teacher-CenteroutTraining.ipynb

{
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   "source": [
    "import math\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GLr3eNbvKDjz"
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   "source": [
    "def createCenterOutTargets(r, N_TARGETS):\n",
    "    target = torch.empty(N_TARGETS, 2)\n",
    "    for i in range(N_TARGETS):\n",
    "        target[i,0] = r * np.cos(np.pi*2*i/N_TARGETS)\n",
    "        target[i,1] = r * np.sin(np.pi*2*i/N_TARGETS)\n",
    "    return target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "source": [
    "# Hyperparameters\n",
    "# Note: when MIN_DELAY and MAX_DELAY are too short compared to SEQLEN, \n",
    "# the network couldn't learn to hold. But if they are too long, it couldn't reach\n",
    "SCREEN_SIZE = 20\n",
    "INPUT_SIZE = 7\n",
    "OUT_SIZE = 2\n",
    "PADDING = 2\n",
    "N_NEURONS = 256\n",
    "MIN_DELAY = 30\n",
    "MAX_DELAY = 60\n",
    "MOVE_TARG_THRES = 2\n",
    "HOLD_TARG_THRES = 1\n",
    "\n",
    "PROP_GAIN = 0.2\n",
    "\n",
    "SEQLEN = 500\n",
    "N_TRAIN_EPOCHS = 21\n",
    "N_TRAIN_STEPS = 2500\n",
    "PRINT_EVERY=100\n",
    "\n",
    "# Alphas and betas determine how much the teacher signal guides the learning of the neural network\n",
    "ALPHAS = [0.8 ** k for k in range(N_TRAIN_EPOCHS)]\n",
    "BETAS = [a / 2 for a in ALPHAS]\n",
    "\n",
    "LEARNING_RATE = 1e-6\n",
    "\n",
    "ACC_PENALTY = 5\n",
    "\n",
    "# Define target locations for the pinball task and for the centerout task\n",
    "TARGET_SEQUENCE = PADDING + np.random.rand(20, 2) * (SCREEN_SIZE - 2 * PADDING)\n",
    "RADIUS = 5\n",
    "N_TARGETS_CO = 8\n",
    "TARGET_SEQUENCE_CO = SCREEN_SIZE/2 + createCenterOutTargets(RADIUS, N_TARGETS_CO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AzJSUCtyKDj6"
   },
   "outputs": [],
   "source": [
    "if not os.path.isdir('./CenteroutModelResults-{}Epochs'.format(N_TRAIN_EPOCHS)):\n",
    "    os.mkdir('./CenteroutModelResults-{}Epochs'.format(N_TRAIN_EPOCHS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XRoX8dnQKDj9"
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   "outputs": [],
   "source": [
    "## Defines the RNN.\n",
    "class MonkRNN(nn.Module):\n",
    "    def __init__(self, inp_size, n_neurons, out_size):\n",
    "        super(MonkRNN, self).__init__()\n",
    "\n",
    "        self.inp_size = inp_size\n",
    "        self.out_size = out_size\n",
    "        self.n_neurons = n_neurons\n",
    "        self.init_hidden_state = nn.Parameter(torch.zeros(1, n_neurons))\n",
    "\n",
    "        # Inputs are:\n",
    "        #  (go, hand_x, hand_y, curr_tgx, curr_tgy, next_tgx, next_tgy)\n",
    "        self.inp_layer = nn.Sequential(nn.Linear(inp_size, n_neurons), nn.ReLU())\n",
    "        \n",
    "        self.teach_inp_layer = nn.Linear(2, n_neurons)\n",
    "        self.teach_inp_layer.weight.data.normal_(0.0, 5 / np.sqrt(n_neurons))\n",
    "        \n",
    "        # Set teacher weights to constant.\n",
    "        for p in self.teach_inp_layer.parameters():\n",
    "            p.requires_grad = False\n",
    "        \n",
    "        # Recurrent layer parameters.\n",
    "#         self.rnn = nn.GRUCell(n_neurons, n_neurons)\n",
    "        self.rnn = nn.RNNCell(input_size=n_neurons,hidden_size=n_neurons,nonlinearity='relu')\n",
    "        self.rnn.weight_hh.data.normal_(0, 2 / np.sqrt(n_neurons))\n",
    "\n",
    "        # Output layer parameters\n",
    "        self.out_layer = nn.Sequential(nn.Linear(n_neurons, n_neurons), nn.ReLU(), nn.Linear(n_neurons, out_size))\n",
    "        self.out_layer[0].weight.data.normal_(0, 1.5 / np.sqrt(n_neurons))\n",
    "        self.out_layer[2].weight.data.normal_(0, 1.5 / np.sqrt(n_neurons))\n",
    "        \n",
    "        # Strength of teacher input.\n",
    "        self.alpha = 1.0\n",
    "\n",
    "    def forward(self, inp, teach_inp, hidden=None):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        inp : torch.tensor\n",
    "            Hand and target positions. Has shape (7,).\n",
    "            (go, hand_x, hand_y, curr_tgx, curr_tgy, next_tgx, next_tgy)\n",
    "        hidden : torch.tensor\n",
    "            Initial firing rates. Has shape (n_neurons,)\n",
    "        task_info : torch.tensor\n",
    "            tensor holding (go, curr_tgx, curr_tgy, next_tgx, next_tgy)\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        acc : torch.tensor\n",
    "            has shape (2,) corresponding to x and y acceleration.\n",
    "        hiddens : torch.tensor\n",
    "            has shape (n_neurons,) corresponding network activity.\n",
    "        \"\"\"\n",
    "\n",
    "        if hidden is None:\n",
    "            hidden = self.init_hidden_state\n",
    "\n",
    "        # Update RNN one time step.\n",
    "        fx = self.inp_layer(inp) + self.alpha * self.teach_inp_layer(teach_inp) # Feed input to the input layer.\n",
    "        h_new = self.rnn(fx, hidden)\n",
    "\n",
    "        # Collect RNN output (acceleration of hand).\n",
    "        acc = self.out_layer(h_new)\n",
    "        # print(\"hidden {}, Acc {}\".format(h_new, acc))\n",
    "        return acc, h_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
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   "source": [
    "## The task class: tracks hand location, monitors is hand reaches the target, and controls the task to switch between delay period and movement period.\n",
    "class PinballTask(nn.Module): \n",
    "    def __init__(self, hand, screen_size, min_delay, max_delay, move_targ_thres, hold_targ_thres, padding):\n",
    "        super(PinballTask, self).__init__()\n",
    "        self.screen_size = screen_size\n",
    "        self.padding = padding\n",
    "        self.move_targ_thres = move_targ_thres\n",
    "        self.hold_targ_thres = hold_targ_thres\n",
    "\n",
    "        self.delay_left = np.random.uniform(min_delay, max_delay)\n",
    "        self.delay = self.delay_left\n",
    "        self.min_delay = min_delay\n",
    "        self.max_delay = max_delay\n",
    "\n",
    "        self.go = torch.tensor([False])\n",
    "        \n",
    "        hx, hy = hand\n",
    "        self.curr_tgx = hx\n",
    "        self.curr_tgy = hy\n",
    "#         self.next_tgx = padding + torch.rand(1) * (screen_size - 2 * padding)\n",
    "#         self.next_tgy = padding + torch.rand(1) * (screen_size - 2 * padding)\n",
    "        self.target_index = 0\n",
    "        self.next_tgx = TARGET_SEQUENCE[0, 0]\n",
    "        self.next_tgy = TARGET_SEQUENCE[0, 1]\n",
    "\n",
    "    def forward(self, hand):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        hand : torch.tensor\n",
    "            Hand positions and velocities\n",
    "        \"\"\"\n",
    "\n",
    "        # Compute distances to targets\n",
    "        hx, hy = hand\n",
    "        dist_to_curr = torch.sqrt(\n",
    "            (hx - self.curr_tgx) ** 2 + (hy - self.curr_tgy) ** 2)\n",
    "        dist_to_next = torch.sqrt(\n",
    "            (hx - self.next_tgx) ** 2 + (hy - self.next_tgy) ** 2)\n",
    "\n",
    "        # Reach period\n",
    "        if self.go:\n",
    "            # assert False\n",
    "\n",
    "            # Check for target acquired\n",
    "            if dist_to_next < self.move_targ_thres:\n",
    "                self.go = torch.tensor([False])\n",
    "                #self.target_index += 1\n",
    "                self.target_index = torch.randint(low = 0, high = len(TARGET_SEQUENCE), size = (1,1))\n",
    "                self.curr_tgx = self.next_tgx\n",
    "                self.curr_tgy = self.next_tgy\n",
    "                self.next_tgx = TARGET_SEQUENCE[self.target_index, 0]\n",
    "                self.next_tgy = TARGET_SEQUENCE[self.target_index, 1]\n",
    "#                 self.next_tgx = self.padding + torch.rand(1) * (self.screen_size - 2 * self.padding)\n",
    "#                 self.next_tgy = self.padding + torch.rand(1) * (self.screen_size - 2 * self.padding)\n",
    "                self.delay_left = np.random.uniform(self.min_delay, self.max_delay)\n",
    "\n",
    "        # Delay / Hold period\n",
    "        else:\n",
    "            # Check if hold is violated\n",
    "            if dist_to_curr >= self.hold_targ_thres:\n",
    "                self.delay_left = np.random.uniform(self.min_delay, self.max_delay)\n",
    "            else:\n",
    "                self.delay_left -=1\n",
    "            \n",
    "            # Check if delay is done.\n",
    "            if self.delay_left <= 0:\n",
    "                self.go = torch.tensor([True])\n",
    "                \n",
    "        return torch.tensor([\n",
    "            self.go,\n",
    "            hx,\n",
    "            hy,\n",
    "            self.curr_tgx,\n",
    "            self.curr_tgy,\n",
    "            self.next_tgx,\n",
    "            self.next_tgy\n",
    "        ])[None, :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "loWhYr31KDkB"
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   "source": [
    "class CenterOutTask(nn.Module):\n",
    "\n",
    "    def __init__(self, hand, screen_size, min_delay, max_delay, move_targ_thres, hold_targ_thres, padding):\n",
    "        super(CenterOutTask, self).__init__()\n",
    "        self.screen_size = screen_size\n",
    "        self.padding = padding\n",
    "        self.move_targ_thres = move_targ_thres\n",
    "        self.hold_targ_thres = hold_targ_thres\n",
    "\n",
    "        self.delay_left = np.random.uniform(min_delay, max_delay)\n",
    "        self.delay = self.delay_left\n",
    "        self.min_delay = min_delay\n",
    "        self.max_delay = max_delay\n",
    "\n",
    "        self.go = torch.tensor([False])\n",
    "        \n",
    "        hx, hy = hand\n",
    "        self.curr_tgx = torch.tensor([SCREEN_SIZE / 2])\n",
    "        self.curr_tgy = torch.tensor([SCREEN_SIZE / 2])\n",
    "        self.target_index = 0\n",
    "        self.next_tgx = TARGET_SEQUENCE_CO[0, 0]\n",
    "        self.next_tgy = TARGET_SEQUENCE_CO[0, 1]\n",
    "\n",
    "    def forward(self, hand):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        hand : torch.tensor\n",
    "            Hand positions and velocities\n",
    "        \"\"\"\n",
    "\n",
    "        # Compute distances to targets\n",
    "        hx, hy = hand\n",
    "        dist_to_curr = torch.sqrt(\n",
    "            (hx - self.curr_tgx) ** 2 + (hy - self.curr_tgy) ** 2)\n",
    "        dist_to_next = torch.sqrt(\n",
    "            (hx - self.next_tgx) ** 2 + (hy - self.next_tgy) ** 2)\n",
    "\n",
    "        # Reach period\n",
    "        if self.go:\n",
    "            # assert False\n",
    "\n",
    "            # Check for target acquired\n",
    "            if dist_to_next < self.move_targ_thres:\n",
    "                self.go = torch.tensor([False])\n",
    "                #self.target_index += 1\n",
    "                self.target_index = torch.randint(low = 0, high = len(TARGET_SEQUENCE_CO), size = (1,1))\n",
    "                hx = torch.tensor([SCREEN_SIZE / 2])\n",
    "                hy = torch.tensor([SCREEN_SIZE / 2])\n",
    "                self.next_tgx = TARGET_SEQUENCE_CO[self.target_index, 0]\n",
    "                self.next_tgy = TARGET_SEQUENCE_CO[self.target_index, 1]\n",
    "                self.delay_left = np.random.uniform(self.min_delay, self.max_delay)\n",
    "\n",
    "        # Delay / Hold period\n",
    "        else:\n",
    "            # Check if hold is violated\n",
    "            if dist_to_curr >= self.hold_targ_thres:\n",
    "                self.delay_left = np.random.uniform(self.min_delay, self.max_delay)\n",
    "            else:\n",
    "                self.delay_left -=1\n",
    "            \n",
    "            # Check if delay is done.\n",
    "            if self.delay_left <= 0:\n",
    "                self.go = torch.tensor([True])\n",
    "                \n",
    "        return torch.tensor([\n",
    "            self.go,\n",
    "            hx,\n",
    "            hy,\n",
    "            self.curr_tgx,\n",
    "            self.curr_tgy,\n",
    "            self.next_tgx,\n",
    "            self.next_tgy\n",
    "        ])[None, :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QkKmNZ-7KDkE"
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   "outputs": [],
   "source": [
    "## Compute losses based on hand location and the smoothness (penalize abrupt hand acceleration) of movement\n",
    "class PinballCriterion(nn.Module):\n",
    "\n",
    "    def __init__(self, acc_penalty):\n",
    "        super(PinballCriterion, self).__init__()\n",
    "        self.acc_penalty = acc_penalty\n",
    "\n",
    "    def get_active_target(self, inp_hist):\n",
    "        \"\"\"Compute the active target at each timestep.\"\"\"\n",
    "        go = inp_hist[:, 0]\n",
    "        curr_targ = inp_hist[:, 3:5]\n",
    "        next_targ = inp_hist[:, 5:7]\n",
    "        return curr_targ * (1 - go[:, None]) + next_targ * go[:, None]\n",
    "        \n",
    "    def target_loss(self, inp_hist):\n",
    "        \"\"\"Computes loss between hand and active target.\"\"\"\n",
    "        active_targ = self.get_active_target(inp_hist)\n",
    "        return torch.mean((inp_hist[:, 1:3] - active_targ) ** 2)\n",
    "\n",
    "    def acc_loss(self, acc_hist):\n",
    "        \"\"\"Computes penalty on the acceleration.\"\"\"\n",
    "        return self.acc_penalty * torch.mean(acc_hist ** 2)\n",
    "    \n",
    "    def forward(self, inp_hist, acc_hist):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        inp_hist : torch.tensor\n",
    "            has shape (n_timesteps, 4) corresponding to (hx, hy, vx, vy) at each timepoint.\n",
    "        acc_hist : torch.tensor\n",
    "            has shape (n_timesteps, 2) corresponding to accelations\n",
    "        \"\"\"\n",
    "        target_loss = self.target_loss(inp_hist)\n",
    "        acc_loss = self.acc_loss(acc_hist)\n",
    "        return target_loss + acc_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Az9Hkx0eKDkG"
   },
   "outputs": [],
   "source": [
    "## Compute the teaching signal based on the difference between current hand acceleration and target hand acc (this is the vector pointing from current hand location to the target location).\n",
    "class TeacherHandDynamics(nn.Module):\n",
    "    def __init__(self, screen_size, friction=5.0, teacher_scale=1.0):\n",
    "        super(TeacherHandDynamics, self).__init__()\n",
    "        self.screen_size = screen_size\n",
    "        self.friction = friction\n",
    "        self.teacher_scale = teacher_scale\n",
    "\n",
    "    def initialize(self, hx, hy):\n",
    "        self.hx = torch.tensor([hx])\n",
    "        self.hy = torch.tensor([hy])\n",
    "        self.vx = torch.zeros(1)\n",
    "        self.vy = torch.zeros(1)\n",
    "        self.int_errx = 0.0\n",
    "        self.int_erry = 0.0\n",
    "\n",
    "    def forward(self, acc, targ_loc):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        acc : torch.tensor\n",
    "            has shape (2,) correspond to (ax, ay)\n",
    "        targ_loc : torch.tensor\n",
    "            location of the current target\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        hand : torch.tensor\n",
    "            has shape (2,) corresponding to (hx, hy).\n",
    "        \"\"\"\n",
    "        acc = acc.squeeze()\n",
    "        \n",
    "        hx = self.hx.item()\n",
    "        hy = self.hy.item()\n",
    "        tgx = targ_loc[0].item()\n",
    "        tgy = targ_loc[1].item()\n",
    "\n",
    "        # Teacher direction\n",
    "        errx = (tgx - hx)\n",
    "        erry = (tgy - hy)\n",
    "        self.int_errx += 0.9 * errx\n",
    "        self.int_erry += 0.9 * erry\n",
    "        \n",
    "        teach_x = 0.01 * self.int_errx + PROP_GAIN * errx\n",
    "        teach_y = 0.01 * self.int_erry + PROP_GAIN * erry\n",
    "\n",
    "        # Add teacher contribution\n",
    "        ax = teach_x * self.teacher_scale + acc[0] * (1 - self.teacher_scale)\n",
    "        ay = teach_y * self.teacher_scale + acc[1] * (1 - self.teacher_scale)\n",
    "\n",
    "        self.vx = self.vx / self.friction + ax\n",
    "        self.vy = self.vy / self.friction + ay\n",
    "        self.hx = torch.clamp(self.hx + self.vx, .1, self.screen_size - .1)\n",
    "        self.hy = torch.clamp(self.hy + self.vy, .1, self.screen_size - .1)\n",
    "        print(teach_x,teach_y,acc)\n",
    "        teacher_loss = torch.clamp((teach_x - acc[0]) ** 2 + (teach_y - acc[1]) ** 2,-10,10)\n",
    "\n",
    "        return (\n",
    "            torch.cat((self.hx, self.hy)),\n",
    "            teacher_loss,\n",
    "            torch.tensor([teach_x, teach_y]),\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c8jnAg41KDkI"
   },
   "outputs": [],
   "source": [
    "# Define transformations.\n",
    "rnn = MonkRNN(INPUT_SIZE, N_NEURONS, OUT_SIZE)\n",
    "hand_dynamics = TeacherHandDynamics(SCREEN_SIZE, teacher_scale=0.1)\n",
    "crit = PinballCriterion(ACC_PENALTY)\n",
    "# optimizer = torch.optim.Adam(rnn.parameters(), lr = LEARNING_RATE)\n",
    "optimizer = torch.optim.SGD(rnn.parameters(), lr=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DhmJkOsAKDkK"
   },
   "outputs": [],
   "source": [
    "# This section is written for continuing training from a trained RNN\n",
    "# rnn = torch.load('./CenteroutModelResults-20Epochs/MonkRNN_TrainedModel_CenteroutEpoch14.pt')\n",
    "# hand_dynamics = torch.load('./CenteroutModelResults-20Epochs/MonkRNN_TrainingHandDynamics_CenteroutEpoch14.pt')\n",
    "\n",
    "ALPHAS = [0.8 ** k for k in range(20)]\n",
    "BETAS = [a / 2 for a in ALPHAS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RYEOiJcyKDkN",
    "outputId": "d961524d-f9ea-48a5-931a-82f6de6df71c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.045 0.0 tensor([0., 0.])\n",
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      "-22.04895407187187 -24.390014906123245 tensor([5.9246e+29, 4.6274e+30], grad_fn=<SqueezeBackward0>)\n",
      "-22.13805406843864 -24.47911490269002 tensor([5.7796e+30, 2.3347e+30], grad_fn=<SqueezeBackward0>)\n",
      "-22.22715406500541 -24.568214899256795 tensor([2.5858e+30, 6.9950e+30], grad_fn=<SqueezeBackward0>)\n",
      "-22.316254061572184 -24.657314895823568 tensor([7.5781e+30, 4.4753e+30], grad_fn=<SqueezeBackward0>)\n",
      "-22.405354058138958 -24.746414892390344 tensor([3.9495e+30, 1.0352e+31], grad_fn=<SqueezeBackward0>)\n",
      "-22.494454054705727 -24.835514888957118 tensor([8.8179e+30, 8.4038e+30], grad_fn=<SqueezeBackward0>)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-8d041e169514>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     57\u001b[0m         \u001b[1;31m# Compute loss, and do backprop.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;31m#     loss = crit(inp_hist, acc_hist)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m             \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretain_graph\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     60\u001b[0m             \u001b[0mlosses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[0;32m    196\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[1;33m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m         \"\"\"\n\u001b[1;32m--> 198\u001b[1;33m         \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\autograd\\__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[0;32m     98\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[0;32m     99\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "## TRAIN NETWORK ##\n",
    "\n",
    "losses = []\n",
    "\n",
    "for epoch in range(N_TRAIN_EPOCHS):\n",
    "\n",
    "    hand_dynamics.teacher_scale = BETAS[epoch]\n",
    "    rnn.alpha = ALPHAS[epoch]\n",
    "    \n",
    "    for k in range(N_TRAIN_STEPS):\n",
    "\n",
    "        # randomize hand position (no velocity).\n",
    "        hand_dynamics.initialize(SCREEN_SIZE / 2, SCREEN_SIZE / 2)\n",
    "        hand, _, teach_signal = hand_dynamics(\n",
    "            torch.zeros(2),\n",
    "            torch.tensor([TARGET_SEQUENCE_CO[0,0], TARGET_SEQUENCE_CO[0,1]]) # initial \"target\"\n",
    "        )\n",
    "\n",
    "        # Initialize task.\n",
    "        hidden = None\n",
    "        task = CenterOutTask(hand, SCREEN_SIZE, MIN_DELAY, MAX_DELAY, MOVE_TARG_THRES, HOLD_TARG_THRES, PADDING)\n",
    "        inp = task(hand)\n",
    "\n",
    "        # Allocate storage\n",
    "        inp_hist = torch.empty(SEQLEN, INPUT_SIZE)\n",
    "        acc_hist = torch.empty(SEQLEN, OUT_SIZE)\n",
    "        hidden_hist = torch.empty(SEQLEN, N_NEURONS)\n",
    "        \n",
    "        # Zero out the gradient\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        loss = torch.tensor([0.0])\n",
    "\n",
    "        # Now simulate the trial\n",
    "        for t in range(SEQLEN):\n",
    "            # Update the RNN activity\n",
    "            acc, hidden = rnn(inp, teach_signal, hidden)\n",
    "\n",
    "            if inp[0, 0] > 0.1:\n",
    "                active_target = inp[0, 5:7] # go cue on\n",
    "            else:\n",
    "                active_target = inp[0, 3:5] # go cue off\n",
    "\n",
    "            # Update hand position one time step\n",
    "            hand, teach_loss, teach_signal = hand_dynamics(acc, active_target)\n",
    "#             loss += teach_loss\n",
    "            loss=teach_loss\n",
    "            \n",
    "            # Store outputs\n",
    "            inp_hist[t] = inp\n",
    "            acc_hist[t] = acc\n",
    "            hidden_hist[t] = hidden\n",
    "            \n",
    "            # Update target locations.\n",
    "            inp = task(hand)\n",
    "            \n",
    "        # Compute loss, and do backprop.\n",
    "    #     loss = crit(inp_hist, acc_hist)\n",
    "            loss.backward(retain_graph=True)\n",
    "            losses.append(loss.item())\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "        # Update the weights\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Save the rnn model\n",
    "        torch.save(rnn, './CenteroutModelResults-{}Epochs/MonkRNN_TrainedModel_CenteroutEpoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "        torch.save(hand_dynamics, './CenteroutModelResults-{}Epochs/MonkRNN_TrainingHandDynamics_CenteroutEpoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "        # Save neuron firing rates\n",
    "        torch.save(hidden_hist, './CenteroutModelResults-{}Epochs/neuronFiringRatesTraining_MonkRNN_CenteroutEpoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "        # Save target locations, hand positions and accelerations of each step in the last epoch\n",
    "        torch.save(inp_hist, './CenteroutModelResults-{}Epochs/handPositionsTraining_MonkRNN_CenteroutEpoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "        torch.save(acc_hist, './CenteroutModelResults-{}Epochs/handAccTraining_MonkRNN_CenteroutEpoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "        if (k % PRINT_EVERY) == 0:\n",
    "            print(\"Epoch {}, Step {}, Loss {}\".format(epoch, k, losses[-1]))\n",
    "    #         print(\"inputs {}, acc {}\".format(inp_hist, acc_hist))\n",
    "    #         plt.plot(np.array(inp_hist[:,1]),np.array(inp_hist[:,2]), color = colors[plot_ind], label=k) \n",
    "    #         plt.plot(inp_hist[1,5],inp_hist[1,6],'.',markersize=10, color = colors[plot_ind])\n",
    "    #         plot_ind +=1\n",
    "\n",
    "# Save losses of each step in all training epochs\n",
    "torch.save(losses, './CenteroutModelResults-{}Epochs/lossesTraining_MonkRNN_CenteroutEpoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "torch.save(TARGET_SEQUENCE, './CenteroutModelResults-{}Epochs/targetSequence_100Targets.pt'.format(N_TRAIN_EPOCHS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XlP3qjgXKDkR",
    "outputId": "cb216e49-0fca-4bca-d485-e026880e69cf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses[50:],'.') \n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UwyP1xEpKDkT",
    "outputId": "171649cf-8d54-4423-e7ae-6859ca7bea40"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(inp_hist[:,1]),np.array(inp_hist[:,2]), 'r', label=k) \n",
    "plt.plot(inp_hist[:,5].detach().numpy(),inp_hist[:,6].detach().numpy(),'k.')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mnatvOH5KDkV",
    "outputId": "22ad7089-b426-4c4e-8f1c-a2f10efe9450"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119fda8d0>,\n",
       " <matplotlib.lines.Line2D at 0x11abd6470>]"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(acc_hist.detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NE-FJwehKDkX",
    "outputId": "e3c95e2a-acb1-4ca9-9ed1-b045932f36d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11afcbcf8>]"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UI5EVvkIhMBSMoBSU+TITD5Qb75trqyUYjlHkcVLqc+d8LEKcSDRmu8hfKUWeB/4i/kKcodEwZV4Xjgx9CUu98TuKyVZLr3+MT973Gs+39mRkv5MZi0TxuhyUecX2sQt2y/YBcCry3vYR8ReAzFb0BCg1Iv/h4MTc+r/95R5+tbOD99/7clbmA8JRjcfloNTrksjfJtgtzx/sU4U2k4j4C0Dc9inLkN8PUOx2otRE2ycYjvLMwS4uX1NLJKb5ybYTGdu/STgaw+10yISvjbBjqqdDiecvFAj+sbjtkykcDkWpxzVhknXHsX6C4RgfumwFW1dU89juUxnbv0koEi8lUOp1TbkLEXJDNBbDbTPbx6GUlHQWCoPhYCRjk70mpb6JVsvu9ni3pPOaq3jrhkW8cXqYk30jGR1DOBrD43JQZkT++f4FXwjYNdVTbB+hIMiK+E+aZD3YOcziCh8VRW6uXFsHwEtHejM6hnA0nlZY6nUR0xOrfAq5wY6png6Hwga9hzKKvT5xIWf4xyKJSdlMMdlnP9g5zNpFZUC8gUZlsZsdx/ozOoa4568SxyqTvrknErNfqqdk+wgFgdaa4WA4oxO+gOGzx8U2GtMc6vKzdlEpEI+0WpZVsf1YX0bHEDInfI35DVnolXsiMftl+ziUIpbnlqCIv8BYJEY4qhOCmCnKkiL/470BQpFYIvIHaFlezZGeQEZTPsPRGB6nI2FxSeSfW2Ixjdbgslu2j3j+QiFgRuPlGbZ9KorcDBgNXQ52DgPxZtkmFyyvAsio9ROOmJ5//C5HyjrnlnAsXmXVfou8JPIXCgAzGs+0599cXUKPP8RwMMzBTj9Kwer60sTrm5ZW4HYqXjuZQfGPxnC7VKKAnVnTSMgNZnRtO89fIn+hEDDz3cu8mfX8V9SWAHCsZ4Q97YOsqCmh2DN+wfG6nKxfXM6uk4Np70trzWgoOiWX3/T8RfztgSmwtvP8Hflf20eqegoJ6yPTkb8p/kd6/LzeNsAlq2qnbHNOYyW/fK2dWEzPuc7QsZ4Av9lzmhcO97C7fZCBkbiwf/7t6/nI5SuBcc/fFP+B0fxu2GF3TPF3KJuJvyLvbR8RfyEh/pnO819WU4zTofj9vk46h8bY3FgxZZvNjRX8+KXjHOnxs7q+LMW7TCQYjvKr19r58UvH2dsxBMC6hjKu29RAU3Ux3/jtAXaeHEhsb+b5+9wOPC6HRP45JhH529Dzn6vtE4tpnj3UzeN7Ozna4+fUYBB/MEIgFKHY42JRuY/1i8vY0lzFec2VrGsoz6ndZcm3XSl1LfBtwAl8X2v9tWm2uxF4ALhAa73Din0L6WNaI5mq5W/iczu5am0dj+yKl3EwF3Ylc25TJQA7Tw6eUfyfOdDFZ3+xm9NDQdYvLufv3rGBazc1sLSyKLHNc4d66BgYBeJCEzUWFCkV9/0HR0T8c4ltI/85NnMZCob52I9e4cUjvZT5XKxdVMbmxkrKfS5KvC4CYxE6BkZ59mA3D77aDsRTn7c0V7JhSTnVxR4iMU3XUJD9p4apLfPwnVvPz9ThARaIv1LKCdwNvAVoA7YrpR7WWu+btF0Z8D+BbenuU7CWxIRvhlM9AT5+1SpeONzLpatrWFlXOuX1lXWllHic7Gob4MbzG6d9n/u3n+Qzv9jFmvpS/vnPL+TilTWoFAKypLKI5w7Fy0WHo/HMErcrvl1lkVsi/xxjFk+zm+fvnENht1hMc9dPXmP7sT6+8u5N3Hh+I16XM+W2Wmva+kd59UQ/24/1seNYP/c+d5SwsZy4zOti9aJSVqf4bliNFd/2rUCr1voIgFLqPuAGYN+k7f4B+AbwKQv2KVhItjx/gAuWV7Pzi2+ZNtJzOhRnN1bwepJVM5nnDvXwvx7cxeVravne7S343Km/aABLK4voHA4SisQS4u8xSgnEU09F/K1mOBjmv7ed4PnWHvoCIcp9btYtLuOC5dVcfVY9RZ7xv1fEEL1M9ZGYLw4Hs57w/fWuDp492M0/3LCRWy9cNuO2Simaqotpqi7mhnOXAvELQiAUxanUhM8m01jxbV8KnEx63AZcmLyBUmoL0KS1fkQpNa34K6XuAO4AaG5utmBowmwYDobxuR1Zq68yXVRkck5jJT94/pjReGXitmORKH/30B6W15Rwz20zCz/ExV9rOD0YTFzczOOsLHbTMRBM40iEybQPjHLL917ieO8I6xrKWFpZRG8gxH0vn+QHzx+jyO3kTevr+cjlKzm3qTIxqWq3yN+hZmf7aK35t6daWddQdkbhnw6lVFbuuidjxR5T/dUSn5pSygF8C/jAmd5Ia30PcA9AS0tLfk+12wj/WGZr+c+Vc5oqCUVj7D81nJgDMPnes0c42hPgRx/aOqsoaVGFD4Cu4SBedzEwLv4VRR72GZPEQvpEojE+9uMd9AVC3P+xi9m6ojrxWjSmefloH4/u7uC/XjpBa5ef337yCiJ2zvOfhe2z43g/rV1+vnnjZtvdvZwJK8S/DWhKetwIdCQ9LgM2Ac8YnmwD8LBS6nqZ9LUHQ8FIRmv5z5UWY6Xvi4d7J4j/yb4R/u/Trbzt7AauSDFZnIq6Ui8A3cNjLCqPXwjM2vG1pR56/CG01innC4S5cd/2k+xpH+I7t543QfghLqYXr6rh4lU1OJVKTHrGbCr+jllm+/z69Q6K3E7evnlxFkZlLVbc528H1iilViilPMBNwMPmi1rrQa11rdZ6udZ6OfASIMJvI/xZKOc8F+rLfJy1qIznWrsnPP8Pj+xDofj82zfM+r3qygzx94+Ne/6u+GlfW+olFI0xJCUe0iYa09zz7BG2NFdy3aaGGbddXFnE8FiE4WB4PPK32cXXOYtsH601T+zr5LI1tRMWKy4U0hZ/rXUEuAt4HNgP3K+13quU+rJS6vp031/IPMPBcFYme+fCVevq2Hakj16jyNvTb3Txu32d/M83rWFJUirnmagu8eBQ8cjfzKgwbR/zwpCN3sH5zrajvZzoG+GDl644413UYsOKOz0YtG15h/gir5m3OdIToGMwyNVn1WdnUBZjyQyf1voxrfVarfUqrfVXjOe+oLV+OMW2V0nUby/8Y5GMl3aYK+/espRITPPzV9rwj0X44sN7WVlXwocvWzGn93E6FDWlXkP8jVRP53jkD9AzLOKfLo/sOkWxx8lb1i8647aLK+IX71N2Fv9ZeP6vGAUIt66oysaQLMde4Z6QE7LRxWuurGso54q1dfzrk4f49a4O2vpH+OlHL0pYNnOhzhD/UEL8Dc+/zANAj19KPKSD1po/HOjm8jW1s5qENyP/U4OjiTtOu4m/cxbZPjuO91FZ7GZlbeZz8jOBFHYTGA5mvovXfPjKuzbRVF3M8Z4RvvbezVy4smZe71NX5qXHP0Y4MjHP35wMFtsnPY71jtA+MMpla2Y5CV82Pglv18h/Ntk+rxzv57zmqgWX5WNiv2+8kFViMW27VE+TpupifvOJywHSysapKnZztCcw7vkbdw9VxR7cTsXpIcn1Twez+9rFK6vPsGUcn9tJmc9Fjz9kW/FXSqE102aC9QdCHO4O8J7zpl+Fbnck8i9w/CGjqJuNUj2TUUqlnYZZVeKhPxAiFI03azcXFDkciiWVRbT3j6Y9zkLm9ZMDlHldc7I/6kq9dPuTIn+7ZfsY45nO+TGLBZ6/bGH6/SDiX/D4s1TRM5dUFXsYHosQGIuLf/Kq4caqIk72j+RqaHnB620DbG6qmJP9UVvqpSfJ9rFdVU9DGafL9d93Kr44cOOS8mwNyXJE/AucbNb1yRVVJfGJ3S4jqyd50rixspg2ifznTTSmOdjpZ8PiuYlgbZmHnqTI345VPWH6mv77Tg3RVF1kS7t0toj4Fzj+MaOL1wI+ic9EVXH82DoNb9+bLP5VRXQPjxEMR3MytoVOe/8ooUhsQjvO2VBb6p3g+dutgbtp+0wX+e/vGGJ9w8KN+kHEv+AxV7fmorBUtqgujkf+pvgnR/4r6uLdxQ53+7M/sDzA/NxWzbEEcW2pl8HRMKPGRddm2p+4E0kV+Y+EIhztDbB+jnc7dsNmH7mQbUzbpzyPbZ9KQ/xPDxrin1S91LQrpMDb/Jiv+FcbVpy5gttukX/C9olNfe3A6WG0RsRfWNiMT/jmr+1jCk2qyH9ZTQnFHmdiAk+YG4e7/VSXeBLzKrOlqnjiArssVROfNeb8c6pc//2nhgHmPM9hN2z2kQvZxvT8S7zZayKRbSoTnv/UCV+nQ7GuoUwi/3lyuCvAKsM6mwtVJfG/ibnAzmmzyN9cd5DK899/aohSr4vGqtnXmLIj9vrEhaxjpj8uxKqEs8XndlLscTIajqLU1MYhG5aUs+/UEHqWbfuEcQ53++ds+UBy5G+Iv82yfcy1JanOif2nhli/uGzBruw1EfEvcILhKF6Xw3YrLK3GFBuP0bw9mQ2LKxgORnKe8hmLab7w0B5eOd6f03HMloGREL2BECvnEfmbVly3afvYLs/fiPwniX8spnnj9PCC9/tBxL/gGQlFKc5i39BcYdoM3hSF4TYYC3X2dgxmdUyTOdob4EcvHue9331hVi0Ec83JvvjFsrl67uJvWnFmRVW7Rf7TpXq2D4ziH4uwboGneYKIf8ETF//8tXxMEpF/iv7B6xrKcDkUu9pyK/47T4w3rW8fsP/Cs/aB+Mro+XjfXpeTEo8zyfO3l/hPl+1ztCcAMK+7Hbsh4l/gjIYj+Nz5fxqY4p8q8ve5nZzVUJZz8d/VNi7+ubagZoM5xrk010mmstjDmFFp1W7inyjvMMn2OdZriH+tiL+wwCmcyD9uM0zXD2BzYwW72gZyOunbPhBMWHBtC6DeUMdAkCK3M/HZzpXqpPRQu4n/dIu8jnQHKPE4E2WpFzIi/gXOSCg6qwYcCx0zD90zTUL55sZKhoIRjvfmTnT7R0JsXFKOUgsj8m8fGGFpVdG8q65WJl00bCv+samR/7KakrQrzdoBEf8CZ7RQJnwTnv/0kT/EK1Tmiv5AiEXlPurLvAvE8x9l6TwtH5gY+U9Ov80102X7HO0JsCIPLB8Q8S94RkKRwhD/kpnFf+2iMjwuB3vac+f79wZC1JR4aCj3JVYj25mOgSBL01joZF6QwYZVPVNk+4SjMdr6R0X8hfwgGI5R5C4gz38a28ftdLCqrpRDXbkp8BaJxhgcDVNV4qGy2MPgaDgn45gtI6EIfYFQWpF/svjbz/aJ/58c+J/sGyEa0ywX8RfygYKJ/M9g+wCsqS/lUGduxL9/JC721SUeqord9I/Yu6l8h2FLpSX+JeOev820P2V5BzPTZ0VtcU7GZDUi/gVO4SzyOrP4r11USvvAKIGxSLaGlcAU+6rieOQ/MGLvyN+ckLbC9nE60m/VaTWOFJ5/x0DciltaKeIvLHCiMc1YJFYQ2T7VM+T5m6yuLwOgNQfWT38gWfzdDAcjRKIp6gnbBHNOoqHcN+/3SBZ/u+FMke3TNRREKagtnVsFU7si4l/AmI00CiHyL/I48bocZ4z8gZz4/ubfosjjpLIobofY2fc3K6TWl88/3920fexW2gHGL0jJmZ5dw2PUlHhx2a3+9DzJj6MQ5sVIKG5vFLnzX/whntHTVDX9LXtzdTEep4NDXcNZHFWcYDge5fvcjoRF1W9j66drOEhVsRtvinIZs8WM/O2W5glgXo+SPf/OoSCL0rjY2Y38T/MQpmU0ZEabhXEaPHjnJTNGmS6ng5V1JTmZ9B2LxP8WXpeTikTkb99J386hMRalYfnAuPjbsTSyM8UK367hMerzYGWviUT+NiEYjmbd4x0JFY7tA/F0zjMJzar6Uo7koJ/vWHLkb4hif8DOkf9Y2iUOijxOitxOW0b+qbJ94uKf3gXPToj424BgOMrV//QMF371SY4b6WTZYCQ07jMLcVbWlnCyf5RQJLsX4mBS5G+WPRiwseffNRRMO/KH+PoLW0b+k8Q/Eo3R4x/LK9tHxN8GPLb7FKcGg/QGQvzT7w5mbb9Bc8K3QDz/2bCyroRoTHOiL7s1fpIjf7Ph/IBNc/1jMU23RRZIVYnHlpG/ab0NGNZbbyCE1lBnwQXPLlgi/kqpa5VSB5RSrUqpz6Z4/a+VUvuUUruUUk8qpZZZsd984Yla3SP/AAAdi0lEQVT9nSyp8HHHFSt5dFdH1io6jts+heH5z4YVtfGMH7Nue7YwL8Q+t5MyrwuHwra5/n0jISIxbVHk77FdaQeAmpL4ha3X6DRmprYuEs9/HKWUE7gbuA7YANyslNowabPXgBat9Wbg58A30t1vvqC1Zvuxfi5cWcP7L1mOUoofvXg8K/tOZPuI7ZPArNuSbd8/GIniMPoLOxwqvtDLphO+CSG0wAJZXOGjzGe/4KO8yIXLoeg11l90JVJbJfJPZivQqrU+orUOAfcBNyRvoLV+WmtthrMvAY0W7DcvONE3QvfwGC3Lq1haWcS1mxr46csnsrLKdLTAJnxnQ0WRm9pST9Yj/7FwDJ/bmVjpWlnktm2qZ5fRerHOgsnPz71tPd+7vSXt97EapRTVJR56jU5j5jGL5z+RpcDJpMdtxnPT8WHgN6leUErdoZTaoZTa0d3dbcHQ7M/2Y/Fm3RcsrwbgQ5euYDgY4RevtmV834WW7TNbVtSWcKQ7y7ZPJIovae6lstjN4DTiPzASSqSG5oIuCyP/6hIPTdX2LJdQU+qlLzBu+8RX94r4J5PKsEvZDkkp9T6gBfhmqte11vdorVu01i11dXUWDM3+7DjWR0WRm9V1ca/5/GVVnNtUyQ+eP5bxJt7Jq0qFcVbWlnIkB5F/cumJymJPyuJuhzqHueIbT3PTPS9l/ALw81fa+PYTh6Y8b1og+dDNaiZqSjz0GJ5/1/AY1cUe3HmyuhesEf82oCnpcSPQMXkjpdSbgb8Frtdaj1mw3zkzFAzzs+0nbNUoY/uxPlqWVU1Id/vQZSs42hPgmYNdGdvv/3lkH/fvOInToaYtc1yorKgrocc/xlAwe7ZLMBKbEvmnmvC959kjDAUjvHZigCf3W3N+jEWitPWP0BcI0dY/wpFuP2ORKJ964HW+9cTU7LNu/xgVRemt7l0I1JR66A0Yts9QMK/8frBmhe92YI1SagXQDtwE3JK8gVJqC/DvwLVa68wp2hn49AOv8/jeTurKvDz6l5fl5I85Gory6ol+LllVQ18gxOHuADee3zRhm+s2NdBQ7uMHzx/jmnWLMjKO7z93FIBSr8t2FRVzjdmc+2h3gHOaKrOyz2A4OjHyL5pa0z8a0zyxv5N3bF7MHw/18PQbXbzt7MXz3ueRbj//9lQrj+89nbAATf7hXZum/b0+o+lMvlPuczM0Gp97y7fVvWBB5K+1jgB3AY8D+4H7tdZ7lVJfVkpdb2z2TaAUeEAptVMp9XC6+50rJ/tGeHxvJ9esq2dwNMzXf3sg20MA4LvPtHLr97fxjccPsOO46fdXTdjG7XTw5xc08VxrD6cGrb9LSV61KJbPVFbWGeKfRetnLBLDOyny949FCCet+j7c7ad/JMw16+q5bHUtLxzunee+onzp13t5y7ee5fG9p7nh3KV8/b1n86m3rk0I3N/9ag8Qz8aZTP9IKFF/KJ8p9bkIjEXQWuddXR+wqLaP1vox4LFJz30h6ec3W7GfdHhyfycAf/eODdy3/QT3PHuED166nE1LK7I6jqcOxG98/uul4/T6xyj2ODm7ceoY3r1lKd9+8hAP7ezg41eusnQMQ0kRpUz2TqW5ugSHym66ZzAcxTfB8zcWGY2EE966WXPorIYyuobHeHT3KQZGQolFYbPBPxbhfd/fxs6TA9x6YTOffPPaCd79Xdes4X//cjc/2XaCEo8TnWLaqdcfsu0krZWUel1EYppgOL66N59KO0ABrfB9+VgfTdVFrKgt4c6rVlNR5Oaffpfd6P/7fzzCnvYhLl1dw3Awwv072rhkVU1K73R5bQlbmiv51Wvtlo8juWxAuc89w5aFicfloKm6OKuTvmPh6ITIP1Vxt0NdwzgUrKorZeOScgD2nRqa9T6iMc1f/uRVdrcP8p1bz+Mr7z475aTtl6/fyB8/czXXn7tkQmEzk/6RUKI/Qj5TYgRGx/sCxHR+pXlCAYn/nvYhNi+N+7cVRW7uuGIlzxzo5tUT/VkbwwM72tiwuJz/eP8FrKmPZ/f8aUvTtNu/Z8tS3jg9zL6O2X/BZ0Ny2YBUt/VC9tM9xyKxCZF/VaLEw/iF+lCnn+bqYnxuJxsWG+I/h3Pjpy+f4OkD3Xzp+o0zzhW4nPGLn1JqivhrrekPhAvC9inxxo2Ro8Z5YMW6BjtREOI/OBrmRN8IG4xoCeD9Fy+nusSTMpUtU2M42DXMtZsa8Lmd/NdHLuTBOy/hTzY2TPs7b9+8BJdD8aud1kb/yZF/Os048pmVtaUc7QmgU/keGSCV5w+TxL9rONFtrKbUS3WJh8OztKYCYxH+5YlDXLC8ilsvbJ7V7ziVmjA/BBAIRQlFY1SX5P8dY6kh/uYdoET+C5ADp+PNOZLFv8Tr4o4rVvKHg5mN/iPRGN95ppWfvnwCraHFmNxdVO7jvOaqGX+3usTDVWfV8dDO9ilfwnRIXjxkRjfCRFbUlTAajnLaWNCUaUKRGG7neNZVZZER+RsX6nA0xtGeAGuMbmMAq+pKONw1u7uTn2w7QY9/jM9et37W2V1Ox1Tx7/OPt5vMd8zvhnkHmG+pngUh/mZ0ZFotJrddtIyKIjff/+ORjOy3ayjIVx97g2/89gBf+80blPtcbDVW8s6Wd29ppHNojBfnmdmRimTbp9gt4p8K81w5mKXGLuFobMJ6i4pE5B//Wx3vDRCO6gnn8Kq60llF/lpr7tt+gpZlVZy/bOaAIxmHUlMmfPuM8dTkSR/bmUiIf0/8M67Lo9W9UCji3+XH53awpKJowvMlXhc3XdDE43s7LV349cS+Tt773RfY+tUnuff5o3icDs5prOCua1bPuf/nm9bXU+Z18UsLJ37NVYsQzxwRprLe8NT3dgxmZX+RmMaVFPmblT3NVb5mps/aReN/r9X1pfQGQonm79Px2skBDncH+NOWuZXUciiITlL/5Ebz+Y5ZcO5Id4DqEs+M/Z8XIgUR9h3u9rOitjRl04jbLl7G9/54hB+9eIzPXbc+rf34xyJ85uev89ju0yyrKeYz157FRStr2LC4fMLqzbngczt5xzmLefDVdj79J2fRYMEE7f5TQ6yuL+Xr7908p0iwkKgoctNUXcReiyfbpyMcjeFyjIuLw6GoKfXSM2yIf5cfZWT6mJg/H+nxc37J9HeUv3ilDZ/bMecFYSltH0P8qwtowndwNMy6PAyS8utSNg2t3X5WGQt3JtNYVcyfbGzgvpdPJkocz4dTg6Pc+N0XeHxvJ5+59iye+OsrufOq1ZzXXDVv4Te586rVxLTm356yZnJ6b8cQm5aUi/CfgQ2Lyy3PtJqOSFRP8PwB6su8dBtVJQ92DtNYVTRhUZ4p/jP5/lprntzfxdVn1VM2x7Reh2Nqto8p/oWQ7VOa1OfCit4FdiPvxX8kFKGtf3TC7fJkPnjpCgZHw/O2Vtr6R7jxuy/S1j/KvR+4gDuvWm1pAaim6mJu3trMz7afTNuG6BoOcnooyMYl2V3cthDZuKSCY70B/Fkorx2JxaacM/VlXrqG4xPOrV1+1tRPPIeXVhXhcTlm9P0Pdvo5PRTkqrPmXijRoWBynkHfSAi3U1FWAIkCJd7xC+3qSfOF+UDei/+R7gBaT53sTeaC5VVsWlrOvc8dnXMlzdODQW753jaGg2Huu+MirlybmWqkf/XmtVSXePjEfTsTXZ/myi9eaeMrj+4H4JLVNVYOLy/ZuKQcreGNOSykmg9aa8JRPWU+qK7MS/fwGJFojCPdEzN9IG7LrKwtobVrevF/xlhRfsU8zstUqZ79gRBVxZ6CqAflcjoS9ZZm0o+FSt6L/6GueJrn5C9OMkopPnr5Sg53B/i9UQZiNnQNB7nley/RFwjxww9tzWipiKoSD//0p+fQ2uXnr362c84XqeO9Af7mgdd5aGcH1SWexCIhYXrMu6PX2zI76Rsx/pZux2Tbx0ePP8Sx3gChaGxK5A9nzvj5w8Fu1jWUsXhSssNsMOfIks+1vkCoIPx+E3OObWWdiP+C41CnH5dDsawmtedv8vazF9NcXcx3njk8q4U9fYEQ7/v+Nk4NBvnBBy9gyxly9q3girV1fP7t6/nNntN89sFdRJKKfs00znuePczt974MwKfeupa7bzmvICK3dGmo8NFcXcxLR6xLs01FJBo/31JF/tGY5qUjfUDq6HNVXQkn+kZS1vb3j0XYfqxv3nejTuMcSc746R8JJRagFQJ333Iel6+p5ews1wDLBnlv3B3q8rOituSMHrzL6eBjV67kb3+5hxcP93LJ6tpptx0cDXPbf2zjeO8IP/jABYkuXNngI5evZGg0zL8+1cqpwSBff+9mllROjOoi0Rjbjvbxs+0n+e2e04SiMVqWVfGFd2zgTeszUyI6X7lkVQ2P7j5FNKZxpsgWs4JwLH4Rnzzh22wUT3t872kgte+8qr6UmIbjvSNT5rVePNxLOKq5ch5+PyRF/kniPzgaZmVt/kXB07FpaQU//vCFuR5GRsh78W/t8rN+8ezStN57XiPffuIQ33riIBevqkkZHQfGInzoP7dzsHOYe25rmfEikSn++q1n0VBRxD88so8rv/k0V6ypY/WiUrSO2zvbjvYxMBKmzOfilgubuXlrs+Tzz5NLVtdynzHRvrkxM7X9zch/coBiFm/746EeVteXplyNPZ7x458i/n842EWxx0nLsvkFJw5l2j7jzw0HI7ZsuC7Mnbz+K46GohzvDfDOc5bManuf28kn37yW//3L3fxmz+kpedFDwTB3/GgHr53o5+5bzuPqdfWZGPasuOXCZi5fU8sPXzjGk2908eyhbhxKsbSqiGvOquetGxdx5dp6qdefJhevjE+Mv3C4N4PiH1dX1+RUz3IfFUVuBkfDXLoq9QS92Xtgsu+vteaZA91csqp23ouTzGtRsu0zNBqmvKhwbJ98Jq/F/9UT/cQ0bGme/Zf2zy9o4kcvHuMrj+7nsjW1iZLHpweDfOAHL9Pa5eef/+xcrkujg5JVNFUX8/l3bODz79iQ66HkLXVlXtY1lPH7fZ2W91UwCRni73ZMFekbzl3Cz19pm/Z8K/a4WFpZxOFJFUgPdwdo6x/lY2mMORH5G+IficYIhKJSBjxPyOsJ321HenEoaJnDYianQ/HV95zN6aEgd/7Xq5zoHeGhne287V//yMm+Ee79wAW8a8vSDI5asBvvPGcJrxzv52TfSEbef3zCd6rN+OUbNrHvy9dy0crpU3NX1k1N93z6jXiK5zVp3J2O2z7x8ZnrHcT2yQ/yVvy11jy+t5MtzVVzXtl4XnMVX3/vZl460ssV33yaT9y3k6WVRfzyf1w6r3xpYWFzvWEb/npXR0bePxIzbZ/5fR3XNZRxoHN4QsbPU290cdaiMpZWzj3F08Sc4DZz/c1+tmL75Ad5ewl/9UQ/BzqH+cq7p29EPRM3nt/IluZKnm/tobGqiCvW1M37yyksbJqqizl/WRUPvtrOX1y5yvI02bAR+XtSRP6z4fxl1Xzvj0fZ0z7E+cuqGAqG2X6sj49cvjKtcZnZPqbnPxSMl5eWyD8/yEs1Gw1F+eLDe6kr83LDufO3aFbVlXL7xcu5Zt0iEf4C55atzbR2+XnmQLfl752wfVJ4/rPB7BGx7Wh8PcIzB7qJxHRalg+M5/mb872m+Ivnnx/kpaL921OH2NsxxFfetSnRjUcQ0uH6c5ewpMLH//vDYcvfOxxLne0zW2pLvZy9tILf7I6vB/jZ9hMsrSya01xXKsxlDVNtH/lO5QN5J/6joSj/ve0E125s4K0ztEgUhLngdjr4yOUr2Xa0L1EvxyrCEXOR1/y/jjecu4Td7YN86dd7eb61l1svak5ZwnwuOCZ5/sMS+ecVeSf+Q8Ewl66u4UOXrcj1UIQ849aLmllRW8KXfr0vZTmF+WLW9nGlIdY3b22msaqIHzx/jJV1JXzo0vTPf+ekVM+hoBH5i/jnBXkn/ovKfXzn1vOzWnJBKAy8Lid/f/1GjvYEuPupVsveN2zm+afRKarE6+LBOy/h2zedy4N/cUnaPSRgPNvHrOtmRv6lMuGbF+Sd+AtCJrlybR3v2bKUu585zJ52a6p9Jso7zHPC16S+zMcN5y6l0qIWiyqF51/qdWWsxpGQXUT8BWGOfPGdG6kp8fCpB14nFDlzZdUzEUlzwjdTOB2TbZ8w5RL15w0i/oIwRyqK3fzje87mjdPDfPvJg2m/XyhR2M1m4q+mTvjKAq/8QcRfEObBm9Yv4s9aGvnOM4d59mB6uf9mYTcrW39aweSSzkOjUtEzn7DX2SYIC4gvXb+JtfVlfPJnOzk1ODrv95mumUuumVzSOW77SOSfL1hytimlrlVKHVBKtSqlPpvida9S6mfG69uUUsut2K8g5JIij5PvvO88xsJRPviD7fQHQvN6n0QzF5tNpE4u6Sy1/POLtMVfKeUE7gauAzYANyulJtcY/jDQr7VeDXwL+Hq6+xUEO7CqrpR7bm/hSE+A2+7dxsDI3C8Ado/8E9k+4vnnFVacbVuBVq31Ea11CLgPuGHSNjcAPzR+/jnwJiVNZIU84dLVtfz7bedz8LSfd3/nBVq7huf0++FpmrnkGjPbR2uN1loi/zzDCvFfCpxMetxmPJdyG611BBgEpi9QLggLjKvPqucnH72Q4WCYd939AvdvP4lO6oA1E2GL8vytJjnyH4vEiMZ0ylaSwsLEirMtVbgy+ayfzTYope5QSu1QSu3o7ra+eqIgZJKW5dU8dNdlbFhSzmd+sYtbvreNV473n/EiYEb+8223mCkS4q81I6F4OYtiC1YOC/bAist4G9CU9LgRmNz1wtymTSnlAiqAvslvpLW+B7gHoKWlZXZhkyDYiKWVRdz30Yv475dP8M+/O8B7v/sCq+tLuWRVDU1VxQB0+8fo8Y/xV29eS1N1MeFoDIfCditnE4u8YjASitf1KZbIP2+w4i+5HVijlFoBtAM3AbdM2uZh4P3Ai8CNwFN6tvfEgrDAcDgUt120jPdsWcqDr7Xzm92n+MUrbQRCE4vBbVpSwYcuW0EoGrNd1A/jJZ1jyZG/RyL/fCFt8ddaR5RSdwGPA07gXq31XqXUl4EdWuuHgf8AfqyUaiUe8d+U7n4Fwe6UeF3cdtEybrtoGbGYJhCKoIEyr4uNX3yctv742oBQJGa7BV4wsZOXiH/+Yck9nNb6MeCxSc99IennIPCnVuxLEBYiDoea0Eu6saqItv54Q/hwNIbHhuLvTGrgPmI0by/2iO2TL9jvjBOEAqCxqpiTSZG/HW2f5AbuEvnnH/Y74wShAGiaEPlrW9o+KuH5w0jYFH+J/PMF+51xglAANFYVMxyMMDgaJhSN2a6iJ0ws6Txu+0jkny+I+AtCDmisKgLgZN+IYfvYT1STSzqbtk+JRP55g4i/IOSApup4zn9b/6gx4Wu/yD+5pLOZ518kkX/eIOIvCDnAjPzb+kdsO+HrUMniH8XlULYcpzA/5C8pCDmgoshNqdeViPztOOE7bvvASCgqfn+eYb8zThAKAKVUItc/ZNNsH7POXCwWt30k0ye/kL+mIOSIxqpi2vpHUMqedkpytk8gFKXYK5F/PmG/M04QCoTGqiIj2ydqyxW+yVU9R8X2yTvsd8YJQoHQVF1MIBSla3jMlnn+jqTyDoGxCMVuMQryCRF/QcgRZsbPcDBia9snGtOMhsX2yTfsd8YJQoGwqq4k8bMdJ3wThd20ZPvkI/Y74wShQFhWU4LLiK7tKP7KzPYxyjtItk9+Yb8zThAKBLfTQXNNfKWv1462T3J5h7BE/vmG/c44QSggVteVAvaM/M15iFAkxshYVCL/PMN+Z5wgFBArauO+v83a9wLxC5LTofCHIoSiMYn88wwRf0HIIabtc2owmOORpMbnctAfCAFSzjnfEPEXhBzSbFT3PNE3kuORpMbndtKXEH+xffIJEX9ByCGbGysBuOXC5hyPJDU+t5NeifzzErmUC0IOqShyc+xrb8/1MKbF53YkIn+p5Z9fSOQvCMK0+NxO+vxx8S/1SqyYT4j4C4IwLT63k2Gjf2+JiH9eIeIvCMK0+NzjElEqtX3yChF/QRCmxZfUWF4i//xCxF8QhGnxuUX88xURf0EQpmWC+Euef14h4i8IwrSYnn+R25mo7y/kByL+giBMixn5i+WTf4j4C4IwLWbkL5k++Uda4q+UqlZK/V4pdcj4vyrFNucqpV5USu1VSu1SSv15OvsUBCF7mNk+XpeIf76RbuT/WeBJrfUa4Enj8WRGgNu11huBa4F/UUpVprlfQRCygFnSIRyL5XgkgtWkK/43AD80fv4h8K7JG2itD2qtDxk/dwBdQF2a+xUEIQtcsqoWgLb+0RyPRLCadGdxFmmtTwForU8ppepn2lgptRXwAIfT3K8gCFlgw5Jyvvrus1lc6cv1UASLOaP4K6WeABpSvPS3c9mRUmox8GPg/VrrlPeQSqk7gDsAmpvtWeJWEAoNu5abFtLjjOKvtX7zdK8ppTqVUouNqH8xcUsn1XblwKPA57XWL82wr3uAewBaWlr0mcYmCIIgzI90Pf+HgfcbP78feGjyBkopD/BL4Eda6wfS3J8gCIJgAemK/9eAtyilDgFvMR6jlGpRSn3f2ObPgCuADyildhr/zk1zv4IgCEIaKK3t6a60tLToHTt25HoYgiAICwql1Cta65YzbScrfAVBEAoQEX9BEIQCRMRfEAShABHxFwRBKEBsO+GrlOoGjqfxFrVAj0XDWSjIMRcGcsyFwXyPeZnW+owldGwr/umilNoxmxnvfEKOuTCQYy4MMn3MYvsIgiAUICL+giAIBUg+i/89uR5ADpBjLgzkmAuDjB5z3nr+giAIwvTkc+QvCIIgTEPeib9S6lql1AGlVKtSKlVbyQWJUupepVSXUmpP0nMpeyirOP9qfAa7lFLn5W7k80cp1aSUeloptd/oAf0J4/m8PW6llE8p9bJS6nXjmL9kPL9CKbXNOOafGdVyUUp5jcetxuvLczn+dFBKOZVSrymlHjEe5/UxK6WOKaV2G8UudxjPZe3czivxV0o5gbuB64ANwM1KqQ25HZVl/CfxHsjJTNdD+TpgjfHvDuC7WRqj1USAv9FarwcuAv6H8ffM5+MeA67RWp8DnAtcq5S6CPg68C3jmPuBDxvbfxjo11qvBr5lbLdQ+QSwP+lxIRzz1Vrrc5NSOrN3bmut8+YfcDHweNLjzwGfy/W4LDy+5cCepMcHgMXGz4uBA8bP/w7cnGq7hfyPeL+ItxTKcQPFwKvAhcQX+7iM5xPnOfA4cLHxs8vYTuV67PM41kZD7K4BHgFUARzzMaB20nNZO7fzKvIHlgInkx63Gc/lKxN6KANmD+W8+xyMW/stwDby/LgN+2Mn8c54vyfe83pAax0xNkk+rsQxG68PAjXZHbEl/AvwGcBs8VpD/h+zBn6nlHrFaGELWTy3023gbjdUiucKMZ0prz4HpVQp8Avgk1rrIaVSHV580xTPLbjj1lpHgXOVUpXEu+CtT7WZ8f+CP2al1DuALq31K0qpq8ynU2yaN8dscKnWukMpVQ/8Xin1xgzbWn7M+Rb5twFNSY8bgY4cjSUbdBq9k5nUQzlvPgellJu48P+31vpB4+m8P24ArfUA8Azx+Y5KpZQZrCUfV+KYjdcrgL7sjjRtLgWuV0odA+4jbv38C/l9zGitO4z/u4hf5LeSxXM738R/O7DGyBLwADcR7zOcr0zXQ/lh4HYjQ+AiYNC8lVxIqHiI/x/Afq31Pye9lLfHrZSqMyJ+lFJFwJuJT4I+DdxobDb5mM3P4kbgKW2YwgsFrfXntNaNWuvlxL+zT2mtbyWPj1kpVaKUKjN/Bt4K7CGb53auJz0yMInyNuAgcZ/0b3M9HguP66fAKSBMPAr4MHGf80ngkPF/tbGtIp71dBjYDbTkevzzPObLiN/a7gJ2Gv/els/HDWwGXjOOeQ/wBeP5lcDLQCvwAOA1nvcZj1uN11fm+hjSPP6rgEfy/ZiNY3vd+LfX1KpsntuywlcQBKEAyTfbRxAEQZgFIv6CIAgFiIi/IAhCASLiLwiCUICI+AuCIBQgIv6CIAgFiIi/IAhCASLiLwiCUID8f1CgeSDzwpADAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hidden_hist[:,201].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t-f4mL6PKDkZ"
   },
   "outputs": [],
   "source": [
    "epoch = 2\n",
    "rnn = torch.load('./CenteroutModelResults-20Epochs/MonkRNN_TrainedModel_CenteroutEpoch{}.pt'.format(epoch))\n",
    "hand_dynamics = torch.load('./CenteroutModelResults-20Epochs/MonkRNN_TrainingHandDynamics_CenteroutEpoch{}.pt'.format(epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NPdhhIhjKDkb",
    "outputId": "fb31677c-4e05-4cad-ccfd-02c5bd84f29f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TEST NETWORK ON NEW INPUTS THE SAME CENTEROUT TASK##\n",
    "\n",
    "N_TEST = 50\n",
    "TEST_SEQLEN = 2000\n",
    "hand_dynamics.teacher_scale = 0\n",
    "rnn.alpha = 0.005\n",
    "losses_test = []\n",
    "crit = PinballCriterion(ACC_PENALTY)\n",
    "\n",
    "for n in range(N_TEST):\n",
    "    \n",
    "    # randomize hand position (no velocity).\n",
    "    hand_dynamics.initialize(SCREEN_SIZE / 2, SCREEN_SIZE / 2)\n",
    "    hand, _, teach_signal = hand_dynamics(\n",
    "        torch.zeros(2),\n",
    "        torch.tensor([SCREEN_SIZE / 2, SCREEN_SIZE / 2]) # initial \"target\"\n",
    "    )\n",
    "\n",
    "    # Initialize task.\n",
    "    hidden = None\n",
    "    task = CenterOutTask(hand, SCREEN_SIZE, MIN_DELAY, MAX_DELAY, MOVE_TARG_THRES, HOLD_TARG_THRES, PADDING)\n",
    "    inp = task(hand)\n",
    "\n",
    "    # Allocate storage\n",
    "    inp_hist_test = torch.empty(TEST_SEQLEN, 7)\n",
    "    acc_hist_test = torch.empty(TEST_SEQLEN, 2)\n",
    "    hidden_hist_test = torch.empty(TEST_SEQLEN, N_NEURONS)\n",
    "    teach_signal_hist = torch.empty(TEST_SEQLEN, 2)\n",
    "    loss = torch.tensor([0.0])\n",
    "\n",
    "    # Now simulate the trial\n",
    "    for t in range(TEST_SEQLEN):\n",
    "\n",
    "        # Update the RNN activity\n",
    "        acc, hidden = rnn(inp, teach_signal, hidden)\n",
    "\n",
    "        if inp[0, 0] > 0.1:\n",
    "            active_target = inp[0, 5:7] # go cue on\n",
    "        else:\n",
    "            active_target = inp[0, 3:5] # go cue off\n",
    "\n",
    "        # Update hand position one time step\n",
    "        hand, teach_loss, teach_signal = hand_dynamics(acc, active_target)\n",
    "        loss += teach_loss\n",
    "        # Store outputs\n",
    "        inp_hist_test[t] = inp\n",
    "        acc_hist_test[t] = acc\n",
    "        hidden_hist_test[t] = hidden\n",
    "        teach_signal_hist[t] = teach_signal\n",
    "\n",
    "        # Update target locations.\n",
    "        inp = task(hand)\n",
    "    losses_test.append(loss.item())\n",
    "\n",
    "hx = inp_hist_test[:, 1].detach().numpy()\n",
    "hy = inp_hist_test[:, 2].detach().numpy()\n",
    "\n",
    "active_targ = crit.get_active_target(inp_hist_test)\n",
    "tx = active_targ[:, 0].detach().numpy()\n",
    "ty = active_targ[:, 1].detach().numpy()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(hx, hy, '.-b')\n",
    "plt.plot(tx, ty, 'or')\n",
    "plt.xlim([0, SCREEN_SIZE])\n",
    "plt.ylim([0, SCREEN_SIZE])\n",
    "\n",
    "# Save neuron firing rates\n",
    "torch.save(hidden_hist_test, './CenteroutModelResults-{}Epochs/neuronFiringRatesTest_MonkRNN_Centerout_Alpha0005_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "torch.save(inp_hist_test, './CenteroutModelResults-{}Epochs/handPositionsTest_MonkRNN_Centerout_Alpha0005_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "torch.save(acc_hist_test, './CenteroutModelResults-{}Epochs/handAccTest_MonkRNN_Centerout_Alpha0005_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "torch.save(teach_signal_hist, './CenteroutModelResults-{}Epochs/teach_signal_Test_MonkRNN_Centerout_Alpha0005_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "torch.save(losses_test, './CenteroutModelResults-{}Epochs/lossesTest_MonkRNN_Centerout_Alpha0005_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n0vR3xR1KDke",
    "outputId": "67d6911c-e4ab-4c7b-d645-7c656d15bbce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "203.41021804809571"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(losses_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "11H01-PbKDkg"
   },
   "outputs": [],
   "source": [
    "epoch = 12\n",
    "rnn = torch.load('./CenteroutModelResults-20Epochs/MonkRNN_TrainedModel_CenteroutEpoch{}.pt'.format(epoch))\n",
    "hand_dynamics = torch.load('./CenteroutModelResults-20Epochs/MonkRNN_TrainingHandDynamics_CenteroutEpoch{}.pt'.format(epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "avelyoX7KDki",
    "outputId": "d792f580-cecc-41c1-d51c-32b7877eb5a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TEST NETWORK ON NEW INPUTS FOR A PINBALL TASK, WITH TARGET_SEQUENCE AS THE RANDOM TARGET LOCATIONS##\n",
    "\n",
    "N_TEST = 50\n",
    "TEST_SEQLEN = 2000\n",
    "hand_dynamics.teacher_scale = 0\n",
    "rnn.alpha = 0.08\n",
    "losses_test = []\n",
    "crit = PinballCriterion(ACC_PENALTY)\n",
    "\n",
    "for n in range(N_TEST):\n",
    "    \n",
    "    # randomize hand position (no velocity).\n",
    "    hand_dynamics.initialize(SCREEN_SIZE / 2, SCREEN_SIZE / 2)\n",
    "    hand, _, teach_signal = hand_dynamics(\n",
    "        torch.zeros(2),\n",
    "        torch.tensor([SCREEN_SIZE / 2, SCREEN_SIZE / 2]) # initial \"target\"\n",
    "    )\n",
    "\n",
    "    # Initialize task.\n",
    "    hidden = None\n",
    "    task = PinballTask(hand, SCREEN_SIZE, MIN_DELAY, MAX_DELAY, MOVE_TARG_THRES, HOLD_TARG_THRES, PADDING)\n",
    "    inp = task(hand)\n",
    "\n",
    "    # Allocate storage\n",
    "    inp_hist_test = torch.empty(TEST_SEQLEN, 7)\n",
    "    acc_hist_test = torch.empty(TEST_SEQLEN, 2)\n",
    "    hidden_hist_test = torch.empty(TEST_SEQLEN, N_NEURONS)\n",
    "    teach_signal_hist = torch.empty(TEST_SEQLEN, 2)\n",
    "    loss = torch.tensor([0.0])\n",
    "    # Now simulate the trial\n",
    "    for t in range(TEST_SEQLEN):\n",
    "\n",
    "        # Update the RNN activity\n",
    "        acc, hidden = rnn(inp, teach_signal, hidden)\n",
    "\n",
    "        if inp[0, 0] > 0.1:\n",
    "            active_target = inp[0, 5:7] # go cue on\n",
    "        else:\n",
    "            active_target = inp[0, 3:5] # go cue off\n",
    "\n",
    "        # Update hand position one time step\n",
    "        hand, teach_loss, teach_signal = hand_dynamics(acc, active_target)\n",
    "        loss += teach_loss\n",
    "\n",
    "        # Store outputs\n",
    "        inp_hist_test[t] = inp\n",
    "        acc_hist_test[t] = acc\n",
    "        hidden_hist_test[t] = hidden\n",
    "        teach_signal_hist[t] = teach_signal\n",
    "\n",
    "        # Update target locations.\n",
    "        inp = task(hand)\n",
    "    losses_test.append(loss.item())\n",
    "\n",
    "hx = inp_hist_test[:, 1].detach().numpy()\n",
    "hy = inp_hist_test[:, 2].detach().numpy()\n",
    "\n",
    "active_targ = crit.get_active_target(inp_hist_test)\n",
    "tx = active_targ[:, 0].detach().numpy()\n",
    "ty = active_targ[:, 1].detach().numpy()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(hx, hy, '.-b')\n",
    "plt.plot(tx, ty, 'or')\n",
    "plt.xlim([0, SCREEN_SIZE])\n",
    "plt.ylim([0, SCREEN_SIZE])\n",
    "\n",
    "# Save neuron firing rates\n",
    "# torch.save(hidden_hist_test, './CenteroutModelResults-{}Epochs/neuronFiringRatesTest_MonkRNN_Pinball_Alpha010_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "# torch.save(inp_hist_test, './CenteroutModelResults-{}Epochs/handPositionsTest_MonkRNN_Pinball_Alpha010_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "# torch.save(acc_hist_test, './CenteroutModelResults-{}Epochs/handAccTest_MonkRNN_Pinball_Alpha010_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "# torch.save(teach_signal_hist, './CenteroutModelResults-{}Epochs/teach_signal_Test_MonkRNN_Pinball_Alpha010_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))\n",
    "torch.save(losses_test, './CenteroutModelResults-{}Epochs/lossesTest_MonkRNN_Pinball_Alpha008_Epoch{}.pt'.format(N_TRAIN_EPOCHS, epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "smllYDcDKDkk",
    "outputId": "e7cd9258-c938-48e0-8851-62f672a49bd7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12240.601462402345"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(losses_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f2b_EWPzKDkm",
    "outputId": "02cf694b-9108-413f-ba03-a1ef737535d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x105e3a470>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "for k, ax in enumerate(axes):\n",
    "    ax.plot(acc_hist_test.detach().numpy()[0:501, k], label='network')\n",
    "    ax.plot(teach_signal_hist.detach().numpy()[0:501, k], label='teacher')\n",
    "axes[-1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z6NqLeGTKDko",
    "outputId": "71767689-50ac-4a25-833d-f6d0bb1682f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1267045c0>]"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hidden_hist_test[0:501,2].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r4xn9hmAKDkq"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "teacher-CenteroutTraining.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}