04_callbacks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from exp.nb_03 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataBunch/Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_valid, y_valid = get_data()\n",
    "train_ds, valid_ds = Dataset(x_train, y_train), Dataset(x_valid, y_valid)\n",
    "nh, bs = 50, 64\n",
    "c = y_train.max().item()+1\n",
    "loss_func = F.cross_entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Factor out the connected pieces of info out of the fit() argument list\n",
    "\n",
    "`fit(epochs, model, loss_func, opt, train_dl, valid_dl)`\n",
    "\n",
    "Let's replace it with something that looks like this:\n",
    "\n",
    "`fit(1, learn)`\n",
    "\n",
    "This will allow us to tweak what's happening inside the training loop in other places of the code because the `Learner` object will be mutable, so changing any of its attribute elsewhere will be seen in our training loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class DataBunch():\n",
    "    def __init__(self, train_dl, valid_dl, c=None):\n",
    "        self.train_dl, self.valid_dl, self.c = train_dl, valid_dl, c\n",
    "        \n",
    "    @property\n",
    "    def train_ds(self): return self.train_dl.dataset\n",
    "    \n",
    "    @property\n",
    "    def valid_ds(self): return self.valid_dl.dataset      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(781, 156)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_ds)//bs, len(valid_ds)//bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_dls??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([50000, 784])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train_ds.x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000, 784])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.valid_ds.x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def get_model(data, lr=0.5, nh=50):\n",
    "    m = data.train_ds.x.shape[1]\n",
    "    model = nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh, data.c))\n",
    "    return model, optim.SGD(model.parameters(), lr=lr)\n",
    "\n",
    "class Learner():\n",
    "    def __init__(self, model, opt, loss_func, data):\n",
    "        self.model, self.opt, self.loss_func, self.data = model, opt, loss_func, data   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(*get_model(data), loss_func, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs, learn):\n",
    "    for epoch in range(epochs):\n",
    "        learn.model.train()\n",
    "        for xb,yb in learn.data.train_dl:\n",
    "            loss = learn.loss_func(learn.model(xb), yb)\n",
    "            loss.backward()\n",
    "            learn.opt.step()\n",
    "            learn.opt.zero_grad()\n",
    "\n",
    "        learn.model.eval()\n",
    "        with torch.no_grad():\n",
    "            tot_loss,tot_acc = 0.,0.\n",
    "            for xb,yb in learn.data.valid_dl:\n",
    "                pred = learn.model(xb)\n",
    "                tot_loss += learn.loss_func(pred, yb)\n",
    "                tot_acc  += accuracy (pred,yb)\n",
    "        nv = len(learn.data.valid_dl)\n",
    "        print(epoch, tot_loss/nv, tot_acc/nv)\n",
    "    return tot_loss/nv, tot_acc/nv    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(0.2936) tensor(0.9110)\n"
     ]
    }
   ],
   "source": [
    "loss, acc = fit(1, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CallbackHandler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are callbacks and why do we need them?  \n",
    "While the above training loops works perfectly, there are probably going to be times when we want to customize it. There are many different things we might want to do, e.g.:\n",
    "- Print out some custom metrics during the training\n",
    "- Alter the learning rate throughout the training\n",
    "- Switch our training between different models (e.g. in a GAN architecture)    \n",
    "Callbacks allow us to do these types of things. Essentially, a callback is an item that inserts custom code into the training loop at different times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How can we create callbacks?\n",
    "There are three main steps to creating callbacks in our training loop:\n",
    "- Create some callback objects\n",
    "- Create a CallbackHandler (an object where we will store our callback objects)\n",
    "- Incorporate the callbacks into our training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generic callback class\n",
    "class Callback():\n",
    "    def begin_fit(self, learn):\n",
    "        self.learn = learn\n",
    "        return True\n",
    "    def after_fit(self): return True\n",
    "    def begin_epoch(self, epoch):\n",
    "        self.epoch = epoch\n",
    "        return True\n",
    "    def after_epoch(self): return True\n",
    "    def begin_validate(self): return True\n",
    "    def begin_batch(self, xb, yb):\n",
    "        self.xb, self.yb = xb, yb\n",
    "        return True\n",
    "    def after_loss(self, loss):\n",
    "        self.loss = loss\n",
    "        return True\n",
    "    def after_backward(self): return True\n",
    "    def after_step(self): return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why do we need a generic callback class?\n",
    "You may have noticed that when we wrote the callbacks, we inherited from a generic callback class. This is to make sure that every callback object has a function defined for every different point in time (begin_fit, after_fit, begin_epoch, etc.). If these functions aren’t all defined, then we will get an exception when the training loop runs, because it looks for each of these functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import time\n",
    "# Step 1: crete some callback objects\n",
    "# prints the number of ITERATIONS(Batches) upto 10 and then stops training.\n",
    "class TestCallback(Callback):\n",
    "    def begin_fit(self, learn):\n",
    "        super().begin_fit(learn)\n",
    "        self.n_iters = 0 \n",
    "        return True\n",
    "    \n",
    "    # after_step=True zeros out the gradients\n",
    "    def after_step(self):\n",
    "        self.n_iters += 1\n",
    "        print(self.n_iters)\n",
    "        if self.n_iters>=10: self.learn.stop = True\n",
    "        return True\n",
    "\n",
    "#prints the time at which an epoch starts\n",
    "#Every time we begin an epoch, we will call begin_epoch,\n",
    "#which will keep track of which epoch we are on, and \n",
    "#print out the epoch number and the current time\n",
    "class Timecheck(Callback):\n",
    "    # when we begin training, we will call begin_fit, which will set epoch_counter to 1.\n",
    "    def begin_fit(self, learn):\n",
    "        self.learn = learn\n",
    "        self.epoch_counter = 1\n",
    "        return True\n",
    "    \n",
    "    def begin_epoch(self, epoch):\n",
    "        self.epoch = epoch\n",
    "        print(f'Epoch {self.epoch_counter} started at {time.strftime(\"%H:%M:%S\", time.gmtime())}')\n",
    "        self.epoch_counter += 1\n",
    "        return True\n",
    "    \n",
    "# Every time we complete an epoch, we will call \n",
    "# after_epoch, which will print out the most recent loss\n",
    "# calculation.\n",
    "class PrintLoss(Callback):\n",
    "    def after_epoch(self):\n",
    "        print(f'Loss:{self.loss}')\n",
    "        return True            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Step 2: create a callback Handler\n",
    "class CallbackHandler():\n",
    "    def __init__(self, cbs=None):\n",
    "        self.cbs = cbs if cbs else []\n",
    "    \n",
    "    def begin_fit(self, learn):\n",
    "        self.learn = learn\n",
    "        self.in_train = True\n",
    "        learn.stop = False\n",
    "        res = True\n",
    "        for cb in self.cbs: res=res and cb.begin_fit(learn)\n",
    "        return res\n",
    "    \n",
    "    def after_fit(self):\n",
    "        res = not self.in_train\n",
    "        for cb in self.cbs: res=res and cb.after_fit()\n",
    "        return res\n",
    "    \n",
    "    def begin_epoch(self, epoch):\n",
    "        self.learn.model.train()\n",
    "        self.in_train = True\n",
    "        res = True\n",
    "        for cb in self.cbs: res=res and cb.begin_epoch(epoch)\n",
    "        return res\n",
    "    \n",
    "    def begin_validate(self):\n",
    "        self.learn.model.eval()\n",
    "        self.in_train = False\n",
    "        res = True\n",
    "        for cb in self.cbs: res=res and cb.begin_validate()\n",
    "        return res\n",
    "    \n",
    "    def after_epoch(self):\n",
    "        res = True\n",
    "        for cb in self.cbs: res=res and cb.after_epoch()\n",
    "        return res\n",
    "    \n",
    "    def begin_batch(self, xb, yb):\n",
    "        res = True\n",
    "        for cb in self.cbs: res=res and cb.begin_batch(xb, yb)\n",
    "        return res\n",
    "    \n",
    "    def after_loss(self, loss):\n",
    "        res = self.in_train\n",
    "        for cb in self.cbs: res=res and cb.after_loss(loss)\n",
    "        return res\n",
    "    \n",
    "    def after_backward(self):\n",
    "        res= True\n",
    "        for cb in self.cbs: res=res and cb.after_backward()\n",
    "        return res\n",
    "    \n",
    "    def after_step(self):\n",
    "        res = True\n",
    "        for cb in self.cbs: res=res and cb.after_step()\n",
    "        return res\n",
    "    \n",
    "    def do_stop(self):\n",
    "        try: return self.learn.stop\n",
    "        finally: self.learn.stop = False"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CallbackHandler class serves two main purposes:\n",
    "- It gives us a place to store all of our individual callbacks. In the above code, the individual callbacks are stored in a list named self.cbs (this happens in the “__init__” function)\n",
    "- It allows us to easily call all of our individual callbacks at the same time. For each different point in time in the training loop, we create a function (begin_fit, after_fit, begin_epoch, etc.). When we call one of these functions, it goes through all our individual callbacks (TestCallback, TimeCheck, PrintLoss) and calls its same-named function. You’ll notice that these functions don’t exist solely to call the callbacks, e.g. begin_epoch also calls learn.model.train(), but that’s not too important to understand for now  \n",
    "  \n",
    "Here’s a diagram summarizing what we have created so far:\n",
    "![image.png](attachment:image.png)\n",
    "Also the TestCallback function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every time the begin_fit() function is called on CallbackHandler() it loops through the list of callbacks (cbs) and for each callback(cb) if runs the begin_fit function inside that callback class.   \n",
    "__Remember, that in the case where there is no begin_fit function in that particular callback class, it will run on the original Callback() template class we discussed at point 1.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was our training loop (without validation) from the previous notebook, with the inner loop contents factored out:\n",
    "\n",
    "```python\n",
    "def one_batch(xb,yb):\n",
    "    pred = model(xb)\n",
    "    loss = loss_func(pred, yb)\n",
    "    loss.backward()\n",
    "    opt.step()\n",
    "    opt.zero_grad()\n",
    "    \n",
    "def fit():\n",
    "    for epoch in range(epochs):\n",
    "        for b in train_dl: one_batch(*b)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add callbacks so we can remove complexity from loop, and make it flexible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Step 3: Incorporate the callbacks into our training loop\n",
    "def one_batch(xb, yb, cb):\n",
    "    if not cb.begin_batch(xb, yb): return\n",
    "    loss = cb.learn.loss_func(cb.learn.model(xb), yb)\n",
    "    if not cb.after_loss(loss): return\n",
    "    loss.backward()\n",
    "    if cb.after_backward(): cb.learn.opt.step()\n",
    "    if cb.after_step(): cb.learn.opt.zero_grad()\n",
    "#     print(f\"A{cb.learn.stop}\")\n",
    "        \n",
    "def all_batches(dl, cb):\n",
    "    for xb,yb in dl:\n",
    "        one_batch(xb,yb, cb)\n",
    "#         print(f\"B{cb.learn.stop}\")\n",
    "#         print(f\"do_stop:{cb.do_stop()}\")\n",
    "        if cb.do_stop(): return\n",
    "    \n",
    "def fit(epochs, learn, cb):\n",
    "    if not cb.begin_fit(learn): return\n",
    "    for epoch in range(epochs):\n",
    "        if not cb.begin_epoch(epoch): continue\n",
    "        all_batches(learn.data.train_dl, cb)\n",
    "        \n",
    "        if cb.begin_validate():\n",
    "            with torch.no_grad(): all_batches(learn.data.valid_dl, cb)\n",
    "#         print(cb.learn.stop)\n",
    "        if cb.do_stop() or not cb.after_epoch(): break\n",
    "    cb.after_fit()             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed that there are a lot of if statements in the updated code. This gives us the ability to stop the training at any time we want (or skip specific steps). For example, if we only wanted to train for 20 batches, we could create a callback that returns False after the 20th batch is complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 started at 12:54:06\n",
      "Loss:0.006560278125107288\n",
      "Epoch 2 started at 12:54:07\n",
      "Loss:0.002138900337740779\n",
      "Epoch 3 started at 12:54:09\n",
      "Loss:0.011787118390202522\n",
      "Epoch 4 started at 12:54:10\n",
      "Loss:0.004337035119533539\n",
      "Epoch 5 started at 12:54:11\n",
      "Loss:0.005933812819421291\n"
     ]
    }
   ],
   "source": [
    "fit(5, learn, cb=CallbackHandler([Timecheck(), PrintLoss()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "fit(1, learn, cb=CallbackHandler([TestCallback()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 started at 12:56:02\n",
      "1\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "2\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "3\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "4\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "5\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "6\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "7\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "8\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "9\n",
      "AFalse\n",
      "BFalse\n",
      "do_stop:False\n",
      "10\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "11\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "12\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "13\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "14\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "15\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "16\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "17\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "18\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "19\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "20\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "21\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "22\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "23\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "24\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "25\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "26\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "27\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "28\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "29\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "30\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "31\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "32\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "33\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "34\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "35\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "686\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "False\n",
      "Loss:0.272739976644516\n",
      "Epoch 2 started at 12:56:05\n",
      "783\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "784\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "785\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "786\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "787\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "788\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "789\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "790\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "791\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "792\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "793\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "794\n",
      "ATrue\n",
      "BTrue\n",
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      "795\n",
      "ATrue\n",
      "BTrue\n",
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      "796\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "797\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "798\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "799\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "800\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "801\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "802\n",
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      "BTrue\n",
      "do_stop:True\n",
      "803\n",
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      "862\n",
      "ATrue\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "do_stop:True\n",
      "863\n",
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      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1141\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
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      "1224\n",
      "ATrue\n",
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      "ATrue\n",
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      "1227\n",
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      "1228\n",
      "ATrue\n",
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      "1229\n",
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      "1230\n",
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      "1231\n",
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      "1232\n",
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      "1233\n",
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      "1235\n",
      "ATrue\n",
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      "1236\n",
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      "1237\n",
      "ATrue\n",
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      "1238\n",
      "ATrue\n",
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      "1239\n",
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      "1240\n",
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      "ATrue\n",
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      "ATrue\n",
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      "ATrue\n",
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      "1253\n",
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      "1262\n",
      "ATrue\n",
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      "1263\n",
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      "1264\n",
      "ATrue\n",
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      "1265\n",
      "ATrue\n",
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      "ATrue\n",
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      "1267\n",
      "ATrue\n",
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      "1268\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1269\n",
      "ATrue\n",
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      "1270\n",
      "ATrue\n",
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      "1271\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1272\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1273\n",
      "ATrue\n",
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      "1274\n",
      "ATrue\n",
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      "1275\n",
      "ATrue\n",
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      "1276\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1277\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1278\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1279\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1280\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1281\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1282\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1283\n",
      "ATrue\n",
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      "1284\n",
      "ATrue\n",
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      "ATrue\n",
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      "ATrue\n",
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      "ATrue\n",
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      "do_stop:True\n",
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      "ATrue\n",
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      "do_stop:True\n",
      "1289\n",
      "ATrue\n",
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      "do_stop:True\n",
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      "ATrue\n",
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      "do_stop:True\n",
      "1291\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1292\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1293\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1294\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1295\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1296\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1297\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1298\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1299\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1300\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1301\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1302\n",
      "ATrue\n",
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      "do_stop:True\n",
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      "ATrue\n",
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      "ATrue\n",
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      "ATrue\n",
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      "ATrue\n",
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      "ATrue\n",
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      "do_stop:True\n",
      "1308\n",
      "ATrue\n",
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      "ATrue\n",
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      "do_stop:True\n",
      "1310\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1311\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1312\n",
      "ATrue\n",
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      "ATrue\n",
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      "1314\n",
      "ATrue\n",
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      "ATrue\n",
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      "do_stop:True\n",
      "1316\n",
      "ATrue\n",
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      "ATrue\n",
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      "1318\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1319\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1320\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1321\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1322\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1323\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1324\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1325\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1326\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1327\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1328\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1329\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1330\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1331\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1332\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1333\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1334\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1335\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1336\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1337\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1338\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1339\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1340\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1341\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1342\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1343\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1344\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1345\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1346\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1347\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1348\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1349\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1350\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1351\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1352\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1353\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1354\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1355\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1356\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1357\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1358\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1359\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1360\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1361\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1362\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1363\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1364\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1365\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1366\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1367\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1368\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1369\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1370\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1371\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1372\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1373\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1374\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1375\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1376\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1377\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1378\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1379\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1380\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1381\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1382\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1383\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1384\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1385\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1386\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1387\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1388\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1389\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1390\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1391\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1392\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1393\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1394\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1395\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1396\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1397\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1398\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1399\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1400\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1401\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1402\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1403\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1404\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1405\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1406\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1407\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1408\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1409\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1410\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1411\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1412\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1413\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1414\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1415\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1416\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1417\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1418\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1419\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1420\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1421\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1422\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1423\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1424\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1425\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1426\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1427\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1428\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1429\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1430\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1431\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1432\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1433\n",
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      "1478\n",
      "ATrue\n",
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      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1479\n",
      "ATrue\n",
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      "do_stop:True\n",
      "1480\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "False\n",
      "Loss:0.04616083577275276\n",
      "Epoch 3 started at 12:56:07\n",
      "1565\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
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      "1572\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1573\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
      "do_stop:True\n",
      "1576\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
      "1586\n",
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      "BTrue\n",
      "do_stop:True\n",
      "1587\n",
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      "BTrue\n",
      "do_stop:True\n",
      "1588\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1589\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1590\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1591\n",
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      "BTrue\n",
      "do_stop:True\n",
      "1592\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1593\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1595\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "1600\n",
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      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1664\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "1764\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1765\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1766\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1767\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1768\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1769\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1770\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1771\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1772\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1773\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1774\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1775\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1776\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1777\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1778\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1779\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1780\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1781\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1782\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1783\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1784\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1785\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1786\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1787\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1788\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1789\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1790\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1791\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1792\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1793\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1794\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1795\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1796\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1797\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1798\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1799\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1800\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1801\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1802\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1803\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1804\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1805\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1806\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1807\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1808\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1809\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1810\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1811\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1812\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1813\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1814\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1815\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1816\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1817\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1818\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1819\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1820\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1821\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1822\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1823\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1824\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1825\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1826\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1827\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1828\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1829\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1830\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1831\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1832\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1833\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1834\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1835\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1836\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1837\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1838\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1839\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1840\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1841\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1842\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1843\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1844\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1845\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1846\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1847\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1848\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1849\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1850\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1851\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1852\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1853\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1854\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1855\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1856\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1857\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1858\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1859\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1860\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1861\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1862\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1863\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1864\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1865\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1866\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1867\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1868\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1869\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1870\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1871\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1872\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1873\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1874\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1875\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1876\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1877\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1878\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1879\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1880\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1881\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1882\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1883\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1884\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1885\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1886\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1887\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1888\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1889\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1890\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1891\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1892\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1893\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1894\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1895\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1896\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1897\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1898\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1899\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1900\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1901\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1902\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1903\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1904\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1905\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1906\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1907\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1908\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1909\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1910\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1911\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1912\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1913\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1914\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1915\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1916\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1917\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1918\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1919\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1920\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1921\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1922\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1923\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1924\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1925\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1926\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1927\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1928\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1929\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1930\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1931\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1932\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1933\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1934\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1935\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1936\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1937\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1938\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1939\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1940\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1941\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1942\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1943\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1944\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1945\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1946\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1947\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1948\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1949\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1950\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1951\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1952\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1953\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1954\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1955\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1956\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1957\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1958\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1959\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1960\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1961\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1962\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1963\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1964\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1965\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1966\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1967\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1968\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1969\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1970\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1971\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1972\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1973\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1974\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1975\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1976\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1977\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1978\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1979\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1980\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1981\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1982\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1983\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1984\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1985\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1986\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1987\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1988\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1989\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1990\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1991\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1992\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1993\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1994\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1995\n",
      "ATrue\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTrue\n",
      "do_stop:True\n",
      "1996\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1997\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1998\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "1999\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2000\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2001\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2002\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2003\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2004\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2005\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2006\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2007\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2008\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2009\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2010\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2011\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2012\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2013\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2014\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2015\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2016\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2017\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2018\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2019\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2020\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2021\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2022\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2023\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2024\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2025\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2026\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2027\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2028\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2029\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2030\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2031\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2032\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2033\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2034\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2035\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2036\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2037\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2038\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2039\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2040\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2041\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2042\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2043\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2044\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2045\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2046\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2047\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2048\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2049\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2050\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2051\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2052\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2053\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2054\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2055\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2056\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2057\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2058\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2059\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2060\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2061\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2062\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2063\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2064\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2065\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2066\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2067\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2068\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2069\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2070\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2071\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2072\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2073\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2074\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2075\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2076\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2077\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2078\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2079\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2080\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2081\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2082\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2083\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2084\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2085\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2086\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2087\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2088\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2089\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2090\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2091\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2092\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2093\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2094\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2095\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2096\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2097\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2098\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2099\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2100\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2101\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2102\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2103\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2104\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2105\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2106\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2107\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2108\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2109\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2110\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2111\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2112\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2113\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2114\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2115\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2116\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2117\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2118\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2119\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2120\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2121\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2122\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2123\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2124\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2125\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2126\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2127\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2128\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2129\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2130\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2131\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2132\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2133\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2134\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2135\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2136\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2137\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2138\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2139\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2140\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2141\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2142\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2143\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2144\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2145\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2146\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2147\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2148\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2149\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2150\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2151\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2152\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2153\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2154\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2155\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2156\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2157\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2158\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2159\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2160\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2161\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2162\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2163\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2164\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2165\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2166\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2167\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2168\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2169\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2170\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2171\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2172\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2173\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2174\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2175\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2176\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2177\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2178\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2179\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2180\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2181\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2182\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2183\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2184\n",
      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2300\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "False\n",
      "Loss:0.011079258285462856\n",
      "Epoch 4 started at 12:56:09\n",
      "2347\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2348\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2349\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2350\n",
      "ATrue\n",
      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "2353\n",
      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "2356\n",
      "ATrue\n",
      "BTrue\n",
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      "2357\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2358\n",
      "ATrue\n",
      "BTrue\n",
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      "2359\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2360\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2361\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2362\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "2364\n",
      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "2367\n",
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      "BTrue\n",
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      "2368\n",
      "ATrue\n",
      "BTrue\n",
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      "2369\n",
      "ATrue\n",
      "BTrue\n",
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      "2370\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2371\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2372\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2373\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2374\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2375\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2376\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2377\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2378\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2379\n",
      "ATrue\n",
      "BTrue\n",
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      "2380\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "2382\n",
      "ATrue\n",
      "BTrue\n",
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      "2383\n",
      "ATrue\n",
      "BTrue\n",
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      "2384\n",
      "ATrue\n",
      "BTrue\n",
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      "2385\n",
      "ATrue\n",
      "BTrue\n",
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      "2386\n",
      "ATrue\n",
      "BTrue\n",
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      "2387\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2388\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2389\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2390\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2391\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2480\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2516\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2517\n",
      "ATrue\n",
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      "2518\n",
      "ATrue\n",
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      "2519\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2520\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2521\n",
      "ATrue\n",
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      "2522\n",
      "ATrue\n",
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      "2523\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2524\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2525\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2526\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2527\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2528\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2529\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2530\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2531\n",
      "ATrue\n",
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      "2532\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2533\n",
      "ATrue\n",
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      "2534\n",
      "ATrue\n",
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      "2535\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2536\n",
      "ATrue\n",
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      "2537\n",
      "ATrue\n",
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      "2538\n",
      "ATrue\n",
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      "2539\n",
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      "2540\n",
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      "do_stop:True\n",
      "2541\n",
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      "2542\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2543\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2544\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2545\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2546\n",
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      "2547\n",
      "ATrue\n",
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      "2548\n",
      "ATrue\n",
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      "2549\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2550\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2551\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2552\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2553\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2554\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2555\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2556\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2557\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2558\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2559\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2560\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2561\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2562\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2563\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2564\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2565\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2566\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2567\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2568\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2569\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2570\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2571\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2572\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2573\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2574\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2575\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2576\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2577\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2578\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2579\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2580\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2581\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2582\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2583\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2584\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2585\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2586\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2587\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2588\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2589\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2590\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2591\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2592\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2593\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2594\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2595\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2596\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2597\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2598\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2599\n",
      "ATrue\n",
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      "do_stop:True\n",
      "2600\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2601\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2602\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2603\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2604\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2605\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2606\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2607\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2608\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2609\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2610\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2611\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2612\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2613\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2614\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2615\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2616\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2617\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2618\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2619\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2620\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2621\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2622\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2623\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2624\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2625\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2626\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2627\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2628\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2629\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2630\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2631\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2632\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2633\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2634\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2635\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2636\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2637\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2638\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2639\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2640\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2641\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2642\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2643\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2644\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2645\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2646\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2647\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2648\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2649\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2650\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2651\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2652\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2653\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2654\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2655\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2656\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2657\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2658\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2659\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2660\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2661\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2662\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2663\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2664\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2665\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2666\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2667\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2668\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2669\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2670\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2671\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2672\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2673\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2674\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2675\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2676\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2677\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2678\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2679\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2680\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2681\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2682\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2683\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2684\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2685\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2686\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2687\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2688\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2689\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2690\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2691\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2692\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2693\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2694\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2695\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2696\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2697\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2698\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2699\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2700\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2701\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2702\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2703\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2704\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2705\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2706\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2707\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2708\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2709\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2710\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2711\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2712\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2713\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2714\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2715\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2716\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2717\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2718\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2719\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2720\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2721\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2722\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2723\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2724\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2725\n",
      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2755\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2756\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2757\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2758\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "2759\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "2885\n",
      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "2893\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "2913\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
      "2933\n",
      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
      "3030\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3031\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "3032\n",
      "ATrue\n",
      "BTrue\n",
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      "do_stop:True\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "False\n",
      "Loss:0.021525789052248\n",
      "Epoch 5 started at 12:56:12\n",
      "3129\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "3130\n",
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      "BTrue\n",
      "do_stop:True\n",
      "3131\n",
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      "BTrue\n",
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      "3132\n",
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      "BTrue\n",
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      "3133\n",
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      "BTrue\n",
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      "3134\n",
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      "BTrue\n",
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      "3135\n",
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      "BTrue\n",
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      "3136\n",
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      "BTrue\n",
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      "3137\n",
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      "BTrue\n",
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      "3138\n",
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      "3139\n",
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      "3141\n",
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      "3142\n",
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      "3143\n",
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      "3144\n",
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      "BTrue\n",
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      "3145\n",
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      "BTrue\n",
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      "3146\n",
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      "BTrue\n",
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      "3147\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "BTrue\n",
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      "3149\n",
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      "BTrue\n",
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      "3150\n",
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      "BTrue\n",
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      "3151\n",
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      "BTrue\n",
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      "3152\n",
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      "BTrue\n",
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      "3153\n",
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      "BTrue\n",
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      "3154\n",
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      "BTrue\n",
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      "3155\n",
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      "BTrue\n",
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      "3156\n",
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      "BTrue\n",
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      "3157\n",
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      "BTrue\n",
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      "3158\n",
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      "3159\n",
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      "3160\n",
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      "BTrue\n",
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      "3169\n",
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      "3170\n",
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      "3173\n",
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      "3175\n",
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      "3182\n",
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      "3200\n",
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      "3216\n",
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      "BTrue\n",
      "do_stop:True\n",
      "3217\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3218\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "3219\n",
      "ATrue\n",
      "BTrue\n",
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      "ATrue\n",
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      "3407\n",
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      "do_stop:True\n",
      "3409\n",
      "ATrue\n",
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      "3420\n",
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      "3421\n",
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      "3422\n",
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      "3423\n",
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      "3424\n",
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      "3425\n",
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      "3426\n",
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      "3427\n",
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      "3428\n",
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      "3429\n",
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      "3430\n",
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      "3431\n",
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      "3436\n",
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      "3437\n",
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      "3438\n",
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      "3439\n",
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      "3440\n",
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      "3448\n",
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      "3449\n",
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      "do_stop:True\n",
      "3450\n",
      "ATrue\n",
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      "3451\n",
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      "do_stop:True\n",
      "3452\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3453\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3454\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3455\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3456\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3457\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3458\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3459\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3460\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3461\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3462\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3463\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3464\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3465\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3466\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3467\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3468\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3469\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3470\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3471\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3472\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3473\n",
      "ATrue\n",
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      "do_stop:True\n",
      "3474\n",
      "ATrue\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
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      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "3558\n",
      "ATrue\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTrue\n",
      "do_stop:True\n",
      "3559\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "3560\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
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      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3884\n",
      "ATrue\n",
      "BTrue\n",
      "do_stop:True\n",
      "3885\n",
      "ATrue\n",
      "BTrue\n",
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      "do_stop:True\n",
      "3891\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
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      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "BFalse\n",
      "do_stop:False\n",
      "False\n",
      "Loss:0.00036046066088601947\n"
     ]
    }
   ],
   "source": [
    "fit(5, learn, cb=CallbackHandler([Timecheck(), PrintLoss(), TestCallback()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 started at 13:15:34\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "Loss:0.0183029193431139\n",
      "Epoch 2 started at 13:15:34\n",
      "11\n",
      "Loss:0.00747452350333333\n",
      "Epoch 3 started at 13:15:34\n",
      "12\n",
      "Loss:0.012809413485229015\n",
      "Epoch 4 started at 13:15:34\n",
      "13\n",
      "Loss:0.03685833513736725\n",
      "Epoch 5 started at 13:15:34\n",
      "14\n",
      "Loss:0.00236335676163435\n"
     ]
    }
   ],
   "source": [
    "fit(5, learn, cb=CallbackHandler([Timecheck(), PrintLoss(), TestCallback()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TestCallback just prints the no of iterations and stops training when n_iter>10(sets self.learn.stop=True). However training still continues to go on because we never check for sel.learn.stop=True here. \n",
    "Now as to the 11-14 that is printed when training is due to the fact that we print the value of n_iter in TestCallback BEFORE incrementing it followed by stopping it.  \n",
    "There is no training going on(see the timestamps), python just keeps jumping in and out of the all_batches and one_batch functions because there's nothing stopping it to not do so. However as self.learn.stop is True it just prints and then comes out. Also note that there are 782 and 157 batches for train_dl and valid_dl respectively.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is roughly how fastai does it now (except that the handler can also change and return `xb`, `yb`, and `loss`). But let's see if we can make things simpler and more flexible, so that a single class has access to everything and can change anything at any time. The fact that we're passing `cb` to so many functions is a strong hint they should all be in the same class!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Runner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import re\n",
    "\n",
    "_camel_re1 = re.compile('(.)([A-Z][a-z]+)')\n",
    "_camel_re2 = re.compile('([a-z0-9])([A-Z])')\n",
    "def camel2snake(name):\n",
    "    s1 = re.sub(_camel_re1, r'\\1_\\2', name)\n",
    "    #print(s1)\n",
    "    return re.sub(_camel_re2, r'\\1_\\2', s1).lower()\n",
    "\n",
    "class Callback():\n",
    "    _order = 0 # this way we can make sure that some things run after other things.\n",
    "    def set_runner(self, run): self.run = run\n",
    "    def __getattr__(self, k): return getattr(self.run, k)\n",
    "    @property\n",
    "    def name(self):\n",
    "        name = re.sub(r'Callback$', '', self.__class__.__name__)\n",
    "        return camel2snake(name or 'callback')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'train_eval_callback'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbname = 'TrainEvalCallback'\n",
    "camel2snake(cbname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'train_eval'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TrainEvalCallback().name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We inherit from a new callback class but we don't have to anymore. The main reason why is that our callback class now has a _order which we can choose what order are the callabcks running."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'__getattr__' is only called by python if it can't find the attribute that you asked for. In this case, very often the thing you actually want in the callback is inside the runner class which we store away as self.run; so this that means in all of our callbacks, you can basically use self.pretty_much_everything(which exists in runner) and it will grab what you want even though most of the stuff you want will be inside Runner.  \n",
    "    \n",
    "This pattern is present throughout the fastai library; when one object contains another object we very often delegate getattr to the other object. So for eg if you're looking at dataset then fastai delegates to x."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This first callback is reponsible to switch the model back and forth in training or validation mode, as well as maintaining a count of the iterations, or the percentage of iterations elapsed in the epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class TrainEvalCallback(Callback):\n",
    "    def begin_fit(self):\n",
    "        self.run.n_epochs = 0.\n",
    "        self.run.n_iter = 0\n",
    "        \n",
    "    def after_batch(self):\n",
    "        if not self.in_train: return\n",
    "        self.run.n_epochs += 1./self.iters\n",
    "        self.run.n_iter += 1\n",
    "        \n",
    "    def begin_epoch(self):\n",
    "        self.run.n_epochs = self.epoch\n",
    "        self.model.train()\n",
    "        self.run.in_train=True\n",
    "        \n",
    "    def begin_validate(self):\n",
    "        self.model.eval()\n",
    "        self.run.in_train=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also re-create our TestCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class TestCallback(Callback):\n",
    "    _order = 1 # because we're using the n_iter variable from TrainEvalCallback and so that class has to run first\n",
    "    def after_step(self):\n",
    "        if self.n_iter >=10: return True #stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from typing import *\n",
    "\n",
    "def listify(o):\n",
    "    if o is None: return []\n",
    "    if isinstance(o, list): return o\n",
    "    if isinstance(o,str): return [o]\n",
    "    if isinstance(o, Iterable): return list(o)\n",
    "    return [o]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The isinstance() function returns True if the specified object is of the specified type, otherwise False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Runner() any cb_fns that you pass in which it uses to create new callbacks, it actually assigns them to an attribute with that name(setattr part). So we'll now hav something called Runner.train_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Runner():\n",
    "    def __init__(self, cbs=None, cb_funcs=None):\n",
    "        cbs = listify(cbs)\n",
    "        for cbf in listify(cb_funcs):\n",
    "            cb = cbf()\n",
    "            setattr(self, cb.name, cb)\n",
    "            cbs.append(cb)\n",
    "        self.stop, self.cbs = False, [TrainEvalCallback()]+ cbs\n",
    "        \n",
    "    @property\n",
    "    def opt(self): return self.learn.opt\n",
    "    \n",
    "    @property\n",
    "    def model(self): return self.learn.model\n",
    "    \n",
    "    @property\n",
    "    def loss_func(self): return self.learn.loss_func\n",
    "    \n",
    "    @property\n",
    "    def data(self): return self.learn.data\n",
    "    \n",
    "    def one_batch(self, xb, yb):\n",
    "        self.xb, self.yb = xb, yb\n",
    "        if self('begin_batch'): return\n",
    "        self.pred = self.model(self.xb)\n",
    "        if self('after_pred'): return\n",
    "        self.loss = self.loss_func(self.pred, self.yb)\n",
    "        if self('after_loss') or not self.in_train: return\n",
    "        self.loss.backward()\n",
    "        if self('after_backward'): return\n",
    "        self.opt.step()\n",
    "        if self('after_step'): return\n",
    "        self.opt.zero_grad()\n",
    "        \n",
    "    def all_batches(self, dl):\n",
    "        self.iters = len(dl)\n",
    "        for xb, yb in dl:\n",
    "            if self.stop: break\n",
    "            self.one_batch(xb, yb)\n",
    "            self('after_batch')\n",
    "        self.stop= False\n",
    "        \n",
    "    def fit(self, epochs, learn):\n",
    "        self.epochs, self.learn = epochs, learn\n",
    "        try:\n",
    "            for cb in self.cbs: cb.set_runner(self) # tell each of our callbacks what runner we're working with.\n",
    "            if self('begin_fit'): return\n",
    "            for epoch in range(epochs):\n",
    "                self.epoch = epoch\n",
    "                if not self('begin_epoch'): self.all_batches(self.data.train_dl)\n",
    "                    \n",
    "                with torch.no_grad():\n",
    "                    if not self('begin_validate'): self.all_batches(self.data.valid_dl)\n",
    "                if self('after_epoch'): break\n",
    "                \n",
    "        finally:\n",
    "            self('after_fit')\n",
    "            self.learn = None\n",
    "        \n",
    "    def __call__(self, cb_name):\n",
    "        for cb in sorted(self.cbs, key =lambda x: x._order):\n",
    "            f = getattr(cb, cb_name, None) # look inside cb object and try to find something of this name(cb_name) & default to None if you can't find it.\n",
    "            # that way you don't have to inherit from a generic callback class. In python None means false so your training will go on without any disturbance.\n",
    "            if f and f(): return True # if you find it then you can call it(the 'f()' part)\n",
    "        return False               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#export\n",
    "class AvgStats():\n",
    "    def __init__(self, metrics, in_train): self.metrics, self.in_train = listify(metrics), in_train\n",
    "    \n",
    "    def reset(self):\n",
    "        self.tot_loss, self.count = 0., 0\n",
    "        self.tot_mets = [0.] * len(self.metrics)\n",
    "        \n",
    "    @property\n",
    "    def all_stats(self): return [self.tot_loss.item()] + self.tot_mets\n",
    "    \n",
    "    @property\n",
    "    # goes through all of the losses & metrics and return the average\n",
    "    def avg_stats(self): return [o/self.count for o in self.all_stats]\n",
    "    \n",
    "    def __repr__(self):\n",
    "        if not self.count: return \"\"\n",
    "        return f\"{'train' if self.in_train else 'valid'}: {self.avg_stats}\"\n",
    "    \n",
    "    def accumulate(self, run):\n",
    "        bn = run.xb.shape[0]\n",
    "        self.tot_loss += run.loss * bn #adds up the total loss\n",
    "        self.count += bn\n",
    "        for i,m in enumerate(self.metrics):\n",
    "            self.tot_mets[i] += m(run.pred, run.yb) * bn# adds up the total metric for each metric\n",
    "\n",
    "class AvgStatsCallback(Callback):\n",
    "    def __init__(self, metrics):\n",
    "        self.train_stats, self.valid_stats = AvgStats(metrics,True), AvgStats(metrics, False)\n",
    "        \n",
    "    def begin_epoch(self):\n",
    "        self.train_stats.reset()\n",
    "        self.valid_stats.reset()\n",
    "        \n",
    "    def after_loss(self):\n",
    "        stats = self.train_stats if self.in_train else self.valid_stats\n",
    "        with torch.no_grad(): stats.accumulate(self.run)\n",
    "            \n",
    "    def after_epoch(self):\n",
    "        print(self.train_stats)\n",
    "        print(self.valid_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(*get_model(data), loss_func, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stats = AvgStatsCallback([accuracy])\n",
    "run = Runner(cbs=stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [0.321871328125, tensor(0.9016)]\n",
      "valid: [0.341895849609375, tensor(0.8816)]\n",
      "train: [0.1474548046875, tensor(0.9553)]\n",
      "valid: [0.12870633544921875, tensor(0.9624)]\n"
     ]
    }
   ],
   "source": [
    "run.fit(2, learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.12870633544921875, tensor(0.9624))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss, acc = stats.valid_stats.avg_stats\n",
    "assert acc>0.9\n",
    "loss, acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_cbf = partial(AvgStatsCallback, accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [0.110805341796875, tensor(0.9662)]\n",
      "valid: [0.1316048095703125, tensor(0.9606)]\n"
     ]
    }
   ],
   "source": [
    "run = Runner(cb_funcs = acc_cbf)\n",
    "run.fit(1, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Jupyter means we can get tab-completion even for dynamic code like this! :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1316048095703125, tensor(0.9606)]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.avg_stats.valid_stats.avg_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 04_callbacks.ipynb to exp\\nb_04.py\n"
     ]
    }
   ],
   "source": [
    "!python notebook2script.py 04_callbacks.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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