path: root/transformer_shortest_paths.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LPphBnKR-aWF"
   },
   "source": [
    "# Step 0: Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: \n",
    "\n",
    "- Do the attention heads learn to attend to the same positional encodings\n",
    "- do interp -- what is it doing? can we figure out?\n",
    "- update: I think we should do interp once it's bigger. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "execution_state": "idle",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ge5QvElvhCOw",
    "outputId": "c7cdaefa-d6dc-44ad-c258-e4fb2aca97a5"
   },
   "outputs": [],
   "source": [
    "# using tqdm.auto glitches out collaborative editing\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "from math import sqrt\n",
    "from collections import deque\n",
    "import os\n",
    "import random\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "import pickle\n",
    "\n",
    "torch.manual_seed(42)\n",
    "random.seed(42)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "assert device.type == \"cuda\", \"CUDA is not available. Please check your GPU setup.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "execution_state": "idle",
   "metadata": {
    "id": "lylOX2POPwFL"
   },
   "outputs": [],
   "source": [
    "# VTXS numbers here are inclusive\n",
    "MIN_VTXS = 3\n",
    "MAX_VTXS = 15\n",
    "MAX_TUNE_VTXS = 7 # 15\n",
    "AVG_DEG = 2\n",
    "SEQ_LEN = MAX_VTXS + 1 # means 32 edges, final token is the target vertex\n",
    "PAD_TOKEN = 0\n",
    "# vertices are labelled 1,2,...,63\n",
    "# we also have a padding token which is 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gKt-yIpDebF1"
   },
   "source": [
    "# Step 1: Generate synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "execution_state": "idle",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1IbzGIWseK3E",
    "outputId": "a3cbc233-358c-4e17-ea6e-f4e9349d886b"
   },
   "outputs": [],
   "source": [
    "# original task data\n",
    "# the data will be edge lists\n",
    "# like this: [1 3 1 5 2 4 0 0 0 0 2]\n",
    "# this represents edges (1,3), (1,5) (2,4)\n",
    "# (the zeros are just padding tokens)\n",
    "# the final 2 means which vertex we're going to \n",
    "\n",
    "# the label is the shortest distance from vtx 1 to vtx 2\n",
    "# or \"number of vertices\" if no path exists\n",
    "\n",
    "def random_graph(n):\n",
    "    edge_list = []\n",
    "    adjacencies = [set() for _ in range(n+1)]\n",
    "    indices = [random.randint(1, n) for _ in range(AVG_DEG * n)]\n",
    "    for i in range(0, len(indices), 2):\n",
    "        u = indices[i]\n",
    "        v = indices[i + 1]\n",
    "        if u != v:\n",
    "            edge_list += [min(u,v),max(u,v)]\n",
    "            adjacencies[u].add(v)\n",
    "            adjacencies[v].add(u)\n",
    "    edge_list += [PAD_TOKEN]*(2*SEQ_LEN-1-len(edge_list))\n",
    "    return edge_list, adjacencies\n",
    "\n",
    "\"\"\"\n",
    "input: G, represented as an adjacency list\n",
    "output: [number of vertices]+[d(1,i) for i in range(n)] if target=None\n",
    "if target is set to some value, then we instead just output that specific distance\n",
    "\"\"\"\n",
    "def SSSP(G, target=2):\n",
    "    dist = [MAX_VTXS for _ in G]\n",
    "    dist[1] = 0\n",
    "    frontier = deque()\n",
    "    frontier.append(1)\n",
    "    while len(frontier) > 0:\n",
    "        vtx = frontier.popleft()\n",
    "        for x in G[vtx]:\n",
    "            if dist[x] == MAX_VTXS:\n",
    "                dist[x] = 1 + dist[vtx]\n",
    "                frontier.append(x)\n",
    "                if x == target:\n",
    "                    return dist[target]\n",
    "    if target is not None:\n",
    "        return dist[target]\n",
    "    else:\n",
    "        return dist\n",
    "\n",
    "def mkbatch(size):\n",
    "    graphs1 = []\n",
    "    distance1 = []\n",
    "    \n",
    "    for i in range(size):\n",
    "        n = random.randint(MIN_VTXS, MAX_VTXS)\n",
    "        edge_list, adj_list = random_graph(n)\n",
    "        dist = SSSP(adj_list)\n",
    "        edge_list[-1] = 2 # target token\n",
    "        graphs1.append(edge_list)\n",
    "        distance1.append(dist)\n",
    "    \n",
    "    data = torch.tensor(graphs1)\n",
    "    labels = torch.tensor(distance1, dtype=torch.bfloat16)\n",
    "    padding = data == PAD_TOKEN\n",
    "    return data, labels, padding\n",
    "\n",
    "def savebatch(size, idx):\n",
    "    data, labels, padding = mkbatch(size)\n",
    "    everything = {\n",
    "        \"data\": data,\n",
    "        \"labels\": labels,\n",
    "        \"padding\": padding,\n",
    "    }\n",
    "    \n",
    "    with open(f'data/{idx}.pickle', 'wb') as file:\n",
    "        pickle.dump(everything, file)\n",
    "\n",
    "def vertices_on_shortest_12_path(G, target=2):\n",
    "    dist = [MAX_VTXS for _ in G]\n",
    "    parent = [-1 for _ in G]\n",
    "    dist[1] = 0\n",
    "    frontier = deque()\n",
    "    frontier.append(1)\n",
    "    while len(frontier) > 0:\n",
    "        vtx = frontier.popleft()\n",
    "        for x in G[vtx]:\n",
    "            if dist[x] == MAX_VTXS:\n",
    "                parent[x] = vtx\n",
    "                dist[x] = 1 + dist[vtx]\n",
    "                frontier.append(x)\n",
    "                if x == target:\n",
    "                    path = [x]\n",
    "                    while parent[x] != -1:\n",
    "                        x = parent[x]\n",
    "                        path.append(x)\n",
    "                    return list(reversed(path))\n",
    "    return []\n",
    "\n",
    "def mktunebatch(size, test=False):\n",
    "    graphs = []\n",
    "    distance = []\n",
    "    \n",
    "    for i in range(size):\n",
    "        n = random.randint(MIN_VTXS, MAX_VTXS if test else MAX_TUNE_VTXS)\n",
    "        while True:\n",
    "            edge_list, adj_list = random_graph(n)\n",
    "            path = vertices_on_shortest_12_path(adj_list)\n",
    "            if len(path) > 1:\n",
    "                target_vtx_idx = random.randrange(1, len(path))\n",
    "                target_vtx = path[target_vtx_idx]\n",
    "                edge_list[-1] = target_vtx\n",
    "                graphs.append(edge_list)\n",
    "                distance.append(target_vtx_idx)\n",
    "                break\n",
    "    \n",
    "    data = torch.tensor(graphs, device=device)\n",
    "    labels = torch.tensor(distance, dtype=torch.bfloat16, device=device)\n",
    "    padding = data == PAD_TOKEN\n",
    "    return data, labels, padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only need to run this once to generate training data\n",
    "# RESTART THE KERNEL BEFORE RUNNING AND ONLY RUN THE CELLS ABOVE\n",
    "# Python is slow and awful\n",
    "\n",
    "# with ProcessPoolExecutor() as executor:\n",
    "#     for i in range(1000):\n",
    "#         executor.submit(savebatch, 2**20, i)\n",
    "#     executor.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "           7, 11,  3,  8,  3,  5,  4, 12,  0,  0,  0,  0,  2]]),\n",
       " tensor([ 1.,  5.,  1., 15.,  2.], dtype=torch.bfloat16),\n",
       " tensor([[False, False, False, False, False, False, False, False, False, False,\n",
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkbatch(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mkbatch(2048)[1].to(torch.float32).cpu(), bins=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1516.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,  420.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,   94.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,   16.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           2.]),\n",
       " array([1.    , 1.0625, 1.125 , 1.1875, 1.25  , 1.3125, 1.375 , 1.4375,\n",
       "        1.5   , 1.5625, 1.625 , 1.6875, 1.75  , 1.8125, 1.875 , 1.9375,\n",
       "        2.    , 2.0625, 2.125 , 2.1875, 2.25  , 2.3125, 2.375 , 2.4375,\n",
       "        2.5   , 2.5625, 2.625 , 2.6875, 2.75  , 2.8125, 2.875 , 2.9375,\n",
       "        3.    , 3.0625, 3.125 , 3.1875, 3.25  , 3.3125, 3.375 , 3.4375,\n",
       "        3.5   , 3.5625, 3.625 , 3.6875, 3.75  , 3.8125, 3.875 , 3.9375,\n",
       "        4.    , 4.0625, 4.125 , 4.1875, 4.25  , 4.3125, 4.375 , 4.4375,\n",
       "        4.5   , 4.5625, 4.625 , 4.6875, 4.75  , 4.8125, 4.875 , 4.9375,\n",
       "        5.    ]),\n",
       " <BarContainer object of 64 artists>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mktunebatch(2048)[1].to(torch.float32).cpu(), bins=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q3Cg_8UQep8g"
   },
   "source": [
    "# Step 2: Define Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "execution_state": "idle",
   "metadata": {
    "id": "tLOWhg_CeWzH"
   },
   "outputs": [],
   "source": [
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, input_dim, model_dim, output_dim, num_heads, num_layers, seq_len, dropout):\n",
    "        super().__init__()\n",
    "        self.model_dim = model_dim\n",
    "        self.embedding = nn.Embedding(input_dim, model_dim // 2, dtype=torch.bfloat16)\n",
    "        # # seq_len is odd\n",
    "        # self.fancy_encoding = torch.repeat_interleave(torch.rand((1, seq_len // 2 + 1, model_dim // 2), device=device, dtype=torch.bfloat16), 2, dim=1)\n",
    "        # # cut off last element since the target vertex is not repeated\n",
    "        # self.fancy_encoding = self.fancy_encoding[:, :seq_len, :]\n",
    "        encoder_layer = nn.TransformerEncoderLayer(d_model=model_dim, nhead=num_heads,\n",
    "                                                   dim_feedforward=model_dim*4,\n",
    "                                                   dropout=dropout, batch_first=True, dtype=torch.bfloat16)\n",
    "        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)\n",
    "        self.fc_out = nn.Linear(model_dim, output_dim, dtype=torch.bfloat16)\n",
    "\n",
    "    def forward(self, src, key_padding_mask):\n",
    "        batch_sz = src.size(0)\n",
    "        src = torch.cat((src, torch.full((batch_sz, 1), MAX_VTXS + 1, device=device)), dim=1)\n",
    "        embed = torch.cat((self.embedding(src[:,::2]), self.embedding(src[:,1::2])), dim=2)\n",
    "        output = self.transformer_encoder(embed, src_key_padding_mask=key_padding_mask[:, ::2])\n",
    "        return self.fc_out(output[:, -1, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bpIeg86S-hBb"
   },
   "source": [
    "# Step 3: Make Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "execution_state": "idle",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kWXvJRDYgFVP",
    "outputId": "c13adb9d-6565-43b5-8437-20cef3dc0d16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: 104M\n",
      "Trainable parameters in the model: 200545\n"
     ]
    }
   ],
   "source": [
    "# PARAMS\n",
    "VOCAB_SIZE = 1 + MAX_VTXS + 1 # pad plus max number of vertices plus target token\n",
    "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n",
    "NEPOCHS = 100\n",
    "BSZ = 2**17 # Batch size\n",
    "BPE = 8 # Batches per epoch\n",
    "NHEADS = 2\n",
    "NLAYERS = 4\n",
    "DROPOUT = 0 # 0.2\n",
    "model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n",
    "                         output_dim=1, num_heads=NHEADS,\n",
    "                         num_layers=NLAYERS, seq_len=SEQ_LEN,\n",
    "                         dropout=DROPOUT).to(device)\n",
    "model = torch.compile(model)\n",
    "\n",
    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f\"Training data: {NEPOCHS*BPE*BSZ//10**6}M\")\n",
    "print(f\"Trainable parameters in the model: {trainable_params}\")\n",
    "\n",
    "train_err = []\n",
    "len1 = []\n",
    "len2 = []\n",
    "len3 = []\n",
    "len15 = []\n",
    "epoch = 0\n",
    "\n",
    "# clear loss file\n",
    "open('loss', 'w').close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n",
    "NEPOCHS = 1000\n",
    "BSZ = 2**17 # Batch size\n",
    "BPE = 8 # Batches per epoch\n",
    "NHEADS = 2\n",
    "NLAYERS = 4\n",
    "DROPOUT = 0 # 0.2\n",
    "\n",
    "LR of 8e-4 for 1000 epochs to get down to 0.35546875"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n",
    "#                          output_dim=1, num_heads=NHEADS,\n",
    "#                          num_layers=NLAYERS, seq_len=SEQ_LEN,\n",
    "#                          dropout=DROPOUT).to(device)\n",
    "# model = torch.compile(model)\n",
    "# model.load_state_dict(torch.load('model.pth', weights_only=True))\n",
    "\n",
    "LR = 8e-4\n",
    "WD = 0 # 1e-5\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=WD)\n",
    "# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=100, cooldown=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f8Zn33m7CxL5"
   },
   "source": [
    "# Step 4: Train the Model for the first task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate():\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ))\n",
    "        output = model(batch_src, batch_padding_mask)\n",
    "        loss = criterion(output.squeeze(1), batch_labels)\n",
    "    return loss.item()\n",
    "\n",
    "# figure out if it's doing better on short paths\n",
    "def evaluate_short():\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ))\n",
    "        output = model(batch_src, batch_padding_mask)\n",
    "        loss = criterion(output[batch_labels == 1].squeeze(1), batch_labels[batch_labels==1])\n",
    "    return loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "execution_state": "idle",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "pvTfzGmCeXU4",
    "outputId": "0d3a20f3-23be-4c19-9eb6-46bfe11a48b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/functional.py:6278: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n",
      "  attn_output = scaled_dot_product_attention(\n",
      "/tmp/torchinductor_sipb/bn/cbngaobakjqlwlijvkqph5lgddb2z2kzjaln3b2g2j75b6snskdn.py:859: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n",
      "  extern_kernels.mm(reinterpret_tensor(buf1, (2097152, 64), (64, 1), 0), reinterpret_tensor(primals_5, (64, 192), (1, 64), 0), out=buf2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/100 \t Train Err: 87.5000 0.62109375 3.28125 8.125 222.0\n",
      "Epoch 0/100 \t Train Err: 70.5000 0.5078125 0.173828125 1.953125 182.0\n",
      "Epoch 0/100 \t Train Err: 59.7500 2.828125 0.4140625 0.134765625 154.0\n",
      "Epoch 0/100 \t Train Err: 54.0000 5.5 1.734375 0.1279296875 137.0\n",
      "Epoch 0/100 \t Train Err: 50.7500 7.9375 3.21875 0.6953125 126.0\n",
      "Epoch 0/100 \t Train Err: 48.5000 10.0 4.625 1.40625 118.0\n",
      "Epoch 0/100 \t Train Err: 46.7500 11.75 5.84375 2.109375 111.5\n",
      "Epoch 0/100 \t Train Err: 45.7500 13.125 6.90625 2.75 107.5\n",
      "Epoch 1/100 \t Train Err: 44.7500 14.25 7.75 3.28125 104.0\n",
      "Epoch 1/100 \t Train Err: 44.5000 15.1875 8.4375 3.71875 102.0\n",
      "Epoch 1/100 \t Train Err: 44.2500 15.875 9.0 4.09375 100.0\n",
      "Epoch 1/100 \t Train Err: 43.7500 16.5 9.4375 4.34375 98.5\n",
      "Epoch 1/100 \t Train Err: 43.7500 16.875 9.8125 4.59375 97.5\n",
      "Epoch 1/100 \t Train Err: 43.5000 17.25 10.1875 4.8125 96.5\n",
      "Epoch 1/100 \t Train Err: 43.2500 17.625 10.4375 5.0 95.0\n",
      "Epoch 1/100 \t Train Err: 43.2500 18.0 10.6875 5.1875 95.0\n",
      "Epoch 2/100 \t Train Err: 43.0000 18.5 11.0 5.34375 94.0\n",
      "Epoch 2/100 \t Train Err: 42.5000 18.75 11.25 5.5625 92.5\n",
      "Epoch 2/100 \t Train Err: 42.7500 19.125 11.5625 5.75 92.5\n",
      "Epoch 2/100 \t Train Err: 42.5000 19.5 11.8125 5.9375 91.5\n",
      "Epoch 2/100 \t Train Err: 42.0000 19.875 12.1875 6.1875 90.0\n",
      "Epoch 2/100 \t Train Err: 42.2500 20.25 12.5 6.40625 90.0\n",
      "Epoch 2/100 \t Train Err: 42.0000 20.625 12.6875 6.59375 89.0\n",
      "Epoch 2/100 \t Train Err: 41.7500 21.0 13.0625 6.84375 88.0\n",
      "Epoch 3/100 \t Train Err: 42.2500 21.375 13.375 7.0625 88.0\n",
      "Epoch 3/100 \t Train Err: 41.7500 21.75 13.6875 7.28125 86.0\n",
      "Epoch 3/100 \t Train Err: 41.5000 22.125 14.0 7.5625 85.5\n",
      "Epoch 3/100 \t Train Err: 41.7500 22.5 14.3125 7.75 85.5\n",
      "Epoch 3/100 \t Train Err: 41.2500 22.875 14.5625 7.9375 84.5\n",
      "Epoch 3/100 \t Train Err: 41.2500 23.25 14.875 8.1875 83.5\n",
      "Epoch 3/100 \t Train Err: 41.5000 23.5 15.1875 8.4375 83.5\n",
      "Epoch 3/100 \t Train Err: 41.2500 23.75 15.4375 8.625 82.0\n",
      "Epoch 4/100 \t Train Err: 41.0000 24.125 15.75 8.8125 81.0\n",
      "Epoch 4/100 \t Train Err: 40.7500 24.375 16.0 9.0625 81.0\n",
      "Epoch 4/100 \t Train Err: 40.7500 24.5 16.25 9.25 80.5\n",
      "Epoch 4/100 \t Train Err: 40.7500 24.625 16.5 9.4375 79.5\n",
      "Epoch 4/100 \t Train Err: 40.5000 24.75 16.75 9.625 79.0\n",
      "Epoch 4/100 \t Train Err: 40.5000 24.625 16.875 9.75 79.0\n",
      "Epoch 4/100 \t Train Err: 40.2500 24.375 17.125 9.875 78.5\n",
      "Epoch 4/100 \t Train Err: 40.0000 23.75 17.125 10.0 78.0\n",
      "Epoch 5/100 \t Train Err: 39.7500 23.0 17.125 10.0625 77.5\n",
      "Epoch 5/100 \t Train Err: 39.5000 21.5 17.0 10.0 78.0\n",
      "Epoch 5/100 \t Train Err: 38.7500 19.375 16.75 9.875 78.0\n",
      "Epoch 5/100 \t Train Err: 38.5000 16.5 16.25 9.6875 78.5\n",
      "Epoch 5/100 \t Train Err: 37.5000 12.9375 15.625 9.375 79.0\n",
      "Epoch 5/100 \t Train Err: 36.5000 8.875 14.9375 9.125 80.0\n",
      "Epoch 5/100 \t Train Err: 35.5000 5.09375 14.6875 9.25 79.5\n",
      "Epoch 5/100 \t Train Err: 34.5000 2.390625 15.5 10.0 78.0\n",
      "Epoch 6/100 \t Train Err: 33.5000 0.9140625 17.5 11.3125 75.0\n",
      "Epoch 6/100 \t Train Err: 33.0000 0.38671875 19.875 12.4375 72.5\n",
      "Epoch 6/100 \t Train Err: 32.7500 0.4921875 21.0 12.9375 71.5\n",
      "Epoch 6/100 \t Train Err: 33.0000 0.85546875 21.375 13.0 71.0\n",
      "Epoch 6/100 \t Train Err: 33.0000 1.1328125 21.5 13.125 70.5\n",
      "Epoch 6/100 \t Train Err: 32.7500 1.1875 21.875 13.4375 69.5\n",
      "Epoch 6/100 \t Train Err: 32.5000 1.0234375 22.5 13.9375 68.5\n",
      "Epoch 6/100 \t Train Err: 32.2500 0.73828125 23.125 14.5 67.5\n",
      "Epoch 7/100 \t Train Err: 31.8750 0.451171875 23.875 15.0625 66.0\n",
      "Epoch 7/100 \t Train Err: 31.6250 0.251953125 24.625 15.625 64.5\n",
      "Epoch 7/100 \t Train Err: 31.5000 0.2060546875 25.25 16.125 63.75\n",
      "Epoch 7/100 \t Train Err: 31.2500 0.2734375 25.625 16.5 63.0\n",
      "Epoch 7/100 \t Train Err: 31.1250 0.37109375 26.125 17.0 62.25\n",
      "Epoch 7/100 \t Train Err: 30.8750 0.400390625 26.625 17.25 61.5\n",
      "Epoch 7/100 \t Train Err: 30.8750 0.353515625 26.875 17.5 61.0\n",
      "Epoch 7/100 \t Train Err: 30.7500 0.275390625 27.25 17.75 60.5\n",
      "Epoch 8/100 \t Train Err: 30.6250 0.18359375 27.625 18.125 59.75\n",
      "Epoch 8/100 \t Train Err: 30.5000 0.10986328125 28.125 18.625 59.0\n",
      "Epoch 8/100 \t Train Err: 30.3750 0.06640625 28.625 19.0 58.5\n",
      "Epoch 8/100 \t Train Err: 30.3750 0.04931640625 29.125 19.375 57.75\n",
      "Epoch 8/100 \t Train Err: 30.1250 0.048583984375 29.75 19.875 57.0\n",
      "Epoch 8/100 \t Train Err: 30.0000 0.054443359375 30.25 20.25 56.0\n",
      "Epoch 8/100 \t Train Err: 29.8750 0.0576171875 30.875 20.875 55.25\n",
      "Epoch 8/100 \t Train Err: 29.8750 0.056884765625 31.5 21.25 54.5\n",
      "Epoch 9/100 \t Train Err: 29.7500 0.051025390625 32.0 21.75 53.75\n",
      "Epoch 9/100 \t Train Err: 29.5000 0.04296875 32.75 22.25 53.0\n",
      "Epoch 9/100 \t Train Err: 29.5000 0.03369140625 33.0 22.625 52.25\n",
      "Epoch 9/100 \t Train Err: 29.5000 0.0260009765625 33.75 23.125 51.75\n",
      "Epoch 9/100 \t Train Err: 29.3750 0.02197265625 34.25 23.5 51.25\n",
      "Epoch 9/100 \t Train Err: 29.3750 0.0216064453125 35.0 24.125 50.25\n",
      "Epoch 9/100 \t Train Err: 29.2500 0.0238037109375 35.25 24.375 50.0\n",
      "Epoch 9/100 \t Train Err: 29.1250 0.02734375 35.75 24.75 49.5\n",
      "Epoch 10/100 \t Train Err: 29.1250 0.0301513671875 36.0 25.0 49.0\n",
      "Epoch 10/100 \t Train Err: 29.1250 0.032470703125 36.75 25.625 48.25\n",
      "Epoch 10/100 \t Train Err: 29.0000 0.03271484375 37.25 26.125 47.5\n",
      "Epoch 10/100 \t Train Err: 28.8750 0.03125 37.5 26.25 47.25\n",
      "Epoch 10/100 \t Train Err: 29.0000 0.027587890625 38.0 26.75 46.5\n",
      "Epoch 10/100 \t Train Err: 28.8750 0.023193359375 38.25 26.875 46.5\n",
      "Epoch 10/100 \t Train Err: 28.8750 0.0196533203125 38.25 26.875 46.5\n",
      "Epoch 10/100 \t Train Err: 28.7500 0.0172119140625 38.75 27.375 45.75\n",
      "Epoch 11/100 \t Train Err: 28.7500 0.0166015625 39.0 27.5 45.5\n",
      "Epoch 11/100 \t Train Err: 28.8750 0.0169677734375 39.0 27.5 45.5\n",
      "Epoch 11/100 \t Train Err: 28.7500 0.0172119140625 39.0 27.5 45.5\n",
      "Epoch 11/100 \t Train Err: 28.7500 0.017578125 39.75 28.25 44.75\n",
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      "Epoch 12/100 \t Train Err: 28.7500 0.015869140625 40.0 28.5 44.25\n",
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      "Epoch 14/100 \t Train Err: 28.6250 0.01318359375 41.5 29.625 43.0\n",
      "Epoch 14/100 \t Train Err: 28.5000 0.012939453125 41.5 29.625 42.75\n",
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      "Epoch 15/100 \t Train Err: 28.5000 0.0111083984375 42.5 30.625 41.75\n",
      "Epoch 15/100 \t Train Err: 28.6250 0.010986328125 42.5 30.75 41.75\n",
      "Epoch 16/100 \t Train Err: 28.6250 0.01104736328125 42.75 30.875 41.5\n",
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      "Epoch 16/100 \t Train Err: 28.6250 0.01025390625 43.0 31.0 41.5\n",
      "Epoch 17/100 \t Train Err: 28.6250 0.0101318359375 43.0 31.0 41.5\n",
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      "Epoch 18/100 \t Train Err: 28.6250 0.00897216796875 43.0 31.0 41.25\n",
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      "Epoch 18/100 \t Train Err: 28.3750 0.00885009765625 43.0 31.0 41.25\n",
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      "Epoch 19/100 \t Train Err: 28.3750 0.0079345703125 43.0 31.125 41.25\n",
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      "Epoch 20/100 \t Train Err: 28.2500 0.006591796875 40.5 29.25 42.5\n",
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      "Epoch 20/100 \t Train Err: 27.2500 0.016357421875 30.125 21.5 48.5\n",
      "Epoch 21/100 \t Train Err: 27.5000 0.005279541015625 19.5 13.5 57.5\n",
      "Epoch 21/100 \t Train Err: 26.8750 0.00982666015625 28.875 20.875 48.25\n",
      "Epoch 21/100 \t Train Err: 26.7500 0.01019287109375 32.5 23.875 45.0\n",
      "Epoch 21/100 \t Train Err: 26.5000 0.0057373046875 27.75 20.625 47.5\n",
      "Epoch 21/100 \t Train Err: 26.5000 0.0111083984375 14.0 10.375 58.5\n",
      "Epoch 21/100 \t Train Err: 25.7500 0.007110595703125 27.625 21.875 45.0\n",
      "Epoch 21/100 \t Train Err: 25.3750 0.0081787109375 27.625 22.25 44.25\n",
      "Epoch 21/100 \t Train Err: 24.7500 0.0101318359375 11.4375 9.5 55.5\n",
      "Epoch 22/100 \t Train Err: 23.7500 0.0091552734375 14.8125 12.625 50.0\n",
      "Epoch 22/100 \t Train Err: 23.3750 0.0196533203125 18.5 16.5 45.5\n",
      "Epoch 22/100 \t Train Err: 22.8750 0.0205078125 9.5625 8.25 52.0\n",
      "Epoch 22/100 \t Train Err: 22.3750 0.045654296875 9.1875 7.90625 50.75\n",
      "Epoch 22/100 \t Train Err: 22.2500 0.1318359375 15.375 13.8125 45.0\n",
      "Epoch 22/100 \t Train Err: 21.6250 0.150390625 11.4375 9.5625 47.25\n",
      "Epoch 22/100 \t Train Err: 21.3750 0.126953125 8.4375 6.34375 49.75\n",
      "Epoch 22/100 \t Train Err: 20.8750 0.1455078125 11.0625 8.75 46.0\n",
      "Epoch 23/100 \t Train Err: 20.6250 0.125 13.6875 11.4375 43.0\n",
      "Epoch 23/100 \t Train Err: 20.3750 0.04931640625 11.625 9.625 44.0\n",
      "Epoch 23/100 \t Train Err: 20.0000 0.033935546875 9.3125 7.6875 45.25\n",
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      "Epoch 23/100 \t Train Err: 19.0000 0.1787109375 11.0 11.1875 40.25\n",
      "Epoch 23/100 \t Train Err: 18.7500 0.25 8.1875 8.9375 41.75\n",
      "Epoch 23/100 \t Train Err: 18.5000 0.2216796875 8.1875 9.9375 40.25\n",
      "Epoch 24/100 \t Train Err: 18.1250 0.1513671875 10.0625 13.4375 37.0\n",
      "Epoch 24/100 \t Train Err: 17.6250 0.12890625 7.6875 11.6875 37.75\n",
      "Epoch 24/100 \t Train Err: 17.3750 0.1201171875 6.28125 10.875 38.0\n",
      "Epoch 24/100 \t Train Err: 17.0000 0.126953125 7.53125 14.5625 35.0\n",
      "Epoch 24/100 \t Train Err: 16.7500 0.11181640625 7.3125 15.6875 33.75\n",
      "Epoch 24/100 \t Train Err: 16.5000 0.08203125 4.75 13.3125 35.5\n",
      "Epoch 24/100 \t Train Err: 16.3750 0.068359375 5.75 17.125 32.75\n",
      "Epoch 24/100 \t Train Err: 15.9375 0.057861328125 6.34375 19.25 30.75\n",
      "Epoch 25/100 \t Train Err: 15.7500 0.051025390625 3.578125 14.5 33.5\n",
      "Epoch 25/100 \t Train Err: 15.2500 0.04248046875 5.0 18.625 29.625\n",
      "Epoch 25/100 \t Train Err: 15.0000 0.040771484375 5.53125 21.125 27.875\n",
      "Epoch 25/100 \t Train Err: 14.8125 0.033935546875 3.171875 16.0 30.375\n",
      "Epoch 25/100 \t Train Err: 14.6250 0.0322265625 3.734375 18.875 28.5\n",
      "Epoch 25/100 \t Train Err: 14.3750 0.03369140625 5.09375 23.0 25.5\n",
      "Epoch 25/100 \t Train Err: 14.1250 0.028076171875 2.046875 14.3125 30.125\n",
      "Epoch 25/100 \t Train Err: 13.8125 0.023681640625 3.234375 19.375 26.625\n",
      "Epoch 26/100 \t Train Err: 13.6875 0.023681640625 4.75 24.875 23.125\n",
      "Epoch 26/100 \t Train Err: 13.5625 0.0245361328125 1.515625 13.6875 29.0\n",
      "Epoch 26/100 \t Train Err: 13.0625 0.0179443359375 2.875 20.5 24.25\n",
      "Epoch 26/100 \t Train Err: 13.0000 0.016845703125 3.5 24.0 22.25\n",
      "Epoch 26/100 \t Train Err: 12.8750 0.02197265625 1.46875 15.625 26.5\n",
      "Epoch 26/100 \t Train Err: 12.5000 0.0174560546875 2.03125 19.5 23.5\n",
      "Epoch 26/100 \t Train Err: 12.4375 0.014404296875 3.0 24.75 20.625\n",
      "Epoch 26/100 \t Train Err: 12.1250 0.0230712890625 1.46875 17.625 23.875\n",
      "Epoch 27/100 \t Train Err: 11.9375 0.022705078125 1.421875 17.75 23.125\n",
      "Epoch 27/100 \t Train Err: 11.7500 0.0150146484375 2.09375 22.625 20.0\n",
      "Epoch 27/100 \t Train Err: 11.6250 0.01531982421875 1.6796875 20.875 20.75\n",
      "Epoch 27/100 \t Train Err: 11.3750 0.0177001953125 1.0546875 17.25 22.0\n",
      "Epoch 27/100 \t Train Err: 11.0625 0.0128173828125 1.359375 20.375 19.875\n",
      "Epoch 27/100 \t Train Err: 11.0000 0.0128173828125 1.5078125 22.0 18.875\n",
      "Epoch 27/100 \t Train Err: 10.8125 0.01190185546875 1.03125 18.125 20.125\n",
      "Epoch 27/100 \t Train Err: 10.7500 0.01165771484375 0.99609375 18.125 20.25\n",
      "Epoch 28/100 \t Train Err: 10.5625 0.012451171875 1.328125 21.125 18.125\n",
      "Epoch 28/100 \t Train Err: 10.3750 0.01104736328125 1.15625 19.375 18.625\n",
      "Epoch 28/100 \t Train Err: 10.3125 0.01025390625 0.953125 17.25 19.5\n",
      "Epoch 28/100 \t Train Err: 10.1250 0.010498046875 1.171875 19.875 17.75\n",
      "Epoch 28/100 \t Train Err: 10.0625 0.0101318359375 1.109375 20.0 17.5\n",
      "Epoch 28/100 \t Train Err: 10.0000 0.0111083984375 0.7578125 16.75 18.875\n",
      "Epoch 28/100 \t Train Err: 9.8125 0.0093994140625 0.87109375 18.375 17.5\n",
      "Epoch 28/100 \t Train Err: 9.7500 0.01043701171875 1.0390625 20.625 16.375\n",
      "Epoch 29/100 \t Train Err: 9.4375 0.00921630859375 0.828125 18.5 16.75\n",
      "Epoch 29/100 \t Train Err: 9.5000 0.00836181640625 0.59375 16.5 17.75\n",
      "Epoch 29/100 \t Train Err: 9.4375 0.0115966796875 0.796875 20.375 15.8125\n",
      "Epoch 29/100 \t Train Err: 9.3125 0.010986328125 0.72265625 20.25 15.625\n",
      "Epoch 29/100 \t Train Err: 9.1875 0.00762939453125 0.51953125 16.75 16.875\n",
      "Epoch 29/100 \t Train Err: 9.0625 0.00799560546875 0.56640625 18.375 15.8125\n",
      "Epoch 29/100 \t Train Err: 9.0625 0.00946044921875 0.66796875 20.625 14.625\n",
      "Epoch 29/100 \t Train Err: 9.0000 0.00665283203125 0.46484375 16.125 16.5\n",
      "Epoch 30/100 \t Train Err: 8.8750 0.008056640625 0.5234375 18.25 15.3125\n",
      "Epoch 30/100 \t Train Err: 8.7500 0.0111083984375 0.59375 20.0 14.1875\n",
      "Epoch 36/100 \t Train Err: 7.3750 0.00799560546875 0.302734375 13.5 12.9375\n",
      "Epoch 36/100 \t Train Err: 7.2188 0.00799560546875 0.369140625 15.5625 11.375\n",
      "Epoch 36/100 \t Train Err: 7.2188 0.00823974609375 0.4296875 17.375 10.375\n",
      "Epoch 36/100 \t Train Err: 7.2500 0.00860595703125 0.412109375 18.0 10.125\n",
      "Epoch 36/100 \t Train Err: 7.1875 0.01171875 0.33984375 15.625 11.1875\n",
      "Epoch 36/100 \t Train Err: 7.0625 0.0177001953125 0.2890625 12.875 12.1875\n",
      "Epoch 36/100 \t Train Err: 7.1562 0.01806640625 0.271484375 11.8125 13.0\n",
      "Epoch 36/100 \t Train Err: 7.1875 0.0120849609375 0.24609375 11.5625 13.0625\n",
      "Epoch 37/100 \t Train Err: 7.0625 0.007171630859375 0.2431640625 12.375 12.3125\n",
      "Epoch 37/100 \t Train Err: 7.0625 0.0101318359375 0.2490234375 14.0 11.5\n",
      "Epoch 37/100 \t Train Err: 7.0625 0.0181884765625 0.28125 15.4375 10.875\n",
      "Epoch 37/100 \t Train Err: 7.0938 0.0244140625 0.287109375 15.8125 10.6875\n",
      "Epoch 37/100 \t Train Err: 6.9375 0.0230712890625 0.27734375 15.1875 10.625\n",
      "Epoch 37/100 \t Train Err: 6.8750 0.01556396484375 0.255859375 13.5625 11.4375\n",
      "Epoch 37/100 \t Train Err: 6.9375 0.0091552734375 0.220703125 12.4375 12.0625\n",
      "Epoch 37/100 \t Train Err: 6.9375 0.006011962890625 0.2158203125 12.4375 12.0625\n",
      "Epoch 38/100 \t Train Err: 6.8438 0.004791259765625 0.232421875 12.9375 11.625\n",
      "Epoch 38/100 \t Train Err: 6.8750 0.004486083984375 0.2421875 14.3125 10.875\n",
      "Epoch 38/100 \t Train Err: 6.8438 0.00433349609375 0.28125 15.0625 10.1875\n",
      "Epoch 38/100 \t Train Err: 6.9375 0.004241943359375 0.25 14.9375 10.4375\n",
      "Epoch 38/100 \t Train Err: 6.7500 0.004241943359375 0.23828125 13.75 10.625\n",
      "Epoch 38/100 \t Train Err: 6.7812 0.0042724609375 0.2109375 12.25 11.5625\n",
      "Epoch 38/100 \t Train Err: 6.8438 0.003692626953125 0.1943359375 11.9375 11.875\n",
      "Epoch 38/100 \t Train Err: 6.6875 0.004119873046875 0.197265625 11.5 11.5\n",
      "Epoch 39/100 \t Train Err: 6.6562 0.007720947265625 0.193359375 12.1875 11.0625\n",
      "Epoch 39/100 \t Train Err: 6.6250 0.01318359375 0.2080078125 13.25 10.4375\n",
      "Epoch 39/100 \t Train Err: 6.6562 0.016357421875 0.224609375 13.9375 10.3125\n",
      "Epoch 39/100 \t Train Err: 6.6562 0.0159912109375 0.2021484375 13.75 10.375\n",
      "Epoch 39/100 \t Train Err: 6.5312 0.0126953125 0.19140625 12.9375 10.5\n",
      "Epoch 39/100 \t Train Err: 6.5938 0.0081787109375 0.1796875 11.9375 11.0625\n",
      "Epoch 39/100 \t Train Err: 6.6250 0.005401611328125 0.1796875 11.875 11.375\n",
      "Epoch 39/100 \t Train Err: 6.5000 0.0040283203125 0.1787109375 12.125 10.9375\n",
      "Epoch 40/100 \t Train Err: 6.5312 0.0031890869140625 0.1962890625 12.8125 10.5625\n",
      "Epoch 40/100 \t Train Err: 6.5625 0.0029296875 0.2080078125 13.25 10.3125\n",
      "Epoch 40/100 \t Train Err: 6.5625 0.0026702880859375 0.189453125 13.5 10.25\n",
      "Epoch 40/100 \t Train Err: 6.5312 0.002685546875 0.177734375 12.5625 10.4375\n",
      "Epoch 40/100 \t Train Err: 6.4375 0.0027008056640625 0.169921875 11.625 10.8125\n",
      "Epoch 40/100 \t Train Err: 6.5000 0.0026092529296875 0.1630859375 11.6875 11.0625\n",
      "Epoch 40/100 \t Train Err: 6.5000 0.0030670166015625 0.162109375 11.9375 10.875\n",
      "Epoch 40/100 \t Train Err: 6.5000 0.004486083984375 0.1630859375 12.4375 10.5625\n",
      "Epoch 41/100 \t Train Err: 6.4375 0.006011962890625 0.1875 13.3125 9.9375\n",
      "Epoch 41/100 \t Train Err: 6.4688 0.005706787109375 0.1708984375 12.75 10.25\n",
      "Epoch 41/100 \t Train Err: 6.4688 0.00445556640625 0.15234375 12.25 10.625\n",
      "Epoch 41/100 \t Train Err: 6.4688 0.0032501220703125 0.166015625 11.8125 10.875\n",
      "Epoch 41/100 \t Train Err: 6.3750 0.0027008056640625 0.166015625 12.0625 10.5\n",
      "Epoch 41/100 \t Train Err: 6.3125 0.0023040771484375 0.158203125 12.0 10.25\n",
      "Epoch 41/100 \t Train Err: 6.4062 0.002227783203125 0.1640625 12.4375 10.125\n",
      "Epoch 41/100 \t Train Err: 6.3438 0.002227783203125 0.171875 12.6875 9.9375\n",
      "Epoch 42/100 \t Train Err: 6.3125 0.002197265625 0.1591796875 12.0625 10.1875\n",
      "Epoch 42/100 \t Train Err: 6.2500 0.0021209716796875 0.1513671875 11.4375 10.3125\n",
      "Epoch 42/100 \t Train Err: 6.2812 0.0022430419921875 0.1396484375 11.5 10.5\n",
      "Epoch 42/100 \t Train Err: 6.1875 0.002838134765625 0.146484375 11.8125 9.9375\n",
      "Epoch 42/100 \t Train Err: 6.3125 0.0037078857421875 0.150390625 12.125 10.0625\n",
      "Epoch 42/100 \t Train Err: 6.2812 0.004425048828125 0.1591796875 12.375 9.875\n",
      "Epoch 42/100 \t Train Err: 6.2188 0.004150390625 0.1357421875 11.625 10.0625\n",
      "Epoch 42/100 \t Train Err: 6.2188 0.0035858154296875 0.1416015625 11.4375 10.25\n",
      "Epoch 43/100 \t Train Err: 6.2500 0.0028839111328125 0.1328125 11.1875 10.4375\n",
      "Epoch 43/100 \t Train Err: 6.1562 0.0025482177734375 0.13671875 11.1875 10.125\n",
      "Epoch 43/100 \t Train Err: 6.0938 0.002227783203125 0.142578125 11.625 9.75\n",
      "Epoch 43/100 \t Train Err: 6.1875 0.002105712890625 0.1435546875 12.0625 9.8125\n",
      "Epoch 43/100 \t Train Err: 6.2812 0.001983642578125 0.150390625 12.125 9.9375\n",
      "Epoch 43/100 \t Train Err: 6.0938 0.0019683837890625 0.1396484375 11.5 9.8125\n",
      "Epoch 43/100 \t Train Err: 6.2188 0.00191497802734375 0.1337890625 11.5 10.125\n",
      "Epoch 43/100 \t Train Err: 6.0938 0.00201416015625 0.1337890625 11.4375 9.875\n",
      "Epoch 44/100 \t Train Err: 6.0938 0.0023040771484375 0.140625 11.375 9.9375\n",
      "Epoch 44/100 \t Train Err: 6.0938 0.002960205078125 0.1298828125 11.125 10.0\n",
      "Epoch 44/100 \t Train Err: 6.1562 0.003662109375 0.1357421875 11.375 10.0\n",
      "Epoch 44/100 \t Train Err: 6.0625 0.003997802734375 0.130859375 11.4375 9.75\n",
      "Epoch 44/100 \t Train Err: 6.0312 0.003997802734375 0.134765625 11.4375 9.5625\n",
      "Epoch 44/100 \t Train Err: 6.0000 0.003265380859375 0.1337890625 11.4375 9.625\n",
      "Epoch 44/100 \t Train Err: 6.0000 0.0024871826171875 0.1337890625 11.5 9.625\n",
      "Epoch 44/100 \t Train Err: 6.0312 0.0020904541015625 0.1376953125 11.0625 9.8125\n",
      "Epoch 45/100 \t Train Err: 5.9688 0.0020294189453125 0.125 10.8125 9.6875\n",
      "Epoch 45/100 \t Train Err: 6.0000 0.0019683837890625 0.1318359375 10.75 9.75\n",
      "Epoch 45/100 \t Train Err: 6.0312 0.002044677734375 0.12890625 11.0625 9.6875\n",
      "Epoch 45/100 \t Train Err: 5.9688 0.002197265625 0.12353515625 11.0625 9.6875\n",
      "Epoch 45/100 \t Train Err: 5.8750 0.0026397705078125 0.1318359375 11.3125 9.375\n",
      "Epoch 45/100 \t Train Err: 5.9375 0.003204345703125 0.1201171875 11.25 9.4375\n",
      "Epoch 45/100 \t Train Err: 5.8125 0.003326416015625 0.115234375 11.0 9.375\n",
      "Epoch 45/100 \t Train Err: 5.9062 0.0030975341796875 0.111328125 11.0 9.5625\n",
      "Epoch 46/100 \t Train Err: 5.9062 0.0026702880859375 0.10498046875 10.5 9.75\n",
      "Epoch 46/100 \t Train Err: 5.8125 0.0024566650390625 0.1044921875 10.375 9.625\n",
      "Epoch 46/100 \t Train Err: 5.8438 0.0024566650390625 0.11474609375 10.875 9.5\n",
      "Epoch 46/100 \t Train Err: 5.8438 0.0023956298828125 0.11962890625 11.375 9.1875\n",
      "Epoch 46/100 \t Train Err: 5.7812 0.0023651123046875 0.12060546875 11.125 9.125\n",
      "Epoch 46/100 \t Train Err: 5.9062 0.0023193359375 0.11767578125 10.875 9.5625\n",
      "Epoch 46/100 \t Train Err: 5.7500 0.002349853515625 0.09912109375 10.25 9.5625\n",
      "Epoch 46/100 \t Train Err: 5.7812 0.0024871826171875 0.10986328125 9.9375 9.8125\n",
      "Epoch 47/100 \t Train Err: 5.7812 0.002960205078125 0.107421875 10.5 9.375\n",
      "Epoch 47/100 \t Train Err: 5.7812 0.0032501220703125 0.1123046875 10.875 9.1875\n",
      "Epoch 47/100 \t Train Err: 5.7188 0.0033111572265625 0.11767578125 10.5625 9.125\n",
      "Epoch 47/100 \t Train Err: 5.7812 0.0030517578125 0.10986328125 10.4375 9.5\n",
      "Epoch 47/100 \t Train Err: 5.6562 0.002899169921875 0.1181640625 10.625 9.0\n",
      "Epoch 47/100 \t Train Err: 5.6562 0.0026702880859375 0.12109375 11.0 8.875\n",
      "Epoch 47/100 \t Train Err: 5.7812 0.00262451171875 0.10302734375 10.4375 9.375\n",
      "Epoch 47/100 \t Train Err: 5.7812 0.0026702880859375 0.1015625 9.8125 9.75\n",
      "Epoch 48/100 \t Train Err: 5.7188 0.002655029296875 0.09814453125 9.9375 9.4375\n",
      "Epoch 48/100 \t Train Err: 5.6562 0.003143310546875 0.111328125 10.75 8.875\n",
      "Epoch 48/100 \t Train Err: 5.5625 0.00335693359375 0.111328125 10.6875 8.75\n",
      "Epoch 48/100 \t Train Err: 5.5625 0.003326416015625 0.1044921875 9.8125 9.1875\n",
      "Epoch 48/100 \t Train Err: 5.6562 0.003265380859375 0.099609375 10.125 9.25\n",
      "Epoch 48/100 \t Train Err: 5.6875 0.0030670166015625 0.10888671875 10.875 8.875\n",
      "Epoch 48/100 \t Train Err: 5.5938 0.0027923583984375 0.09619140625 10.25 8.9375\n",
      "Epoch 48/100 \t Train Err: 5.5938 0.0027313232421875 0.10400390625 9.4375 9.4375\n",
      "Epoch 49/100 \t Train Err: 5.5312 0.002777099609375 0.09326171875 10.0 8.9375\n",
      "Epoch 49/100 \t Train Err: 5.5938 0.0031890869140625 0.1015625 10.9375 8.5\n",
      "Epoch 49/100 \t Train Err: 5.5000 0.00341796875 0.08984375 10.25 8.8125\n",
      "Epoch 49/100 \t Train Err: 5.5938 0.003662109375 0.07666015625 8.8125 9.75\n",
      "Epoch 49/100 \t Train Err: 5.5312 0.004547119140625 0.09375 9.6875 9.0625\n",
      "Epoch 49/100 \t Train Err: 5.5000 0.004638671875 0.1103515625 11.125 8.125\n",
      "Epoch 49/100 \t Train Err: 5.4375 0.0031890869140625 0.08447265625 8.875 9.3125\n",
      "Epoch 49/100 \t Train Err: 5.5312 0.0031890869140625 0.0908203125 9.3125 9.375\n",
      "Epoch 50/100 \t Train Err: 5.5625 0.003265380859375 0.10693359375 11.75 7.625\n",
      "Epoch 50/100 \t Train Err: 5.5000 0.00341796875 0.07763671875 7.5625 10.1875\n",
      "Epoch 50/100 \t Train Err: 5.3750 0.00323486328125 0.08056640625 8.8125 9.125\n",
      "Epoch 50/100 \t Train Err: 5.4688 0.00433349609375 0.11767578125 11.875 7.46875\n",
      "Epoch 50/100 \t Train Err: 5.5312 0.00433349609375 0.09130859375 8.625 9.75\n",
      "Epoch 50/100 \t Train Err: 5.4062 0.0047607421875 0.087890625 9.3125 9.0\n",
      "Epoch 50/100 \t Train Err: 5.4375 0.00457763671875 0.11767578125 11.875 7.4375\n",
      "Epoch 50/100 \t Train Err: 5.5312 0.003387451171875 0.06640625 6.75 10.8125\n",
      "Epoch 51/100 \t Train Err: 5.4062 0.003448486328125 0.08984375 9.5 8.75\n",
      "Epoch 51/100 \t Train Err: 5.4062 0.003662109375 0.11279296875 12.0 7.40625\n",
      "Epoch 51/100 \t Train Err: 5.3125 0.003692626953125 0.087890625 8.4375 9.1875\n",
      "Epoch 51/100 \t Train Err: 5.3750 0.003692626953125 0.08349609375 8.5625 9.3125\n",
      "Epoch 51/100 \t Train Err: 5.3125 0.0037994384765625 0.09765625 10.8125 7.9375\n",
      "Epoch 51/100 \t Train Err: 5.3750 0.0036773681640625 0.0791015625 9.6875 8.4375\n",
      "Epoch 51/100 \t Train Err: 5.3125 0.0037078857421875 0.06787109375 8.375 9.25\n",
      "Epoch 51/100 \t Train Err: 5.2500 0.003936767578125 0.076171875 9.25 8.625\n",
      "Epoch 52/100 \t Train Err: 5.3125 0.00433349609375 0.08349609375 10.375 8.0\n",
      "Epoch 52/100 \t Train Err: 5.1875 0.00457763671875 0.0732421875 8.8125 8.6875\n",
      "Epoch 52/100 \t Train Err: 5.2188 0.0047607421875 0.06787109375 8.25 8.9375\n",
      "Epoch 52/100 \t Train Err: 5.2812 0.00518798828125 0.08056640625 9.5 8.3125\n",
      "Epoch 52/100 \t Train Err: 5.0938 0.0047607421875 0.08154296875 9.5 7.9375\n",
      "Epoch 52/100 \t Train Err: 5.2188 0.00396728515625 0.06591796875 8.0625 9.1875\n",
      "Epoch 52/100 \t Train Err: 5.1250 0.004180908203125 0.07421875 9.5 8.0\n",
      "Epoch 52/100 \t Train Err: 5.1250 0.004150390625 0.078125 9.5 8.0625\n",
      "Epoch 53/100 \t Train Err: 5.1562 0.004180908203125 0.064453125 8.0 8.875\n",
      "Epoch 53/100 \t Train Err: 5.0312 0.004608154296875 0.0703125 8.3125 8.4375\n",
      "Epoch 53/100 \t Train Err: 5.0625 0.00531005859375 0.07861328125 8.625 8.1875\n",
      "Epoch 53/100 \t Train Err: 5.0312 0.005340576171875 0.07763671875 9.0 8.0625\n",
      "Epoch 53/100 \t Train Err: 5.0312 0.004791259765625 0.07421875 8.4375 8.25\n",
      "Epoch 53/100 \t Train Err: 5.0312 0.00445556640625 0.0673828125 8.3125 8.375\n",
      "Epoch 53/100 \t Train Err: 4.9688 0.004486083984375 0.06591796875 8.625 8.0\n",
      "Epoch 53/100 \t Train Err: 5.0312 0.004486083984375 0.06396484375 8.25 8.4375\n",
      "Epoch 54/100 \t Train Err: 4.9688 0.004425048828125 0.06689453125 8.3125 8.3125\n",
      "Epoch 54/100 \t Train Err: 5.0000 0.00457763671875 0.07470703125 8.75 7.96875\n",
      "Epoch 54/100 \t Train Err: 4.9375 0.004669189453125 0.07080078125 7.96875 8.25\n",
      "Epoch 54/100 \t Train Err: 4.8750 0.004852294921875 0.07275390625 7.90625 8.1875\n",
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      "Epoch 54/100 \t Train Err: 4.8438 0.004608154296875 0.0791015625 8.625 7.5\n",
      "Epoch 55/100 \t Train Err: 4.9062 0.0045166015625 0.0693359375 7.3125 8.5625\n",
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      "Epoch 55/100 \t Train Err: 4.8750 0.00531005859375 0.0849609375 8.6875 7.6875\n",
      "Epoch 55/100 \t Train Err: 4.7812 0.004638671875 0.06494140625 6.8125 8.5625\n",
      "Epoch 55/100 \t Train Err: 4.8438 0.004791259765625 0.08203125 9.0 7.1875\n",
      "Epoch 55/100 \t Train Err: 4.9688 0.004241943359375 0.055419921875 5.40625 10.0\n",
      "Epoch 55/100 \t Train Err: 6.1562 0.005401611328125 0.19140625 20.125 4.1875\n",
      "Epoch 55/100 \t Train Err: 14.0000 0.005706787109375 0.107421875 0.53515625 36.0\n",
      "Epoch 56/100 \t Train Err: 11.1875 0.314453125 0.1689453125 0.56640625 29.0\n",
      "Epoch 56/100 \t Train Err: 7.6250 1.75 3.984375 17.0 9.1875\n",
      "Epoch 56/100 \t Train Err: 12.3125 2.03125 11.1875 47.25 3.21875\n",
      "Epoch 56/100 \t Train Err: 7.0625 0.88671875 1.78125 10.1875 12.5625\n",
      "Epoch 56/100 \t Train Err: 8.7500 0.05078125 0.1953125 2.484375 21.875\n",
      "Epoch 56/100 \t Train Err: 8.8750 0.1328125 0.0419921875 2.5625 22.0\n",
      "Epoch 56/100 \t Train Err: 6.6562 0.32421875 0.0888671875 9.6875 12.8125\n",
      "Epoch 56/100 \t Train Err: 7.5000 0.349609375 0.51171875 28.375 5.90625\n",
      "Epoch 57/100 \t Train Err: 8.4375 0.337890625 0.66796875 35.75 4.53125\n",
      "Epoch 57/100 \t Train Err: 7.1562 0.3125 0.265625 25.375 6.375\n",
      "Epoch 57/100 \t Train Err: 6.3750 0.259765625 0.095703125 10.75 11.5\n",
      "Epoch 57/100 \t Train Err: 7.0625 0.125 0.08837890625 5.75 15.75\n",
      "Epoch 57/100 \t Train Err: 7.1562 0.022705078125 0.06005859375 5.75 16.0\n",
      "Epoch 57/100 \t Train Err: 6.5625 0.1787109375 0.2412109375 8.625 13.0625\n",
      "Epoch 57/100 \t Train Err: 6.4375 0.443359375 0.59765625 14.5 9.375\n",
      "Epoch 57/100 \t Train Err: 6.5625 0.408203125 0.6484375 18.875 7.34375\n",
      "Epoch 58/100 \t Train Err: 6.4062 0.150390625 0.337890625 19.25 6.9375\n",
      "Epoch 58/100 \t Train Err: 6.1875 0.0218505859375 0.11865234375 16.5 7.65625\n",
      "Epoch 58/100 \t Train Err: 5.9688 0.1796875 0.30078125 11.4375 9.4375\n",
      "Epoch 58/100 \t Train Err: 6.1562 0.35546875 0.5703125 8.625 11.0625\n",
      "Epoch 58/100 \t Train Err: 6.0312 0.31640625 0.55859375 8.125 11.375\n",
      "Epoch 58/100 \t Train Err: 5.8125 0.130859375 0.333984375 9.375 10.125\n",
      "Epoch 58/100 \t Train Err: 5.5938 0.01422119140625 0.138671875 12.125 8.4375\n",
      "Epoch 58/100 \t Train Err: 5.6562 0.06884765625 0.1396484375 14.4375 7.28125\n",
      "Epoch 59/100 \t Train Err: 5.6875 0.1884765625 0.20703125 14.5625 7.28125\n",
      "Epoch 59/100 \t Train Err: 5.6562 0.24609375 0.216796875 12.9375 7.96875\n",
      "Epoch 59/100 \t Train Err: 5.4688 0.2109375 0.162109375 10.3125 9.0\n",
      "Epoch 59/100 \t Train Err: 5.4688 0.134765625 0.10888671875 8.6875 9.9375\n",
      "Epoch 59/100 \t Train Err: 5.4375 0.06689453125 0.0966796875 8.125 10.25\n",
      "Epoch 59/100 \t Train Err: 5.2812 0.0262451171875 0.11279296875 8.3125 9.6875\n",
      "Epoch 59/100 \t Train Err: 5.2812 0.0106201171875 0.1416015625 10.375 8.5\n",
      "Epoch 59/100 \t Train Err: 5.3125 0.0084228515625 0.177734375 12.5 7.46875\n",
      "Epoch 60/100 \t Train Err: 5.3125 0.0126953125 0.1875 12.1875 7.28125\n",
      "Epoch 60/100 \t Train Err: 5.2188 0.019775390625 0.1982421875 11.1875 7.5625\n",
      "Epoch 60/100 \t Train Err: 5.1250 0.0240478515625 0.203125 9.3125 8.375\n",
      "Epoch 60/100 \t Train Err: 5.1250 0.019775390625 0.1875 8.25 9.1875\n",
      "Epoch 60/100 \t Train Err: 5.1250 0.010986328125 0.1572265625 7.71875 9.1875\n",
      "Epoch 60/100 \t Train Err: 5.0312 0.007171630859375 0.1259765625 8.375 8.625\n",
      "Epoch 60/100 \t Train Err: 4.9688 0.0150146484375 0.10400390625 9.125 8.0\n",
      "Epoch 60/100 \t Train Err: 4.9375 0.0308837890625 0.09033203125 9.625 7.625\n",
      "Epoch 61/100 \t Train Err: 4.9688 0.046630859375 0.08447265625 9.5625 7.65625\n",
      "Epoch 61/100 \t Train Err: 4.9688 0.0546875 0.08154296875 9.125 7.875\n",
      "Epoch 61/100 \t Train Err: 4.9688 0.054443359375 0.0712890625 8.1875 8.5\n",
      "Epoch 61/100 \t Train Err: 4.9062 0.049072265625 0.07275390625 7.875 8.4375\n",
      "Epoch 61/100 \t Train Err: 4.8125 0.040771484375 0.0693359375 7.71875 8.25\n",
      "Epoch 61/100 \t Train Err: 4.8750 0.031494140625 0.06884765625 7.9375 8.4375\n",
      "Epoch 61/100 \t Train Err: 4.8125 0.0228271484375 0.072265625 8.5625 7.8125\n",
      "Epoch 61/100 \t Train Err: 4.8125 0.01611328125 0.07568359375 9.0 7.59375\n",
      "Epoch 62/100 \t Train Err: 4.7500 0.010986328125 0.07861328125 8.5625 7.625\n",
      "Epoch 62/100 \t Train Err: 4.6875 0.0079345703125 0.08203125 7.9375 7.625\n",
      "Epoch 62/100 \t Train Err: 4.7500 0.00665283203125 0.0810546875 7.5 8.3125\n",
      "Epoch 62/100 \t Train Err: 4.7188 0.00634765625 0.07861328125 7.09375 8.3125\n",
      "Epoch 62/100 \t Train Err: 4.6562 0.006317138671875 0.08349609375 7.40625 8.0\n",
      "Epoch 62/100 \t Train Err: 4.6562 0.006500244140625 0.0791015625 7.9375 7.625\n",
      "Epoch 62/100 \t Train Err: 4.7188 0.006500244140625 0.0732421875 8.1875 7.6875\n",
      "Epoch 62/100 \t Train Err: 4.6875 0.00677490234375 0.0771484375 8.5 7.53125\n",
      "Epoch 63/100 \t Train Err: 4.6875 0.00732421875 0.07421875 7.9375 7.78125\n",
      "Epoch 63/100 \t Train Err: 4.6562 0.00836181640625 0.0703125 7.84375 7.8125\n",
      "Epoch 63/100 \t Train Err: 4.6875 0.0093994140625 0.0654296875 7.65625 7.90625\n",
      "Epoch 63/100 \t Train Err: 4.6875 0.010498046875 0.0654296875 7.75 7.96875\n",
      "Epoch 63/100 \t Train Err: 4.6250 0.01116943359375 0.0634765625 7.6875 7.71875\n",
      "Epoch 63/100 \t Train Err: 4.6562 0.01153564453125 0.064453125 7.75 7.78125\n",
      "Epoch 63/100 \t Train Err: 4.6562 0.0118408203125 0.060302734375 7.875 7.78125\n",
      "Epoch 63/100 \t Train Err: 4.5938 0.01171875 0.0634765625 7.625 7.71875\n",
      "Epoch 64/100 \t Train Err: 4.6562 0.010986328125 0.060791015625 7.53125 7.90625\n",
      "Epoch 64/100 \t Train Err: 4.5312 0.01019287109375 0.05908203125 7.15625 7.6875\n",
      "Epoch 64/100 \t Train Err: 4.5312 0.009033203125 0.064453125 7.28125 7.75\n",
      "Epoch 64/100 \t Train Err: 4.5625 0.00836181640625 0.06298828125 7.5 7.75\n",
      "Epoch 64/100 \t Train Err: 4.5625 0.007598876953125 0.059326171875 7.5625 7.6875\n",
      "Epoch 64/100 \t Train Err: 4.5938 0.007110595703125 0.0654296875 7.5625 7.75\n",
      "Epoch 64/100 \t Train Err: 4.5312 0.00689697265625 0.06396484375 7.53125 7.625\n",
      "Epoch 64/100 \t Train Err: 4.5625 0.006591796875 0.06494140625 7.59375 7.59375\n",
      "Epoch 65/100 \t Train Err: 4.5000 0.0064697265625 0.061767578125 7.40625 7.625\n",
      "Epoch 65/100 \t Train Err: 4.5938 0.006439208984375 0.061767578125 7.625 7.71875\n",
      "Epoch 65/100 \t Train Err: 4.5625 0.006500244140625 0.0615234375 7.5 7.6875\n",
      "Epoch 65/100 \t Train Err: 4.5625 0.0064697265625 0.055908203125 7.25 7.75\n",
      "Epoch 65/100 \t Train Err: 4.5625 0.00640869140625 0.056640625 7.34375 7.75\n",
      "Epoch 65/100 \t Train Err: 4.4688 0.0064697265625 0.0625 7.375 7.5625\n",
      "Epoch 65/100 \t Train Err: 4.4688 0.00640869140625 0.060302734375 7.34375 7.53125\n",
      "Epoch 65/100 \t Train Err: 4.4375 0.00640869140625 0.059814453125 7.03125 7.6875\n",
      "Epoch 66/100 \t Train Err: 4.5000 0.00628662109375 0.06005859375 6.96875 7.71875\n",
      "Epoch 66/100 \t Train Err: 4.5312 0.006622314453125 0.058349609375 7.125 7.71875\n",
      "Epoch 66/100 \t Train Err: 4.4688 0.006500244140625 0.05908203125 7.34375 7.53125\n",
      "Epoch 66/100 \t Train Err: 4.5000 0.006683349609375 0.06298828125 7.46875 7.4375\n",
      "Epoch 66/100 \t Train Err: 4.3750 0.006744384765625 0.057861328125 7.0625 7.3125\n",
      "Epoch 66/100 \t Train Err: 4.4688 0.006683349609375 0.060791015625 7.125 7.5625\n",
      "Epoch 66/100 \t Train Err: 4.5000 0.006744384765625 0.060302734375 7.25 7.65625\n",
      "Epoch 66/100 \t Train Err: 4.5000 0.006744384765625 0.056884765625 7.125 7.625\n",
      "Epoch 67/100 \t Train Err: 4.4688 0.006683349609375 0.05712890625 6.96875 7.65625\n",
      "Epoch 67/100 \t Train Err: 4.3750 0.006683349609375 0.060546875 6.875 7.4375\n",
      "Epoch 67/100 \t Train Err: 4.3438 0.0067138671875 0.06298828125 7.21875 7.25\n",
      "Epoch 67/100 \t Train Err: 4.4688 0.00665283203125 0.06201171875 7.25 7.5625\n",
      "Epoch 67/100 \t Train Err: 4.4375 0.00628662109375 0.057861328125 6.96875 7.5625\n",
      "Epoch 67/100 \t Train Err: 4.2500 0.00640869140625 0.055419921875 6.53125 7.4375\n",
      "Epoch 67/100 \t Train Err: 4.3750 0.0062255859375 0.051513671875 6.96875 7.46875\n",
      "Epoch 67/100 \t Train Err: 4.4062 0.006195068359375 0.055419921875 6.96875 7.46875\n",
      "Epoch 68/100 \t Train Err: 4.3438 0.00604248046875 0.052734375 6.71875 7.34375\n",
      "Epoch 68/100 \t Train Err: 4.4062 0.005950927734375 0.055419921875 6.65625 7.59375\n",
      "Epoch 68/100 \t Train Err: 4.3750 0.005859375 0.0546875 6.90625 7.34375\n",
      "Epoch 68/100 \t Train Err: 4.3125 0.005950927734375 0.057373046875 6.6875 7.375\n",
      "Epoch 68/100 \t Train Err: 4.3750 0.005889892578125 0.05517578125 6.53125 7.625\n",
      "Epoch 68/100 \t Train Err: 4.3438 0.00592041015625 0.053955078125 6.53125 7.65625\n",
      "Epoch 68/100 \t Train Err: 4.3438 0.005889892578125 0.056640625 6.65625 7.34375\n",
      "Epoch 68/100 \t Train Err: 4.3125 0.005950927734375 0.05419921875 7.21875 7.25\n",
      "Epoch 69/100 \t Train Err: 4.3438 0.005859375 0.057373046875 6.9375 7.25\n",
      "Epoch 69/100 \t Train Err: 4.4062 0.00592041015625 0.0576171875 6.875 7.4375\n",
      "Epoch 69/100 \t Train Err: 4.3125 0.00592041015625 0.0556640625 6.65625 7.375\n",
      "Epoch 69/100 \t Train Err: 4.3438 0.00592041015625 0.0556640625 7.03125 7.40625\n",
      "Epoch 69/100 \t Train Err: 4.2812 0.005889892578125 0.058837890625 6.53125 7.34375\n",
      "Epoch 69/100 \t Train Err: 4.2500 0.00579833984375 0.05419921875 6.46875 7.25\n",
      "Epoch 69/100 \t Train Err: 4.3125 0.005828857421875 0.051513671875 6.625 7.34375\n",
      "Epoch 69/100 \t Train Err: 4.3125 0.005859375 0.052978515625 6.78125 7.21875\n",
      "Epoch 70/100 \t Train Err: 4.2812 0.005859375 0.04931640625 6.625 7.25\n",
      "Epoch 70/100 \t Train Err: 4.1875 0.005767822265625 0.051025390625 6.21875 7.1875\n",
      "Epoch 70/100 \t Train Err: 4.2500 0.005828857421875 0.05126953125 6.34375 7.4375\n",
      "Epoch 70/100 \t Train Err: 4.2812 0.005706787109375 0.05126953125 6.28125 7.5\n",
      "Epoch 70/100 \t Train Err: 4.3438 0.0057373046875 0.05078125 6.40625 7.5\n",
      "Epoch 70/100 \t Train Err: 4.3125 0.0057373046875 0.05517578125 6.875 7.1875\n",
      "Epoch 70/100 \t Train Err: 4.1562 0.005828857421875 0.052734375 6.78125 6.875\n",
      "Epoch 70/100 \t Train Err: 4.2188 0.00579833984375 0.05419921875 6.59375 7.15625\n",
      "Epoch 71/100 \t Train Err: 4.3125 0.00567626953125 0.052490234375 6.5 7.46875\n",
      "Epoch 71/100 \t Train Err: 4.2500 0.00567626953125 0.05126953125 6.125 7.5\n",
      "Epoch 71/100 \t Train Err: 4.2500 0.005706787109375 0.05224609375 6.5 7.4375\n",
      "Epoch 71/100 \t Train Err: 4.1875 0.0057373046875 0.053466796875 6.65625 6.96875\n",
      "Epoch 71/100 \t Train Err: 4.2812 0.005767822265625 0.0546875 6.875 7.09375\n",
      "Epoch 71/100 \t Train Err: 4.1562 0.005645751953125 0.054443359375 6.40625 6.96875\n",
      "Epoch 71/100 \t Train Err: 4.2188 0.005615234375 0.0498046875 6.3125 7.4375\n",
      "Epoch 71/100 \t Train Err: 4.2500 0.00555419921875 0.05224609375 6.40625 7.40625\n",
      "Epoch 72/100 \t Train Err: 4.2500 0.005615234375 0.052978515625 6.15625 7.375\n",
      "Epoch 72/100 \t Train Err: 4.2500 0.00537109375 0.0517578125 6.5 7.25\n",
      "Epoch 72/100 \t Train Err: 4.1562 0.00543212890625 0.05029296875 6.4375 7.0\n",
      "Epoch 72/100 \t Train Err: 4.1875 0.005401611328125 0.04638671875 6.3125 7.09375\n",
      "Epoch 72/100 \t Train Err: 4.1562 0.00537109375 0.048828125 6.34375 7.09375\n",
      "Epoch 72/100 \t Train Err: 4.2500 0.00537109375 0.0498046875 6.21875 7.3125\n",
      "Epoch 72/100 \t Train Err: 4.2188 0.00531005859375 0.05078125 6.46875 7.21875\n",
      "Epoch 72/100 \t Train Err: 4.1250 0.00537109375 0.05078125 6.46875 6.90625\n",
      "Epoch 73/100 \t Train Err: 4.2500 0.005340576171875 0.053955078125 6.40625 7.1875\n",
      "Epoch 73/100 \t Train Err: 4.1875 0.005279541015625 0.0517578125 6.59375 6.96875\n",
      "Epoch 73/100 \t Train Err: 4.2500 0.00537109375 0.0556640625 6.40625 7.21875\n",
      "Epoch 73/100 \t Train Err: 4.1250 0.00531005859375 0.051513671875 6.34375 6.96875\n",
      "Epoch 73/100 \t Train Err: 4.2188 0.005279541015625 0.04833984375 6.0 7.46875\n",
      "Epoch 73/100 \t Train Err: 4.1250 0.00537109375 0.050537109375 6.0625 7.1875\n",
      "Epoch 73/100 \t Train Err: 4.1250 0.00531005859375 0.048095703125 5.90625 7.09375\n",
      "Epoch 73/100 \t Train Err: 4.1562 0.0052490234375 0.05029296875 6.375 6.90625\n",
      "Epoch 74/100 \t Train Err: 4.0625 0.005523681640625 0.049560546875 6.4375 6.8125\n",
      "Epoch 74/100 \t Train Err: 4.1875 0.005279541015625 0.049072265625 5.90625 7.34375\n",
      "Epoch 74/100 \t Train Err: 4.2188 0.00531005859375 0.04931640625 6.09375 7.375\n",
      "Epoch 74/100 \t Train Err: 4.0938 0.00531005859375 0.05029296875 6.21875 6.96875\n",
      "Epoch 74/100 \t Train Err: 4.1562 0.0052490234375 0.053955078125 6.3125 7.09375\n",
      "Epoch 74/100 \t Train Err: 4.1250 0.00518798828125 0.05126953125 6.3125 7.0625\n",
      "Epoch 74/100 \t Train Err: 4.1562 0.0052490234375 0.051513671875 6.21875 7.0625\n",
      "Epoch 74/100 \t Train Err: 4.0938 0.005218505859375 0.049560546875 6.1875 7.0\n",
      "Epoch 75/100 \t Train Err: 4.1250 0.005218505859375 0.0517578125 6.3125 7.0625\n",
      "Epoch 75/100 \t Train Err: 4.0312 0.005218505859375 0.050048828125 6.03125 6.875\n",
      "Epoch 75/100 \t Train Err: 4.0938 0.005157470703125 0.0498046875 5.84375 7.15625\n",
      "Epoch 75/100 \t Train Err: 4.1250 0.005126953125 0.04541015625 5.875 7.21875\n",
      "Epoch 75/100 \t Train Err: 4.0625 0.005126953125 0.05126953125 6.09375 6.9375\n",
      "Epoch 75/100 \t Train Err: 4.0000 0.005218505859375 0.050537109375 6.40625 6.53125\n",
      "Epoch 75/100 \t Train Err: 4.1250 0.00518798828125 0.049560546875 6.09375 6.9375\n",
      "Epoch 75/100 \t Train Err: 4.0625 0.005126953125 0.046875 5.59375 7.1875\n",
      "Epoch 76/100 \t Train Err: 4.0000 0.00506591796875 0.045654296875 5.5 7.15625\n",
      "Epoch 76/100 \t Train Err: 4.1562 0.005157470703125 0.0458984375 6.0 7.25\n",
      "Epoch 76/100 \t Train Err: 4.0625 0.00518798828125 0.048095703125 6.5 6.59375\n",
      "Epoch 76/100 \t Train Err: 4.0625 0.005096435546875 0.0458984375 6.0 6.875\n",
      "Epoch 76/100 \t Train Err: 4.0312 0.0052490234375 0.044921875 5.75 7.03125\n",
      "Epoch 76/100 \t Train Err: 4.1562 0.00518798828125 0.043701171875 5.75 7.28125\n",
      "Epoch 76/100 \t Train Err: 4.0625 0.005096435546875 0.0498046875 5.84375 7.09375\n",
      "Epoch 76/100 \t Train Err: 4.0625 0.005126953125 0.044189453125 6.25 6.8125\n",
      "Epoch 77/100 \t Train Err: 3.9688 0.005126953125 0.0478515625 6.15625 6.59375\n",
      "Epoch 77/100 \t Train Err: 4.0312 0.005126953125 0.046142578125 5.84375 6.90625\n",
      "Epoch 77/100 \t Train Err: 4.0000 0.00506591796875 0.04541015625 5.71875 6.96875\n",
      "Epoch 77/100 \t Train Err: 3.9531 0.004974365234375 0.046142578125 5.46875 7.0\n",
      "Epoch 77/100 \t Train Err: 4.0312 0.004974365234375 0.044677734375 6.0625 6.8125\n",
      "Epoch 77/100 \t Train Err: 4.0312 0.005035400390625 0.047607421875 6.125 6.6875\n",
      "Epoch 77/100 \t Train Err: 4.0312 0.004852294921875 0.047607421875 6.09375 6.84375\n",
      "Epoch 77/100 \t Train Err: 3.9844 0.0048828125 0.043701171875 5.53125 6.96875\n",
      "Epoch 78/100 \t Train Err: 3.9844 0.004852294921875 0.045654296875 5.875 6.875\n",
      "Epoch 78/100 \t Train Err: 3.9844 0.004913330078125 0.0458984375 5.8125 6.78125\n",
      "Epoch 78/100 \t Train Err: 3.9375 0.0048828125 0.046142578125 5.6875 6.65625\n",
      "Epoch 78/100 \t Train Err: 3.9531 0.004974365234375 0.04541015625 5.71875 6.78125\n",
      "Epoch 78/100 \t Train Err: 4.0000 0.004913330078125 0.0458984375 5.5625 7.0\n",
      "Epoch 78/100 \t Train Err: 3.9375 0.0048828125 0.041748046875 5.46875 6.90625\n",
      "Epoch 78/100 \t Train Err: 3.9688 0.00494384765625 0.043701171875 5.71875 6.875\n",
      "Epoch 78/100 \t Train Err: 3.9375 0.005035400390625 0.048095703125 5.96875 6.46875\n",
      "Epoch 79/100 \t Train Err: 3.9844 0.0050048828125 0.049072265625 6.03125 6.625\n",
      "Epoch 79/100 \t Train Err: 3.9688 0.0050048828125 0.040283203125 4.90625 7.25\n",
      "Epoch 79/100 \t Train Err: 4.0312 0.004974365234375 0.04345703125 5.4375 7.1875\n",
      "Epoch 79/100 \t Train Err: 3.9688 0.005035400390625 0.045166015625 6.03125 6.5625\n",
      "Epoch 79/100 \t Train Err: 4.0000 0.005035400390625 0.0478515625 6.46875 6.28125\n",
      "Epoch 79/100 \t Train Err: 3.9219 0.005035400390625 0.043701171875 5.03125 7.03125\n",
      "Epoch 79/100 \t Train Err: 4.0312 0.004974365234375 0.044921875 4.90625 7.375\n",
      "Epoch 79/100 \t Train Err: 3.9062 0.004974365234375 0.04296875 5.8125 6.625\n",
      "Epoch 80/100 \t Train Err: 3.9219 0.0050048828125 0.04638671875 6.40625 6.25\n",
      "Epoch 80/100 \t Train Err: 3.8750 0.004974365234375 0.045654296875 5.65625 6.5\n",
      "Epoch 80/100 \t Train Err: 3.9531 0.0048828125 0.04345703125 5.15625 7.15625\n",
      "Epoch 80/100 \t Train Err: 3.9688 0.004913330078125 0.043701171875 5.25 7.0\n",
      "Epoch 80/100 \t Train Err: 3.8906 0.004974365234375 0.04345703125 5.875 6.53125\n",
      "Epoch 80/100 \t Train Err: 3.8281 0.00494384765625 0.044677734375 5.65625 6.4375\n",
      "Epoch 80/100 \t Train Err: 3.9531 0.004852294921875 0.041259765625 5.4375 6.96875\n",
      "Epoch 80/100 \t Train Err: 3.9531 0.00494384765625 0.04345703125 5.15625 7.0\n",
      "Epoch 81/100 \t Train Err: 3.9844 0.0048828125 0.0419921875 5.5625 6.8125\n",
      "Epoch 81/100 \t Train Err: 3.9219 0.0048828125 0.04443359375 5.71875 6.46875\n",
      "Epoch 81/100 \t Train Err: 3.9375 0.00482177734375 0.046630859375 5.71875 6.625\n",
      "Epoch 81/100 \t Train Err: 3.9219 0.00469970703125 0.042236328125 5.03125 6.90625\n",
      "Epoch 81/100 \t Train Err: 3.9219 0.0047607421875 0.042236328125 5.0625 7.03125\n",
      "Epoch 81/100 \t Train Err: 3.9844 0.004791259765625 0.044189453125 5.5625 6.90625\n",
      "Epoch 81/100 \t Train Err: 3.8750 0.0048828125 0.044677734375 5.875 6.375\n",
      "Epoch 81/100 \t Train Err: 3.8438 0.00482177734375 0.045166015625 5.40625 6.5625\n",
      "Epoch 82/100 \t Train Err: 3.8281 0.0048828125 0.044189453125 5.0625 6.8125\n",
      "Epoch 82/100 \t Train Err: 3.8906 0.004913330078125 0.042724609375 5.15625 7.03125\n",
      "Epoch 82/100 \t Train Err: 3.8750 0.00482177734375 0.048583984375 5.90625 6.34375\n",
      "Epoch 82/100 \t Train Err: 3.8594 0.004913330078125 0.045166015625 5.6875 6.28125\n",
      "Epoch 82/100 \t Train Err: 3.8594 0.004852294921875 0.043701171875 5.28125 6.78125\n",
      "Epoch 82/100 \t Train Err: 3.8594 0.004852294921875 0.042236328125 4.84375 6.9375\n",
      "Epoch 82/100 \t Train Err: 3.7969 0.0048828125 0.0400390625 5.15625 6.625\n",
      "Epoch 82/100 \t Train Err: 3.8281 0.004730224609375 0.04736328125 5.53125 6.28125\n",
      "Epoch 83/100 \t Train Err: 3.8438 0.0047607421875 0.044921875 5.3125 6.53125\n",
      "Epoch 83/100 \t Train Err: 3.7969 0.00469970703125 0.043212890625 5.09375 6.625\n",
      "Epoch 83/100 \t Train Err: 3.8125 0.004669189453125 0.043701171875 5.1875 6.71875\n",
      "Epoch 83/100 \t Train Err: 3.8281 0.004669189453125 0.042236328125 5.09375 6.6875\n",
      "Epoch 83/100 \t Train Err: 3.8125 0.0047607421875 0.042236328125 5.40625 6.46875\n",
      "Epoch 83/100 \t Train Err: 3.8594 0.00482177734375 0.04150390625 5.34375 6.59375\n",
      "Epoch 83/100 \t Train Err: 3.7500 0.00482177734375 0.041748046875 5.3125 6.375\n",
      "Epoch 83/100 \t Train Err: 3.7812 0.004791259765625 0.040771484375 4.75 6.75\n",
      "Epoch 84/100 \t Train Err: 3.7188 0.0047607421875 0.0390625 5.21875 6.34375\n",
      "Epoch 84/100 \t Train Err: 3.7656 0.0047607421875 0.040771484375 5.09375 6.40625\n",
      "Epoch 84/100 \t Train Err: 3.7969 0.004852294921875 0.04248046875 5.25 6.34375\n",
      "Epoch 84/100 \t Train Err: 3.7344 0.0047607421875 0.03955078125 5.125 6.3125\n",
      "Epoch 84/100 \t Train Err: 3.7500 0.004791259765625 0.03857421875 4.5 6.90625\n",
      "Epoch 84/100 \t Train Err: 3.7656 0.00482177734375 0.04248046875 5.1875 6.40625\n",
      "Epoch 84/100 \t Train Err: 3.7656 0.00482177734375 0.042724609375 5.3125 6.21875\n",
      "Epoch 84/100 \t Train Err: 3.7188 0.004730224609375 0.04345703125 5.21875 6.28125\n",
      "Epoch 85/100 \t Train Err: 3.7500 0.004608154296875 0.039794921875 4.65625 6.625\n",
      "Epoch 85/100 \t Train Err: 3.7344 0.004608154296875 0.04150390625 4.71875 6.53125\n",
      "Epoch 85/100 \t Train Err: 3.7344 0.004730224609375 0.042236328125 5.375 6.3125\n",
      "Epoch 85/100 \t Train Err: 3.7656 0.004730224609375 0.040283203125 5.4375 6.28125\n",
      "Epoch 85/100 \t Train Err: 3.6875 0.00469970703125 0.0400390625 5.0 6.28125\n",
      "Epoch 85/100 \t Train Err: 3.7031 0.00469970703125 0.037109375 4.8125 6.53125\n",
      "Epoch 85/100 \t Train Err: 3.7500 0.00469970703125 0.038330078125 4.78125 6.625\n",
      "Epoch 85/100 \t Train Err: 3.6875 0.004669189453125 0.0400390625 5.34375 6.0625\n",
      "Epoch 86/100 \t Train Err: 3.7344 0.004638671875 0.037841796875 5.40625 6.25\n",
      "Epoch 86/100 \t Train Err: 3.7031 0.004638671875 0.0380859375 4.875 6.46875\n",
      "Epoch 86/100 \t Train Err: 3.7344 0.00457763671875 0.0390625 4.59375 6.6875\n",
      "Epoch 86/100 \t Train Err: 3.7031 0.004638671875 0.036865234375 5.09375 6.21875\n",
      "Epoch 86/100 \t Train Err: 3.7031 0.004638671875 0.0390625 5.28125 6.15625\n",
      "Epoch 86/100 \t Train Err: 3.7500 0.004669189453125 0.037841796875 4.875 6.59375\n",
      "Epoch 86/100 \t Train Err: 3.7188 0.004638671875 0.0390625 4.625 6.5625\n",
      "Epoch 86/100 \t Train Err: 3.6406 0.004730224609375 0.039306640625 5.125 5.9375\n",
      "Epoch 87/100 \t Train Err: 3.6875 0.00469970703125 0.03759765625 4.9375 6.28125\n",
      "Epoch 87/100 \t Train Err: 3.6875 0.004730224609375 0.039794921875 4.78125 6.25\n",
      "Epoch 87/100 \t Train Err: 3.7031 0.004791259765625 0.0361328125 4.65625 6.53125\n",
      "Epoch 87/100 \t Train Err: 3.6719 0.0047607421875 0.03662109375 4.625 6.5\n",
      "Epoch 87/100 \t Train Err: 3.6094 0.004791259765625 0.037109375 5.125 6.0\n",
      "Epoch 87/100 \t Train Err: 3.7344 0.00482177734375 0.036865234375 5.375 6.15625\n",
      "Epoch 87/100 \t Train Err: 3.6719 0.0047607421875 0.037353515625 4.65625 6.53125\n",
      "Epoch 87/100 \t Train Err: 3.6562 0.004791259765625 0.03662109375 4.59375 6.4375\n",
      "Epoch 88/100 \t Train Err: 3.6562 0.004638671875 0.03857421875 5.28125 6.0\n",
      "Epoch 88/100 \t Train Err: 3.5938 0.004730224609375 0.040283203125 4.875 6.03125\n",
      "Epoch 88/100 \t Train Err: 3.6875 0.004608154296875 0.03955078125 4.5625 6.5\n",
      "Epoch 88/100 \t Train Err: 3.6875 0.004730224609375 0.0380859375 4.71875 6.46875\n",
      "Epoch 88/100 \t Train Err: 3.5469 0.004608154296875 0.037353515625 4.53125 6.21875\n",
      "Epoch 88/100 \t Train Err: 3.6250 0.004608154296875 0.040771484375 4.9375 6.125\n",
      "Epoch 88/100 \t Train Err: 3.6094 0.004486083984375 0.038330078125 4.8125 6.21875\n",
      "Epoch 88/100 \t Train Err: 3.5938 0.0045166015625 0.0400390625 4.75 6.21875\n",
      "Epoch 89/100 \t Train Err: 3.6406 0.0045166015625 0.038330078125 4.875 6.21875\n",
      "Epoch 89/100 \t Train Err: 3.5469 0.00457763671875 0.041748046875 5.21875 5.6875\n",
      "Epoch 89/100 \t Train Err: 3.6250 0.004608154296875 0.03759765625 4.46875 6.40625\n",
      "Epoch 89/100 \t Train Err: 3.5469 0.004638671875 0.035400390625 4.625 6.15625\n",
      "Epoch 89/100 \t Train Err: 3.6094 0.00469970703125 0.0341796875 4.40625 6.375\n",
      "Epoch 89/100 \t Train Err: 3.5781 0.00469970703125 0.0361328125 4.75 6.0625\n",
      "Epoch 89/100 \t Train Err: 3.4688 0.0047607421875 0.0341796875 4.53125 5.96875\n",
      "Epoch 89/100 \t Train Err: 3.5781 0.00469970703125 0.033935546875 4.40625 6.34375\n",
      "Epoch 90/100 \t Train Err: 3.5625 0.0047607421875 0.03369140625 4.53125 6.25\n",
      "Epoch 90/100 \t Train Err: 3.6094 0.004791259765625 0.033447265625 4.875 6.0625\n",
      "Epoch 90/100 \t Train Err: 3.6250 0.0048828125 0.034423828125 4.40625 6.34375\n",
      "Epoch 90/100 \t Train Err: 3.5469 0.004913330078125 0.03662109375 4.71875 6.0\n",
      "Epoch 90/100 \t Train Err: 3.5000 0.004913330078125 0.035400390625 4.40625 6.125\n",
      "Epoch 90/100 \t Train Err: 3.5469 0.00494384765625 0.03369140625 4.125 6.375\n",
      "Epoch 90/100 \t Train Err: 3.5156 0.004852294921875 0.035400390625 4.53125 5.96875\n",
      "Epoch 90/100 \t Train Err: 3.5156 0.00469970703125 0.036376953125 4.71875 5.90625\n",
      "Epoch 91/100 \t Train Err: 3.5312 0.0047607421875 0.033447265625 4.375 6.28125\n",
      "Epoch 91/100 \t Train Err: 3.5469 0.004730224609375 0.032958984375 4.34375 6.3125\n",
      "Epoch 91/100 \t Train Err: 3.6094 0.004669189453125 0.0390625 5.34375 5.65625\n",
      "Epoch 91/100 \t Train Err: 3.5156 0.004608154296875 0.032958984375 4.28125 6.0625\n",
      "Epoch 91/100 \t Train Err: 3.5469 0.00457763671875 0.03125 4.03125 6.4375\n",
      "Epoch 91/100 \t Train Err: 3.5312 0.004608154296875 0.03564453125 4.84375 5.71875\n",
      "Epoch 91/100 \t Train Err: 3.4844 0.004547119140625 0.0322265625 4.5 5.90625\n",
      "Epoch 91/100 \t Train Err: 3.5312 0.004608154296875 0.0299072265625 3.953125 6.625\n",
      "Epoch 92/100 \t Train Err: 3.5000 0.004547119140625 0.03515625 5.21875 5.46875\n",
      "Epoch 92/100 \t Train Err: 3.4688 0.004486083984375 0.034423828125 4.53125 5.9375\n",
      "Epoch 92/100 \t Train Err: 3.4531 0.00457763671875 0.032470703125 3.875 6.375\n",
      "Epoch 92/100 \t Train Err: 3.5938 0.004669189453125 0.038330078125 4.9375 5.84375\n",
      "Epoch 92/100 \t Train Err: 3.5156 0.004730224609375 0.03369140625 5.03125 5.6875\n",
      "Epoch 92/100 \t Train Err: 3.5625 0.004791259765625 0.029052734375 3.890625 6.65625\n",
      "Epoch 92/100 \t Train Err: 3.4531 0.004730224609375 0.032470703125 4.5 5.90625\n",
      "Epoch 92/100 \t Train Err: 3.4531 0.0047607421875 0.030517578125 4.71875 5.6875\n",
      "Epoch 93/100 \t Train Err: 3.5156 0.00482177734375 0.0281982421875 3.78125 6.53125\n",
      "Epoch 93/100 \t Train Err: 3.4531 0.00482177734375 0.03173828125 4.6875 5.5625\n",
      "Epoch 93/100 \t Train Err: 3.4531 0.004791259765625 0.03271484375 4.6875 5.6875\n",
      "Epoch 93/100 \t Train Err: 3.4375 0.00469970703125 0.0279541015625 3.96875 6.25\n",
      "Epoch 93/100 \t Train Err: 3.3594 0.004730224609375 0.03076171875 4.125 5.9375\n",
      "Epoch 93/100 \t Train Err: 3.4688 0.004730224609375 0.0301513671875 4.96875 5.625\n",
      "Epoch 93/100 \t Train Err: 3.3906 0.004730224609375 0.0296630859375 4.03125 6.0625\n",
      "Epoch 93/100 \t Train Err: 3.4688 0.0047607421875 0.0294189453125 4.25 6.0625\n",
      "Epoch 94/100 \t Train Err: 3.3906 0.0047607421875 0.031005859375 4.375 5.6875\n",
      "Epoch 94/100 \t Train Err: 3.4219 0.004791259765625 0.031494140625 4.53125 5.8125\n",
      "Epoch 94/100 \t Train Err: 3.4375 0.004791259765625 0.0281982421875 4.25 6.03125\n",
      "Epoch 94/100 \t Train Err: 3.4219 0.004791259765625 0.02978515625 4.34375 5.8125\n",
      "Epoch 94/100 \t Train Err: 3.4219 0.00469970703125 0.0308837890625 4.375 5.78125\n",
      "Epoch 94/100 \t Train Err: 3.4375 0.004669189453125 0.028564453125 4.25 6.0\n",
      "Epoch 94/100 \t Train Err: 3.3594 0.00469970703125 0.0283203125 4.25 5.875\n",
      "Epoch 94/100 \t Train Err: 3.3594 0.004852294921875 0.03125 4.28125 5.6875\n",
      "Epoch 95/100 \t Train Err: 3.3750 0.004791259765625 0.0284423828125 4.25 5.6875\n",
      "Epoch 95/100 \t Train Err: 3.3750 0.0047607421875 0.0279541015625 3.90625 5.96875\n",
      "Epoch 95/100 \t Train Err: 3.3594 0.004730224609375 0.029052734375 4.28125 5.71875\n",
      "Epoch 95/100 \t Train Err: 3.3906 0.004791259765625 0.0296630859375 4.3125 5.78125\n",
      "Epoch 95/100 \t Train Err: 3.3750 0.004791259765625 0.03076171875 4.125 5.78125\n",
      "Epoch 95/100 \t Train Err: 3.3594 0.0047607421875 0.0291748046875 4.25 5.8125\n",
      "Epoch 95/100 \t Train Err: 3.3438 0.004669189453125 0.02783203125 4.15625 5.875\n",
      "Epoch 95/100 \t Train Err: 3.3281 0.0047607421875 0.0299072265625 4.375 5.5\n",
      "Epoch 96/100 \t Train Err: 3.3438 0.0048828125 0.02880859375 3.84375 5.90625\n",
      "Epoch 96/100 \t Train Err: 3.3750 0.0047607421875 0.030029296875 4.0625 5.8125\n",
      "Epoch 96/100 \t Train Err: 3.3438 0.00482177734375 0.0308837890625 3.9375 5.71875\n",
      "Epoch 96/100 \t Train Err: 3.3125 0.004730224609375 0.028564453125 4.21875 5.59375\n",
      "Epoch 96/100 \t Train Err: 3.2969 0.004638671875 0.0291748046875 3.765625 5.90625\n",
      "Epoch 96/100 \t Train Err: 3.3750 0.004638671875 0.034912109375 4.375 5.75\n",
      "Epoch 96/100 \t Train Err: 3.2656 0.004638671875 0.029296875 4.125 5.53125\n",
      "Epoch 96/100 \t Train Err: 3.2500 0.004638671875 0.0286865234375 3.984375 5.625\n",
      "Epoch 97/100 \t Train Err: 3.2656 0.00457763671875 0.028564453125 4.125 5.59375\n",
      "Epoch 97/100 \t Train Err: 3.3438 0.004547119140625 0.02587890625 3.859375 5.9375\n",
      "Epoch 97/100 \t Train Err: 3.3125 0.00457763671875 0.02783203125 4.03125 5.6875\n",
      "Epoch 97/100 \t Train Err: 3.3438 0.004608154296875 0.02783203125 4.125 5.6875\n",
      "Epoch 97/100 \t Train Err: 3.3125 0.004730224609375 0.0279541015625 3.875 5.75\n",
      "Epoch 97/100 \t Train Err: 3.2500 0.004730224609375 0.0289306640625 3.78125 5.6875\n",
      "Epoch 97/100 \t Train Err: 3.2812 0.004669189453125 0.030517578125 4.03125 5.5625\n",
      "Epoch 97/100 \t Train Err: 3.3281 0.004638671875 0.029296875 4.1875 5.625\n",
      "Epoch 98/100 \t Train Err: 3.2969 0.004669189453125 0.0264892578125 3.734375 6.0\n",
      "Epoch 98/100 \t Train Err: 3.2031 0.0047607421875 0.0262451171875 3.921875 5.5\n",
      "Epoch 98/100 \t Train Err: 3.2969 0.004791259765625 0.02685546875 4.59375 5.1875\n",
      "Epoch 98/100 \t Train Err: 3.3750 0.0047607421875 0.02392578125 3.34375 6.5\n",
      "Epoch 98/100 \t Train Err: 3.4688 0.004791259765625 0.033447265625 5.625 4.625\n",
      "Epoch 98/100 \t Train Err: 4.5625 0.00482177734375 0.01953125 1.4296875 11.0625\n",
      "Epoch 98/100 \t Train Err: 10.6875 0.00567626953125 0.44140625 46.5 1.109375\n",
      "Epoch 98/100 \t Train Err: 11.5625 0.0096435546875 0.0252685546875 0.322265625 29.875\n",
      "Epoch 99/100 \t Train Err: 12.5000 0.1318359375 0.0341796875 0.1640625 32.25\n",
      "Epoch 99/100 \t Train Err: 7.3125 0.71484375 0.66796875 2.953125 17.125\n",
      "Epoch 99/100 \t Train Err: 9.1250 1.265625 3.046875 38.0 3.265625\n",
      "Epoch 99/100 \t Train Err: 9.4375 1.078125 3.578125 40.5 2.859375\n",
      "Epoch 99/100 \t Train Err: 5.9688 0.419921875 1.484375 14.3125 8.125\n",
      "Epoch 99/100 \t Train Err: 6.6250 0.01513671875 0.3125 4.5 15.25\n",
      "Epoch 99/100 \t Train Err: 7.5000 0.326171875 0.042236328125 2.78125 18.0\n",
      "Epoch 99/100 \t Train Err: 6.6250 0.765625 0.087890625 5.0 14.375\n"
     ]
    }
   ],
   "source": [
    "while epoch < NEPOCHS:\n",
    "    model.train()\n",
    "    with open(f\"data/{epoch}.pickle\", \"rb\") as f:\n",
    "        pickled_stuff = pickle.load(f)\n",
    "    data = pickled_stuff[\"data\"].to(device)\n",
    "    label = pickled_stuff[\"labels\"].to(device)\n",
    "    padding = pickled_stuff[\"padding\"].to(device)\n",
    "    dataset = TensorDataset(data, label, padding)\n",
    "    loader = DataLoader(dataset, batch_size=BSZ)\n",
    "    for batch_src, batch_labels, batch_padding_mask in loader:\n",
    "        optimizer.zero_grad()\n",
    "        output = model(batch_src, batch_padding_mask)\n",
    "        loss = criterion(output.squeeze(1), batch_labels)\n",
    "        train_loss = loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # scheduler.step(loss)\n",
    "    \n",
    "        # test_loss = evaluate()\n",
    "        # test_short_loss = evaluate_short()\n",
    "        \n",
    "        # test_err.append(test_loss)\n",
    "        train_err.append(train_loss)\n",
    "        len1.append(criterion(output[batch_labels == 1].squeeze(1), batch_labels[batch_labels==1]))\n",
    "        len2.append(criterion(output[batch_labels == 2].squeeze(1), batch_labels[batch_labels==2]))\n",
    "        len3.append(criterion(output[batch_labels == 3].squeeze(1), batch_labels[batch_labels==3]))\n",
    "        len15.append(criterion(output[batch_labels == 15].squeeze(1), batch_labels[batch_labels==15]))\n",
    "        \n",
    "        with open('loss', 'a') as f:\n",
    "            f.write(f\"{train_loss}\\n\")\n",
    "        print(f\"Epoch {epoch}/{NEPOCHS} \\t Train Err: {train_loss:.4f} {len1[-1]} {len2[-1]} {len3[-1]} {len15[-1]}\")\n",
    "\n",
    "    epoch += 1\n",
    "    if epoch % 100 == 0:\n",
    "        torch.save(model.state_dict(), f\"model_weights_{epoch}.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\"\"\n",
    "# Now let's figure out what it's doing. \n",
    "\n",
    "# step 1: figure out what people are attending to \n",
    "# \"\"\"\n",
    "\n",
    "# example_graph, answer, padding = mkbatch(1)\n",
    "# sentance_embeddings = model.full_embedding(example_graph)[0,:,:][example_graph.flatten() != 0]\n",
    "# WQ,WK,WV = torch.split(model.transformer_encoder.layers[0].self_attn.in_proj_weight, (MODEL_DIM, MODEL_DIM, MODEL_DIM))\n",
    "\n",
    "# Q = sentance_embeddings@WQ\n",
    "# K = sentance_embeddings@WK\n",
    "\n",
    "# raw_scores = Q @ K.T / sqrt(MODEL_DIM)\n",
    "# soft = torch.softmax(raw_scores, dim=-1).detach().cpu().to(float).numpy()\n",
    "# plt.imshow(soft)\n",
    "# plt.show()\n",
    "\n",
    "# print(example_graph)\n",
    "\n",
    "# print(Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.suptitle('MSE vs Epochs')\n",
    "plt.plot(train_err, label='Train', color='blue')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "execution_state": "idle",
   "metadata": {
    "id": "LoGEmM5lH7_A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ))\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    output = model(batch_src, batch_padding_mask)\n",
    "x = batch_labels.detach().to(torch.uint8)\n",
    "y = output.detach()\n",
    "cnts = torch.bincount(x)\n",
    "weights = [1/cnts[i.item()].item() for i in x] # normalize by label count\n",
    "fig, ax = plt.subplots()\n",
    "h = ax.hist2d(x.cpu().numpy().flatten(), y.to(torch.float16).cpu().numpy().flatten(), weights=weights, bins=[15,50], norm=mpl.colors.LogNorm())\n",
    "fig.colorbar(h[3], ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LC6Xv3YfC0Rm"
   },
   "source": [
    "# Step 5: Fine Tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_TUNE_EPOCHS = 100\n",
    "TUNE_LR = 1e-5\n",
    "TUNE_WD = 0 # 1e-5\n",
    "\n",
    "tune_criterion = nn.MSELoss()\n",
    "tune_optimizer = torch.optim.Adam(model.parameters(), lr=TUNE_LR, weight_decay=TUNE_WD)\n",
    "\n",
    "tune_train_err = []\n",
    "\n",
    "# clear loss file\n",
    "open('tune_loss', 'w').close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tune_evaluate():\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ)\n",
    "        output = model(batch_src, batch_padding_mask)\n",
    "        loss = criterion(output.squeeze(1), batch_labels)\n",
    "    return loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(N_TUNE_EPOCHS):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ)\n",
    "    optimizer.zero_grad()\n",
    "    output = model(batch_src, batch_padding_mask)\n",
    "    loss = criterion(output.squeeze(1), batch_labels)\n",
    "    train_loss = loss.item()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    tune_train_err.append(train_loss)\n",
    "    with open('tune_loss', 'a') as f:\n",
    "        f.write(f\"{train_loss}\\n\")\n",
    "    print(f\"Epoch {epoch}/{N_TUNE_EPOCHS} \\t Train Err: {train_loss:.4f}\")\n",
    "\n",
    "    if epoch % 10 == 9:\n",
    "        torch.save(model.state_dict(), f\"tune_model_weights_{epoch + 1}.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.suptitle('MSE vs Epochs')\n",
    "plt.plot(tune_train_err, label='Train', color='blue')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "tune_evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ)\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    output = model(batch_src, batch_padding_mask)\n",
    "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n",
    "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n",
    "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JtTLXn4zC1z_"
   },
   "source": [
    "# Step 6: Test generalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ, test=True)\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    output = model(batch_src, batch_padding_mask)\n",
    "print(criterion(output.squeeze(1), batch_labels).item())\n",
    "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n",
    "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n",
    "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('training-loss') as f:\n",
    "    train_err = list(map(float, f.read().split()))\n",
    "    plt.suptitle('Log MSE vs Epochs')\n",
    "    plt.plot(torch.log(torch.tensor(train_err)[:500]), label='Train', color='blue')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Log MSE')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"lens_loss\", \"wb\") as f:\n",
    "    pickle.dump((train_err, len1, len2, len3, len15), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "execution_state": "idle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.suptitle('Log MSE vs Epochs')\n",
    "plt.plot(torch.log(torch.tensor(train_err)), label='Train', color='blue')\n",
    "plt.plot(torch.log(torch.tensor(len1)).to(torch.float16), label='Len 1', color='red')\n",
    "plt.plot(torch.log(torch.tensor(len2)).to(torch.float16), label='Len 2', color='green')\n",
    "plt.plot(torch.log(torch.tensor(len3)).to(torch.float16), label='Len 3', color='yellow')\n",
    "plt.plot(torch.log(torch.tensor(len15)).to(torch.float16), label='Len 15', color='magenta')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Log MSE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}