2KKY7DORyHczgO53Aczkml49DrLkIAAPQNKXUGBABIHwQIAGCCAAEATBAgAICJlAnQ6tWrNXz4cA0cOFDFxcX66KOPrEfqcStXrpTP54vZRo8ebT1W0m3btk133323QqGQfD6fNm3aFPO8c05PPvmkCgoKNGjQIJWWlmr//v02wybR5Y7DvHnzLnh/zJw502bYJKmqqtKtt96qzMxM5eXlafbs2WpoaIh5zenTp1VeXq4bbrhB119/vebMmaO2tjajiZPjSo7D1KlTL3g/LFy40Gji7qVEgF5//XVVVFRoxYoV+vjjjzVhwgTNmDFDR48etR6tx40ZM0YtLS3R7YMPPrAeKena29s1YcIErV69utvnV61apRdeeEEvvfSSduzYoeuuu04zZszQ6dOne3jS5LrccZCkmTNnxrw/Xn311R6cMPlqa2tVXl6u7du3691339WZM2c0ffp0tbe3R1+zdOlSvfnmm3rjjTdUW1urI0eO6N577zWcOvGu5DhI0vz582PeD6tWrTKa+CJcCpg0aZIrLy+Pfn327FkXCoVcVVWV4VQ9b8WKFW7ChAnWY5iS5DZu3Bj9uqurywWDQferX/0q+tjx48ed3+93r776qsGEPeP84+Ccc3PnznWzZs0ymcfK0aNHnSRXW1vrnDv3v/2AAQPcG2+8EX3NP//5TyfJ1dXVWY2ZdOcfB+ecu+OOO9xPfvITu6GuQK8/A+rs7FR9fb1KS0ujj/Xr10+lpaWqq6sznMzG/v37FQqFNGLECD300EM6ePCg9Uimmpub1draGvP+CAQCKi4u7pPvj5qaGuXl5WnUqFFatGiRjh07Zj1SUoXDYUlSTk6OJKm+vl5nzpyJeT+MHj1aQ4cOTev3w/nH4X9eeeUV5ebmauzYsaqsrNSpU6csxruoXncz0vN99tlnOnv2rPLz82Mez8/P1759+4ymslFcXKx169Zp1KhRamlp0VNPPaXbb79de/fuVWZmpvV4JlpbWyWp2/fH/57rK2bOnKl7771XRUVFampq0s9//nOVlZWprq5O/fv3tx4v4bq6urRkyRJNnjxZY8eOlXTu/ZCRkaHs7OyY16bz+6G74yBJDz74oIYNG6ZQKKQ9e/Zo2bJlamho0IYNGwynjdXrA4T/V1ZWFv3z+PHjVVxcrGHDhukvf/mLHnnkEcPJ0Bvcf//90T+PGzdO48eP18iRI1VTU6Np06YZTpYc5eXl2rt3b5/4PeilXOw4LFiwIPrncePGqaCgQNOmTVNTU5NGjhzZ02N2q9f/CC43N1f9+/e/4CqWtrY2BYNBo6l6h+zsbN1yyy1qbGy0HsXM/94DvD8uNGLECOXm5qbl+2Px4sV666239P7778d8fEswGFRnZ6eOHz8e8/p0fT9c7Dh0p7i4WJJ61fuh1wcoIyNDEydOVHV1dfSxrq4uVVdXq6SkxHAyeydPnlRTU5MKCgqsRzFTVFSkYDAY8/6IRCLasWNHn39/HD58WMeOHUur94dzTosXL9bGjRv13nvvqaioKOb5iRMnasCAATHvh4aGBh08eDCt3g+XOw7d2b17tyT1rveD9VUQV+K1115zfr/frVu3zv3jH/9wCxYscNnZ2a61tdV6tB7105/+1NXU1Ljm5mb34YcfutLSUpebm+uOHj1qPVpSnThxwu3atcvt2rXLSXLPPfec27Vrl/vPf/7jnHPumWeecdnZ2W7z5s1uz549btasWa6oqMh98cUXxpMn1qWOw4kTJ9xjjz3m6urqXHNzs9u6dav7+te/7m6++WZ3+vRp69ETZtGiRS4QCLiamhrX0tIS3U6dOhV9zcKFC93QoUPde++953bu3OlKSkpcSUmJ4dSJd7nj0NjY6H7xi1+4nTt3uubmZrd582Y3YsQIN2XKFOPJY6VEgJxz7sUXX3RDhw51GRkZbtKkSW779u3WI/W4++67zxUUFLiMjAx34403uvvuu881NjZaj5V077//vpN0wTZ37lzn3LlLsZ944gmXn5/v/H6/mzZtmmtoaLAdOgkudRxOnTrlpk+f7gYPHuwGDBjghg0b5ubPn592/yetu/9+SW7t2rXR13zxxRfuRz/6kfvKV77irr32WnfPPfe4lpYWu6GT4HLH4eDBg27KlCkuJyfH+f1+d9NNN7mf/exnLhwO2w5+Hj6OAQBgotf/DggAkJ4IEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABP/B7i9khJMp/zWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 2\n",
      "Predicted Class: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 3\n",
      "Predicted Class: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 5\n",
      "Predicted Class: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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t080336zt27fr29/+dvwmBwAMalf0HlAoFJIkpaenS5J27dql8+fPq7i4OHJMfn6+cnNz1dDQ0Ovv0dXVpXA4HLUAAMkv5gD19PRoxYoVuuOOOzRlyhRJUltbm4YPH67Ro0dHHZuVlaW2trZef5/KykoFAoHIGj9+fKwjAQAGkZgDVF5err179+q11167ogEqKioUCoUiq7W19Yp+PwDA4BDTX0Rdvny53n33XdXX12vcuHGR24PBoM6dO6eTJ09GvQpqb29XMBjs9ffy+/3y+/2xjAEAGMQ8vQJyzmn58uWqrq7W1q1blZeXF3X/9OnTNWzYMNXU1ERua2xsVEtLiwoLC+MzMQAgKXh6BVReXq4NGzZo06ZNSk1NjbyvEwgENHLkSAUCAT344INauXKl0tPTlZaWpoceekiFhYV8Ag4AEMVTgF544QVJ0qxZs6JuX7dunRYtWiRJ+v3vf6+UlBQtWLBAXV1dKikp0Z/+9Ke4DAsASB6eAvR1LlA4YsQIVVVVqaqqKuah0L+OHz8e074TJ0543hPrhU+TTX5+vuc9sVzs87bbbvO8R5I++eQTz3vWrl0b02Ph6sW14AAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGAipp+ICkjSvn37PO/58Y9/7HlPLFfrrq6u9rxHksrKyjzvmTlzpuc98+fP97xn1KhRnve8/fbbnvdI0rJlyzzv6ejoiOmxcPXiFRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYMLnnHPWQ3xZOBxWIBCwHgNfQ35+vuc99fX1nveMGTPG856UlNj+36qnp6dfHuutt97yvOeVV17xvCfWi7IC8RAKhZSWltbn/bwCAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMcDFS9Kvc3FzPexYvXux5z8yZMz3vkWK7eOdHH33kec+BAwc87zlz5oznPYAlLkYKABiQCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATXIwUAJAQXIwUADAgESAAgAlPAaqsrNTtt9+u1NRUZWZmav78+WpsbIw6ZtasWfL5fFFr6dKlcR0aADD4eQpQXV2dysvLtX37dm3ZskXnz5/XnDlz1NnZGXXc4sWLdfTo0chas2ZNXIcGAAx+Q70cvHnz5qiv169fr8zMTO3atUtFRUWR20eNGqVgMBifCQEASemK3gMKhUKSpPT09KjbX3nlFWVkZGjKlCmqqKi45I8S7urqUjgcjloAgKuAi1F3d7f7/ve/7+64446o2//85z+7zZs3uz179rh//OMf7rrrrnNlZWV9/j6rV692klgsFouVZCsUCl2yIzEHaOnSpW7ChAmutbX1ksfV1NQ4Sa6pqanX+8+ePetCoVBktba2mp80FovFYl35ulyAPL0H9IXly5fr3XffVX19vcaNG3fJYwsKCiRJTU1NmjRp0kX3+/1++f3+WMYAAAxingLknNNDDz2k6upq1dbWKi8v77J7du/eLUnKzs6OaUAAQHLyFKDy8nJt2LBBmzZtUmpqqtra2iRJgUBAI0eO1KFDh7RhwwZ973vf05gxY7Rnzx49/PDDKioq0tSpUxPyDwAAGKS8vO+jPr7Pt27dOueccy0tLa6oqMilp6c7v9/vJk+e7B599NHLfh/wy0KhkPn3LVksFot15etyf/ZzMVIAQEJwMVIAwIBEgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADAx4ALknLMeAQAQB5f783zABejUqVPWIwAA4uByf5773AB7ydHT06MjR44oNTVVPp8v6r5wOKzx48ertbVVaWlpRhPa4zxcwHm4gPNwAefhgoFwHpxzOnXqlHJycpSS0vfrnKH9ONPXkpKSonHjxl3ymLS0tKv6CfYFzsMFnIcLOA8XcB4usD4PgUDgsscMuG/BAQCuDgQIAGBiUAXI7/dr9erV8vv91qOY4jxcwHm4gPNwAefhgsF0HgbchxAAAFeHQfUKCACQPAgQAMAEAQIAmCBAAAATgyZAVVVVuv766zVixAgVFBTo448/th6p3z311FPy+XxRKz8/33qshKuvr9fdd9+tnJwc+Xw+bdy4Mep+55xWrVql7OxsjRw5UsXFxTp48KDNsAl0ufOwaNGii54fc+fOtRk2QSorK3X77bcrNTVVmZmZmj9/vhobG6OOOXv2rMrLyzVmzBhde+21WrBggdrb240mToyvcx5mzZp10fNh6dKlRhP3blAE6PXXX9fKlSu1evVqffLJJ5o2bZpKSkp07Ngx69H63S233KKjR49G1rZt26xHSrjOzk5NmzZNVVVVvd6/Zs0arV27Vi+++KJ27Niha665RiUlJTp79mw/T5pYlzsPkjR37tyo58err77ajxMmXl1dncrLy7V9+3Zt2bJF58+f15w5c9TZ2Rk55uGHH9Y777yjN998U3V1dTpy5Ijuuecew6nj7+ucB0lavHhx1PNhzZo1RhP3wQ0CM2bMcOXl5ZGvu7u7XU5OjqusrDScqv+tXr3aTZs2zXoMU5JcdXV15Ouenh4XDAbdb3/728htJ0+edH6/37366qsGE/aPr54H55xbuHChmzdvnsk8Vo4dO+Ykubq6OufchX/3w4YNc2+++WbkmP379ztJrqGhwWrMhPvqeXDOue985zvupz/9qd1QX8OAfwV07tw57dq1S8XFxZHbUlJSVFxcrIaGBsPJbBw8eFA5OTmaOHGiHnjgAbW0tFiPZKq5uVltbW1Rz49AIKCCgoKr8vlRW1urzMxM3XTTTVq2bJlOnDhhPVJChUIhSVJ6erokadeuXTp//nzU8yE/P1+5ublJ/Xz46nn4wiuvvKKMjAxNmTJFFRUVOnPmjMV4fRpwFyP9qo6ODnV3dysrKyvq9qysLB04cMBoKhsFBQVav369brrpJh09elRPP/20Zs6cqb179yo1NdV6PBNtbW2S1Ovz44v7rhZz587VPffco7y8PB06dEiPP/64SktL1dDQoCFDhliPF3c9PT1asWKF7rjjDk2ZMkXShefD8OHDNXr06Khjk/n50Nt5kKQf/vCHmjBhgnJycrRnzx79/Oc/V2Njo95++23DaaMN+ADh/0pLSyO/njp1qgoKCjRhwgS98cYbevDBBw0nw0Bw3333RX596623aurUqZo0aZJqa2s1e/Zsw8kSo7y8XHv37r0q3ge9lL7Ow5IlSyK/vvXWW5Wdna3Zs2fr0KFDmjRpUn+P2asB/y24jIwMDRky5KJPsbS3tysYDBpNNTCMHj1aN954o5qamqxHMfPFc4Dnx8UmTpyojIyMpHx+LF++XO+++64+/PDDqB/fEgwGde7cOZ08eTLq+GR9PvR1HnpTUFAgSQPq+TDgAzR8+HBNnz5dNTU1kdt6enpUU1OjwsJCw8nsnT59WocOHVJ2drb1KGby8vIUDAajnh/hcFg7duy46p8fhw8f1okTJ5Lq+eGc0/Lly1VdXa2tW7cqLy8v6v7p06dr2LBhUc+HxsZGtbS0JNXz4XLnoTe7d++WpIH1fLD+FMTX8dprrzm/3+/Wr1/v9u3b55YsWeJGjx7t2trarEfrVz/72c9cbW2ta25udv/6179ccXGxy8jIcMeOHbMeLaFOnTrlPv30U/fpp586Se53v/ud+/TTT91nn33mnHPu17/+tRs9erTbtGmT27Nnj5s3b57Ly8tzn3/+ufHk8XWp83Dq1Cn3yCOPuIaGBtfc3Ow++OADd9ttt7kbbrjBnT171nr0uFm2bJkLBAKutrbWHT16NLLOnDkTOWbp0qUuNzfXbd261e3cudMVFha6wsJCw6nj73LnoampyT3zzDNu586drrm52W3atMlNnDjRFRUVGU8ebVAEyDnn/vjHP7rc3Fw3fPhwN2PGDLd9+3brkfrdvffe67Kzs93w4cPddddd5+69917X1NRkPVbCffjhh07SRWvhwoXOuQsfxX7yySddVlaW8/v9bvbs2a6xsdF26AS41Hk4c+aMmzNnjhs7dqwbNmyYmzBhglu8eHHS/U9ab//8kty6desix3z++efuJz/5ifvGN77hRo0a5crKytzRo0fthk6Ay52HlpYWV1RU5NLT053f73eTJ092jz76qAuFQraDfwU/jgEAYGLAvwcEAEhOBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJ/wGadsfDMX4yaQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 1\n",
      "Predicted Class: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 2\n",
      "Predicted Class: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Class: 4\n",
      "Predicted Class: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seePredictions(model, best_train_states, x_val[:50], y_val[:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# Whats wrong in this?\n",
    "def my_patchify(x, P): # (B, H, W, C)\n",
    "    N = np.prod(x.shape[1:-1])//P**2\n",
    "    assert N*P**2 == H*W, \"<<-- should be True for patching\"\n",
    "    return x.reshape((-1, N, (P**2)*x.shape[-1]))\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}