central_concepts_in_ML_for_decoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/KordingLab/Neural_Decoding/blob/classifiers/central_concepts_in_ML_for_decoding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w2FCVtm3EhuL"
   },
   "source": [
    "# Decoding neural activity\n",
    "_Machine learning for mind reading_\n",
    "\n",
    "This tutorial introduces concepts that are central to the practice of decoding neural activity using machine learning (or any method). We will make heavy use of our [Python package for neural decoding](https://github.com/KordingLab/Neural_Decoding).\n",
    "\n",
    "This tutorial accompanies these [lecture slides](https://).\n",
    "\n",
    "The solutions to the exercises can be found in [this completed Colab](https://colab.research.google.com/drive/1SxXwmTgW2Ro34BQEy91zOXHs9JhMqcBJ).\n",
    "\n",
    "### Outline\n",
    "\n",
    "1. Overfitting\n",
    "2. Crossvalidation\n",
    "3. Regularization\n",
    "4. Applying recurrent neural networks\n",
    "5. What methods work best and when?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ufp0LsaTwe5B"
   },
   "source": [
    "## Preliminaries\n",
    "\n",
    "Import modules and simulate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ih_NKq_Xn98m"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6zqKK3iOwK6X"
   },
   "source": [
    "### Let's simulate some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate some fake trial data\n",
    "n_trials = 250\n",
    "n_neurons = 10\n",
    "n_bins_per_trial = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aKOAseCQmd5U"
   },
   "outputs": [],
   "source": [
    "# And generate some fake neural recordings\n",
    "# We'll pretend we have a drift diffusion model\n",
    "\n",
    "def generate_neural_data(n_trials, n_neurons = 25, n_bins_per_trial = 50,\n",
    "                         noise_variance = 1, drift_rate = 0.07,\n",
    "                         mean_rate = 25):\n",
    "    '''Generates fake neural data of shape (n_trials, n_neurons, n_bins_per_trial\n",
    "        according to a drift diffusion process with given parameters.\n",
    "     Also generates decisions, which is 0 or 1 depending on the \"animal's decision\"\n",
    "        and is returned as an array of shape (n_trials,) \n",
    "        \n",
    "    Returns: (neural_data, decisions)\n",
    "    '''\n",
    "\n",
    "    decisions = np.random.binomial(1,.5,size = n_trials)\n",
    "  \n",
    "    neural_recordings = np.zeros((n_trials,n_neurons,n_bins_per_trial))\n",
    "\n",
    "    for t in range(n_bins_per_trial):\n",
    "        if t==0:\n",
    "            neural_recordings[:,:,t] = mean_rate + np.random.randn(n_trials,n_neurons) * noise_variance\n",
    "        else:\n",
    "            neural_recordings[:,:,t] = neural_recordings[:,:,t-1] \\\n",
    "                      + np.reshape(drift_rate*(decisions*2-1),(len(decisions),1)) \\\n",
    "                      + np.random.randn(n_trials,n_neurons) * noise_variance\n",
    "      \n",
    "    return neural_recordings, decisions\n",
    "\n",
    "neural_recordings, decisions = generate_neural_data(n_trials, \n",
    "                                         n_neurons, n_bins_per_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(decisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "bN82ZMD7q0X1",
    "outputId": "86300c04-0da6-4d08-9e92-9388bac8e82f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe4c75e96d8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot out some trials for one neuron\n",
    "\n",
    "neuron_id = 4\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(np.arange(0,500,10),neural_recordings[:60,neuron_id,:].T)\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Spike rate (Hz)\")\n",
    "plt.legend([\"Trial {}\".format(i) for i in range(1,6)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "euk_r7MYJ9PS"
   },
   "source": [
    "#### Exercise 0: Look at the distribution of neural activity \n",
    "\n",
    "Let's gain an intuition for the data. Plot out the average neural activity for each of the two choices. \n",
    "\n",
    "Average across trials **and** neurons — we're going to assume they have the same response properties. Then, plot out the averages for trials with a choices of 0 vs a choice of 1.\n",
    "\n",
    "If you have extra time, overlay the standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 10, 50)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_recordings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_dec0 = np.mean(np.mean(neural_recordings[decisions == 0,:,:], axis=0), axis=0)\n",
    "std_dec0     = np.std(np.mean(neural_recordings[decisions == 0,:,:], axis=0), axis=0)\n",
    "average_dec1 = np.mean(np.mean(neural_recordings[decisions == 1,:,:], axis=0), axis=0)\n",
    "std_dec1     = np.std(np.mean(neural_recordings[decisions == 1,:,:], axis=0), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe55d13ef60>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "#plt.plot(np.arange(0,500,10),average_dec1)\n",
    "plt.errorbar(np.arange(0,500,10), average_dec0, std_dec0, linestyle='None', marker='.', label='Decision 0')\n",
    "plt.errorbar(np.arange(0,500,10), average_dec1, std_dec1, linestyle='None', marker='.', label='Decision 1')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Spike rate (Hz)\")\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3r6MNcOQN7Kc"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JhQtKkGZgrvB"
   },
   "source": [
    "## 1. Overfitting\n",
    "\n",
    "<img src='https://cdn-images-1.medium.com/max/1600/1*SBUK2QEfCP-zvJmKm14wGQ.png' />\n",
    "\n",
    "In common parlance, we call a classifier or regressor 'overfit' when it has learned to explain noise in the training set at the expense of its ability to generalize to new data.\n",
    "\n",
    "In that one-sentence description, we invoked the concepts of _training set_ and _generalization_. These are absolutely key to any modeling effort, including decoding.\n",
    "\n",
    "##### Training sets vs. testing sets\n",
    "You need to train your decoder, obviously, and for that you'll need training data. No one cares how well your decoder works on your training data, though. What we care about is its _performance on data not used for training_.  That is, we are interested in how well your decoder **generalizes** to new data. The only way to rigorously know how well your decoder generalizes is to test it on data not used for training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8ge2NEhXUD8E"
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "u1EhoS2kgsZT",
    "outputId": "2b9cc4fa-06ba-489d-8b1a-8f78e04da00a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 was 1.0\n"
     ]
    }
   ],
   "source": [
    "## Fit a decoder using all your data\n",
    "\n",
    "# We'll predict each decision using all neurons's activity throughout the trials\n",
    "X = np.reshape(neural_recordings, (n_trials,-1))\n",
    "\n",
    "# initalize the model\n",
    "my_naive_model = linear_model.LinearRegression()\n",
    "\n",
    "#fit\n",
    "my_naive_model.fit(X, decisions)\n",
    "\n",
    "# And see how we did on our data set,\n",
    "print(\"R2 was\",my_naive_model.score(X,decisions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 500)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(15,6))\n",
    "ax = figure.add_subplot(111)\n",
    "for trial in range(X.shape[0]):\n",
    "    ax.plot(np.mean(X[trial,:].reshape((n_neurons,-1)),axis=0), c=['red' if not decisions[trial] else 'green'][0], alpha=.3)\n",
    "\n",
    "sns.despine(left=True,bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aATf_EwnnQyC"
   },
   "source": [
    "**Looks great!** \n",
    "\n",
    "#### Exercise 1.1: \n",
    "Now, suppose you release your decoder in the world. Will it work? Can't be better than perfect, right?\n",
    "\n",
    "Create some new data and use the `score` function to see the $R^2$ of your model on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3OmY3-jDnWjR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3583780446279863"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's do new electrophysiology and take new data. This cost $2,000,000 in \n",
    "# NIH funding so it better work.\n",
    "new_neural_recordings, new_decisions = generate_neural_data(n_trials, \n",
    "                                             n_neurons, n_bins_per_trial)\n",
    "new_X = np.reshape(new_neural_recordings, (n_trials,-1))\n",
    "\n",
    "\n",
    "# Now you: score the model on this new data\n",
    "my_naive_model.score(new_X, new_decisions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iBrTBP3oZ16v"
   },
   "source": [
    "#### Exercise 1.2: \n",
    "You may have noticed that we're using linear *regression*, even though we have a classification problem.\n",
    "It'd be better to use logistic regression. \n",
    "\n",
    "Fit and score this logistic regression method using your original data.\n",
    "\n",
    "Then, also score this method with the new data you just obtained with your R01 funds. (That is, print both the test and train accuracy.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C13c7lGIYZZa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 of train: 1.0 R^2 of test: 0.916\n"
     ]
    }
   ],
   "source": [
    "log_reg = linear_model.LogisticRegression(penalty='none', solver = 'lbfgs',\n",
    "                                          max_iter = 1000)\n",
    "\n",
    "# fit\n",
    "log_reg.fit(X, decisions)\n",
    "# now score on both test and train\n",
    "print(f'R^2 of train: {log_reg.score(X,decisions)} R^2 of test: {log_reg.score(new_X, new_decisions)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aYdsXI596Gck"
   },
   "source": [
    "Once you've completed that, just run this next cell. \n",
    "\n",
    "It shows the coefficients of the fit you just made. Does it match your intuitions? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#n_trials = 250\n",
    "#n_neurons = 10\n",
    "#n_bins_per_trial = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 50)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.coef_.reshape(n_neurons, n_bins_per_trial).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dMYFYr5VzPXy"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_coefs(fit_model):\n",
    "    \"\"\"Makes a nice plot of the coefficients. fit_model is the model instance after fitting.\"\"\"\n",
    "    # get the coefficients of your fit\n",
    "    coefficients = fit_model.coef_.reshape(n_neurons, n_bins_per_trial)\n",
    "\n",
    "    # show them\n",
    "    plt.figure(figsize = (10,5))\n",
    "    plt.imshow(coefficients, cmap = 'coolwarm', vmin = -np.max(coefficients), \n",
    "                                                 vmax = np.max(coefficients))\n",
    "    #make it pretty\n",
    "    plt.ylabel(\"Neuron #\", fontsize = 14)\n",
    "    plt.xlabel(\"Time (ms)\", fontsize = 14)\n",
    "    plt.colorbar(orientation = 'horizontal', shrink = .6, \n",
    "                        label=\"Contribution of bin to decision; its 'Vote' towards a positive decision.\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_coefs(log_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ED41_d9dniIE"
   },
   "source": [
    "## 2. Crossvalidation\n",
    "\n",
    "We saw in the last section that if we used all of our training data to fit a method, we have no idea how our method will work \n",
    "on new data. This a problem for any engineering purpose.\n",
    "\n",
    "We also have no idea if our coefficients are meaningful or reflect random noise in the data. This is a problem for the science.\n",
    "\n",
    "The way to address this problem is to **split your data into a training segment and a validation segment**. We'll try an 80%/20% split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = int(n_trials*4/5)\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZwRNhIco_nrS"
   },
   "outputs": [],
   "source": [
    "training_data = X[:split,:]\n",
    "validation_data = X[split:,:]\n",
    "\n",
    "training_decisions = decisions[:split]\n",
    "validation_decisions = decisions[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 500)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xii4Ow-nAQVV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.94\n"
     ]
    }
   ],
   "source": [
    "# fit on the training data. (Don't forget to reinitialize your decoder first.)\n",
    "log_reg = linear_model.LogisticRegression(penalty='none', solver = 'lbfgs',\n",
    "                                          max_iter = 10000)\n",
    "\n",
    "\n",
    "# fit and score on training\n",
    "log_reg.fit(training_data,training_decisions)\n",
    "score_validation = log_reg.score(validation_data,validation_decisions)\n",
    "print('Score: {:.2f}'.format(score_validation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SeUInXtmAQYQ"
   },
   "outputs": [],
   "source": [
    "# score on the validation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bo94tWYoAQbF"
   },
   "source": [
    "But right now we're only testing on 20% of the data! Small data means high variance, so maybe we can't trust these scores much.\n",
    "\n",
    "A common practice is therefore to perform __k-fold crossvalidation__. This just means we rotate which segment of the original data is the validation set. We can then average the scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CK6-aoITAQel"
   },
   "outputs": [],
   "source": [
    "# Just run this.\n",
    "def get_test_train_splits(data, decisions, n_folds=5):\n",
    "    \"\"\"\n",
    "    Returns a tuple of matched train sets and validation sets, rotating through the data.\n",
    "    Note that there are scikit-learn functions that do this, too.\"\"\"\n",
    "\n",
    "    fold_size = len(data)//n_folds\n",
    "    print(fold_size)\n",
    "    training_sets = [np.roll(data,fold_size*i,axis=0)[fold_size:,:] for i in range(n_folds)]\n",
    "    val_sets = [np.roll(data,fold_size*i,axis=0)[:fold_size,:] for i in range(n_folds)]\n",
    "\n",
    "    training_Y = [np.roll(decisions,fold_size*i)[fold_size:] for i in range(n_folds)]\n",
    "    val_Y = [np.roll(decisions,fold_size*i)[:fold_size] for i in range(n_folds)]\n",
    "\n",
    "    return (training_sets, training_Y), (val_sets, val_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0EVx8pdAI7aB"
   },
   "source": [
    "#### Excercise 2\n",
    "\n",
    "Fill out the missing gaps in the script below. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j3KLNF9vGDnb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "Fold 0 of 5\n",
      "   Validation accuracy of 0.92\n",
      "   True test set accuracy of 0.904\n",
      "Fold 1 of 5\n",
      "   Validation accuracy of 0.94\n",
      "   True test set accuracy of 0.916\n",
      "Fold 2 of 5\n",
      "   Validation accuracy of 0.94\n",
      "   True test set accuracy of 0.928\n",
      "Fold 3 of 5\n",
      "   Validation accuracy of 0.92\n",
      "   True test set accuracy of 0.908\n",
      "Fold 4 of 5\n",
      "   Validation accuracy of 0.92\n",
      "   True test set accuracy of 0.92\n",
      "Mean validation accuracy: 0.9279999999999999\n"
     ]
    }
   ],
   "source": [
    "(training_sets, training_Ys), (val_sets, val_Ys) = get_test_train_splits(X, decisions)\n",
    "\n",
    "scores = []\n",
    "# Iterate through the k=5 folds\n",
    "for fold in range(5):\n",
    "    print(\"Fold {} of 5\".format(fold))\n",
    "\n",
    "    training_X = training_sets[fold]\n",
    "    training_Y = training_Ys[fold]\n",
    "\n",
    "    validation_X = val_sets[fold]\n",
    "    validation_Y = val_Ys[fold]\n",
    "\n",
    "    # Redefine the logistic regression model. (important to do this inside the loop)\n",
    "    log_reg = linear_model.LogisticRegression(penalty='l2', solver = 'lbfgs',\n",
    "                                          max_iter = 10000)\n",
    "\n",
    "    # Now fit on the training data\n",
    "    this_model.fit(training_X,training_Y)\n",
    "\n",
    "    # score on the validation data\n",
    "    this_accuracy = this_model.score(validation_X,validation_Y)\n",
    "\n",
    "    scores.append(this_accuracy)\n",
    "\n",
    "    # score on the new dataset (new_X, new_decisions from above)\n",
    "    accuracy_on_test_data = this_model.score(new_X,new_decisions)\n",
    "\n",
    "    print(\"   Validation accuracy of {}\".format( this_accuracy ))\n",
    "    print(\"   True test set accuracy of {}\".format( accuracy_on_test_data ))\n",
    "    #plot_coefs(this_model)\n",
    "\n",
    "print(\"Mean validation accuracy: {}\".format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ItAke3ZhGDpt"
   },
   "source": [
    "How close is the validation accuracy to the test set accuracy? \n",
    "\n",
    "How much did using 80% of the data affect the test set accuracy?\n",
    "\n",
    "## 3. Regularization\n",
    "\n",
    "Here we'll apply regularization, as talked about in the lecture.\n",
    "\n",
    "\n",
    "#### Exercise 3.1\n",
    "\n",
    "Go to the [scikit-learn docs](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) and decide which form of regularization you would like to use with logistic regression.\n",
    "\n",
    "#### Exercise 3.2\n",
    "\n",
    "Copy and paste the cell from _Excercise 2_, but this time change the `penalty` flag in the `LogisticRegression` call to the form of regularization you chose. \n",
    "\n",
    "\n",
    "First all other default parameters. Then set `C=1e-6` and see what happens.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rkVb9JB3GDrq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "Fold 0 of 5\n",
      "   Validation accuracy of 0.96\n",
      "   True test set accuracy of 0.916\n",
      "Fold 1 of 5\n",
      "   Validation accuracy of 0.96\n",
      "   True test set accuracy of 0.932\n",
      "Fold 2 of 5\n",
      "   Validation accuracy of 0.92\n",
      "   True test set accuracy of 0.944\n",
      "Fold 3 of 5\n",
      "   Validation accuracy of 0.9\n",
      "   True test set accuracy of 0.92\n",
      "Fold 4 of 5\n",
      "   Validation accuracy of 0.92\n",
      "   True test set accuracy of 0.916\n",
      "\n",
      "\n",
      "Mean validation accuracy: 0.932\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "(training_sets, training_Ys), (val_sets, val_Ys) = get_test_train_splits(X, decisions)\n",
    "\n",
    "scores = []\n",
    "\n",
    "# Iterate through the k=5 folds\n",
    "for fold in range(5):\n",
    "    print(\"Fold {} of 5\".format(fold))\n",
    "\n",
    "    training_X = training_sets[fold]\n",
    "    training_Y = training_Ys[fold]\n",
    "\n",
    "    validation_X = val_sets[fold]\n",
    "    validation_Y = val_Ys[fold]\n",
    "\n",
    "    # Redefine the logistic regression model. (important to do this inside the loop)\n",
    "    this_model = linear_model.LogisticRegression(penalty='l2', solver='sag',\n",
    "                                          max_iter = 1000)\n",
    "\n",
    "    # Now fit on the training data\n",
    "    this_model.fit(training_X,training_Y)\n",
    "\n",
    "    # score on the validation data\n",
    "    this_accuracy = this_model.score(validation_X,validation_Y)\n",
    "\n",
    "    scores.append(this_accuracy)\n",
    "\n",
    "    # score on the new dataset (new_X, new_decisions from above)\n",
    "    accuracy_on_test_data = this_model.score(new_X,new_decisions)\n",
    "\n",
    "    print(\"   Validation accuracy of {}\".format( this_accuracy ))\n",
    "    print(\"   True test set accuracy of {}\".format( accuracy_on_test_data ))\n",
    "\n",
    "print(\"\\n\\nMean validation accuracy: {}\\n\\n\".format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fBksWszaLgi5"
   },
   "source": [
    "#### Exercise 3.3: Choose the best regularization penalty\n",
    "\n",
    "Let's automate the above process so that we can choose the best performing value of `C`.\n",
    "\n",
    "First, write a function (largely copy and paste from the last exercise) that returns the mean validation accuracy for\n",
    "an arbitrary value of `C`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UXsa18cbqt67"
   },
   "outputs": [],
   "source": [
    "# First complete this function that takes data (e.g. X) \n",
    "# and a model, and returns the average validation accuracy from 5-fold CV\n",
    "# (Largely copy and past from above, but deleting print statements)\n",
    "def get_kfold_validation_score(data, decisions, C):\n",
    "\n",
    "    (training_sets, training_Ys), (val_sets, val_Ys) = get_test_train_splits(data, decisions)\n",
    "\n",
    "    scores = []\n",
    "\n",
    "    # Iterate through the k=5 folds\n",
    "    for fold in range(5):\n",
    "        training_X = training_sets[fold]\n",
    "        training_Y = training_Ys[fold]\n",
    "\n",
    "        validation_X = val_sets[fold]\n",
    "        validation_Y = val_Ys[fold]\n",
    "\n",
    "        # Redefine the logistic regression model. (important to do this inside the loop)\n",
    "        this_model = linear_model.LogisticRegression(penalty='l2', solver='lbfgs',\n",
    "                                                          max_iter = 10000, C=C)\n",
    "\n",
    "        # Now fit on the training data\n",
    "        this_model.fit(training_X,training_Y)\n",
    "\n",
    "        # score on the validation data\n",
    "        this_accuracy = this_model.score(validation_X,validation_Y)\n",
    "\n",
    "        scores.append(this_accuracy)\n",
    "\n",
    "        # score on the new dataset (new_X, new_decisions from above)\n",
    "        accuracy_on_test_data = this_model.score(new_X,new_decisions)\n",
    "        \n",
    "    mean_validation_accuracy = np.mean(scores)\n",
    "    print(\"Mean validation accuracy: {}\".format(mean_validation_accuracy))\n",
    "\n",
    "    return mean_validation_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7QFQQAj-rFIF"
   },
   "source": [
    "Now let's create a loop for various values of `C` and see what's the best.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4cobrMeXLgoZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "Mean validation accuracy: 0.9400000000000001\n",
      "50\n",
      "Mean validation accuracy: 0.932\n",
      "50\n",
      "Mean validation accuracy: 0.932\n",
      "50\n",
      "Mean validation accuracy: 0.932\n",
      "50\n",
      "Mean validation accuracy: 0.9199999999999999\n",
      "50\n",
      "Mean validation accuracy: 0.9279999999999999\n",
      "50\n",
      "Mean validation accuracy: 0.868\n",
      "50\n",
      "Mean validation accuracy: 0.612\n",
      "50\n",
      "Mean validation accuracy: 0.52\n",
      "50\n",
      "Mean validation accuracy: 0.52\n"
     ]
    }
   ],
   "source": [
    "# decide on a logarithmic schedule of Cs. \n",
    "Cs_to_test = [1/(10**x) for x in range(10)]\n",
    "\n",
    "validation_accuracy_vs_C = []\n",
    "for C in Cs_to_test: \n",
    "    # get the mean score on k-fold cross-validation using your function above\n",
    "    mean_validation_accuracy = get_kfold_validation_score(X, decisions, C)\n",
    "\n",
    "    validation_accuracy_vs_C.append(mean_validation_accuracy)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "30dujMtRNSFP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'validation acc.')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now plot the lists you just made. (Just run this.)\n",
    "plt.semilogx(Cs_to_test, validation_accuracy_vs_C,\"o-\")\n",
    "plt.xlabel(\"C\",fontsize=16)\n",
    "plt.ylabel(\"validation acc.\",fontsize=16)\n",
    "#plt.ylim([.8,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zaPCPWdJSqSf"
   },
   "source": [
    "## 4. Applying recurrent neural networks\n",
    "\n",
    "This section is going to be a bit of an introduction to our [python package for neural decoding](https://github.com/KordingLab/Neural_Decoding).\n",
    "\n",
    "First let's install the package. This will also install all the dependencies we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n7R8Wu-iSqSk"
   },
   "outputs": [],
   "source": [
    "from Neural_Decoding import decoders\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mNRHoZ8kSqSm"
   },
   "source": [
    "Let's take a look at the documentation of the decoder we'll use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoders.NaiveBayesRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PfPChxogSqSo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mdecoders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleRNNClassification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdropout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Class for the RNN decoder\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "units: integer, optional, default 400\n",
       "    Number of hidden units in each layer\n",
       "\n",
       "dropout: decimal, optional, default 0\n",
       "    Proportion of units that get dropped out\n",
       "\n",
       "num_epochs: integer, optional, default 10\n",
       "    Number of epochs used for training\n",
       "\n",
       "verbose: binary, optional, default=0\n",
       "    Whether to show progress of the fit after each epoch\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Documents/python/Neural_Decoding/Neural_Decoding/decoders.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run me\n",
    "?decoders.SimpleRNNClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CWLHbPsR_-rb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mdecoders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleRNNClassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Train GRU Decoder\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "X_train: numpy 3d array of shape [n_samples,n_time_bins,n_neurons]\n",
       "    This is the neural data.\n",
       "    See example file for an example of how to format the neural data correctly\n",
       "\n",
       "y_train: numpy 2d array of shape [n_samples, n_outputs]\n",
       "    This is the outputs that are being predicted\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Documents/python/Neural_Decoding/Neural_Decoding/decoders.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?decoders.SimpleRNNClassification.fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sLQd5QKsaKdz"
   },
   "source": [
    "You'll notice that the shape of the training inputs is now **3-dimensional**. This is because we can now explitly model the effect of time! Hooray! \n",
    "\n",
    "Recall that recurrent neural networks contain a \"hidden state\". The way this decoder works is that it reads all the neural activities in at the first time bin (out of `n_time_bins`) and then updates its hidden state accordingly. The value of this hidden state informs how the hidden state will evolve over time in each trial. After the hidden state updates `n_time_bin` times, it outputs its prediction for the animal's choice.\n",
    "\n",
    "\n",
    "Lets apply this classifier to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 10, 50)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_recordings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZGDcn7g6AVeN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training input data is of shape (200, 10, 50)\n",
      "Validation input data is of shape (50, 10, 50)\n"
     ]
    }
   ],
   "source": [
    "# lets re-split our original 3-dimensional trials/neurons/time input data.\n",
    "split = int(n_trials*4/5)\n",
    "\n",
    "training_data = neural_recordings[:split]\n",
    "validation_data = neural_recordings[split:]\n",
    "\n",
    "training_decisions = decisions[:split]\n",
    "validation_decisions = decisions[split:]\n",
    "\n",
    "print(\"Training input data is of shape\", training_data.shape)\n",
    "print(\"Validation input data is of shape\", validation_data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change axes \n",
    "training_data = np.moveaxis(training_data, 1, -1)\n",
    "validation_data = np.moveaxis(validation_data, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D5CLevam9m6N"
   },
   "outputs": [],
   "source": [
    "\n",
    "# first we instantiate the decoder\n",
    "my_RNN_classifier = decoders.SimpleRNNClassification(units = 50,\n",
    "                                                    dropout = 0,\n",
    "                                                    num_epochs =250,\n",
    "                                                    verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g2V2ttwE-SG9",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "200/200 [==============================] - 0s 1ms/step - loss: 0.9563 - accuracy: 0.4950\n",
      "Epoch 2/250\n",
      "200/200 [==============================] - 0s 412us/step - loss: 0.7636 - accuracy: 0.4900\n",
      "Epoch 3/250\n",
      "200/200 [==============================] - 0s 393us/step - loss: 0.7112 - accuracy: 0.4800\n",
      "Epoch 4/250\n",
      "200/200 [==============================] - 0s 443us/step - loss: 0.6820 - accuracy: 0.4800\n",
      "Epoch 5/250\n",
      "200/200 [==============================] - 0s 414us/step - loss: 0.6689 - accuracy: 0.5600\n",
      "Epoch 6/250\n",
      "200/200 [==============================] - 0s 423us/step - loss: 0.6576 - accuracy: 0.6350\n",
      "Epoch 7/250\n",
      "200/200 [==============================] - 0s 404us/step - loss: 0.6545 - accuracy: 0.6600\n",
      "Epoch 8/250\n",
      "200/200 [==============================] - 0s 398us/step - loss: 0.6482 - accuracy: 0.6750\n",
      "Epoch 9/250\n",
      "200/200 [==============================] - 0s 400us/step - loss: 0.6437 - accuracy: 0.6350\n",
      "Epoch 10/250\n",
      "200/200 [==============================] - 0s 414us/step - loss: 0.6365 - accuracy: 0.6600\n",
      "Epoch 11/250\n",
      "200/200 [==============================] - 0s 392us/step - loss: 0.6302 - accuracy: 0.6700\n",
      "Epoch 12/250\n",
      "200/200 [==============================] - 0s 362us/step - loss: 0.6293 - accuracy: 0.6850\n",
      "Epoch 13/250\n",
      "200/200 [==============================] - 0s 350us/step - loss: 0.6204 - accuracy: 0.6850\n",
      "Epoch 14/250\n",
      "200/200 [==============================] - 0s 374us/step - loss: 0.6173 - accuracy: 0.6950\n",
      "Epoch 15/250\n",
      "200/200 [==============================] - 0s 392us/step - loss: 0.6120 - accuracy: 0.6900\n",
      "Epoch 16/250\n",
      "200/200 [==============================] - 0s 423us/step - loss: 0.6069 - accuracy: 0.6850\n",
      "Epoch 17/250\n",
      "200/200 [==============================] - 0s 393us/step - loss: 0.6016 - accuracy: 0.7050\n",
      "Epoch 18/250\n",
      "200/200 [==============================] - 0s 368us/step - loss: 0.5959 - accuracy: 0.7100\n",
      "Epoch 19/250\n",
      "200/200 [==============================] - 0s 448us/step - loss: 0.5894 - accuracy: 0.7300\n",
      "Epoch 20/250\n",
      "200/200 [==============================] - 0s 465us/step - loss: 0.5873 - accuracy: 0.7250\n",
      "Epoch 21/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.5805 - accuracy: 0.7000\n",
      "Epoch 22/250\n",
      "200/200 [==============================] - 0s 423us/step - loss: 0.5731 - accuracy: 0.7250\n",
      "Epoch 23/250\n",
      "200/200 [==============================] - 0s 436us/step - loss: 0.5647 - accuracy: 0.7500\n",
      "Epoch 24/250\n",
      "200/200 [==============================] - 0s 468us/step - loss: 0.5643 - accuracy: 0.7350\n",
      "Epoch 25/250\n",
      "200/200 [==============================] - 0s 448us/step - loss: 0.5558 - accuracy: 0.7500\n",
      "Epoch 26/250\n",
      "200/200 [==============================] - 0s 447us/step - loss: 0.5493 - accuracy: 0.7600\n",
      "Epoch 27/250\n",
      "200/200 [==============================] - 0s 456us/step - loss: 0.5446 - accuracy: 0.7650\n",
      "Epoch 28/250\n",
      "200/200 [==============================] - 0s 512us/step - loss: 0.5404 - accuracy: 0.7650\n",
      "Epoch 29/250\n",
      "200/200 [==============================] - 0s 495us/step - loss: 0.5345 - accuracy: 0.7650\n",
      "Epoch 30/250\n",
      "200/200 [==============================] - 0s 472us/step - loss: 0.5279 - accuracy: 0.7800\n",
      "Epoch 31/250\n",
      "200/200 [==============================] - 0s 494us/step - loss: 0.5233 - accuracy: 0.7700\n",
      "Epoch 32/250\n",
      "200/200 [==============================] - 0s 496us/step - loss: 0.5186 - accuracy: 0.8050\n",
      "Epoch 33/250\n",
      "200/200 [==============================] - 0s 518us/step - loss: 0.5115 - accuracy: 0.7950\n",
      "Epoch 34/250\n",
      "200/200 [==============================] - 0s 548us/step - loss: 0.5082 - accuracy: 0.7700\n",
      "Epoch 35/250\n",
      "200/200 [==============================] - 0s 597us/step - loss: 0.5012 - accuracy: 0.8250\n",
      "Epoch 36/250\n",
      "200/200 [==============================] - 0s 477us/step - loss: 0.5055 - accuracy: 0.7950\n",
      "Epoch 37/250\n",
      "200/200 [==============================] - 0s 407us/step - loss: 0.4927 - accuracy: 0.8200\n",
      "Epoch 38/250\n",
      "200/200 [==============================] - 0s 397us/step - loss: 0.4846 - accuracy: 0.8100\n",
      "Epoch 39/250\n",
      "200/200 [==============================] - 0s 389us/step - loss: 0.4804 - accuracy: 0.8200\n",
      "Epoch 40/250\n",
      "200/200 [==============================] - 0s 391us/step - loss: 0.4817 - accuracy: 0.8250\n",
      "Epoch 41/250\n",
      "200/200 [==============================] - 0s 443us/step - loss: 0.4702 - accuracy: 0.8100\n",
      "Epoch 42/250\n",
      "200/200 [==============================] - 0s 434us/step - loss: 0.4638 - accuracy: 0.8300\n",
      "Epoch 43/250\n",
      "200/200 [==============================] - 0s 428us/step - loss: 0.4610 - accuracy: 0.8250\n",
      "Epoch 44/250\n",
      "200/200 [==============================] - 0s 466us/step - loss: 0.4548 - accuracy: 0.8350\n",
      "Epoch 45/250\n",
      "200/200 [==============================] - 0s 494us/step - loss: 0.4508 - accuracy: 0.8400\n",
      "Epoch 46/250\n",
      "200/200 [==============================] - 0s 505us/step - loss: 0.4427 - accuracy: 0.8400\n",
      "Epoch 47/250\n",
      "200/200 [==============================] - 0s 481us/step - loss: 0.4373 - accuracy: 0.8450\n",
      "Epoch 48/250\n",
      "200/200 [==============================] - 0s 469us/step - loss: 0.4446 - accuracy: 0.8300\n",
      "Epoch 49/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.4313 - accuracy: 0.8500\n",
      "Epoch 50/250\n",
      "200/200 [==============================] - 0s 459us/step - loss: 0.4285 - accuracy: 0.8300\n",
      "Epoch 51/250\n",
      "200/200 [==============================] - 0s 445us/step - loss: 0.4183 - accuracy: 0.8550\n",
      "Epoch 52/250\n",
      "200/200 [==============================] - 0s 419us/step - loss: 0.4211 - accuracy: 0.8500\n",
      "Epoch 53/250\n",
      "200/200 [==============================] - 0s 461us/step - loss: 0.4120 - accuracy: 0.8600\n",
      "Epoch 54/250\n",
      "200/200 [==============================] - 0s 444us/step - loss: 0.4043 - accuracy: 0.8400\n",
      "Epoch 55/250\n",
      "200/200 [==============================] - 0s 465us/step - loss: 0.4010 - accuracy: 0.8650\n",
      "Epoch 56/250\n",
      "200/200 [==============================] - 0s 461us/step - loss: 0.4007 - accuracy: 0.8500\n",
      "Epoch 57/250\n",
      "200/200 [==============================] - 0s 441us/step - loss: 0.3912 - accuracy: 0.8800\n",
      "Epoch 58/250\n",
      "200/200 [==============================] - 0s 420us/step - loss: 0.3880 - accuracy: 0.8750\n",
      "Epoch 59/250\n",
      "200/200 [==============================] - 0s 489us/step - loss: 0.3806 - accuracy: 0.8700\n",
      "Epoch 60/250\n",
      "200/200 [==============================] - 0s 453us/step - loss: 0.3760 - accuracy: 0.8700\n",
      "Epoch 61/250\n",
      "200/200 [==============================] - 0s 468us/step - loss: 0.3729 - accuracy: 0.8950\n",
      "Epoch 62/250\n",
      "200/200 [==============================] - 0s 466us/step - loss: 0.3722 - accuracy: 0.8900\n",
      "Epoch 63/250\n",
      "200/200 [==============================] - 0s 502us/step - loss: 0.3650 - accuracy: 0.8950\n",
      "Epoch 64/250\n",
      "200/200 [==============================] - 0s 507us/step - loss: 0.3552 - accuracy: 0.8950\n",
      "Epoch 65/250\n",
      "200/200 [==============================] - 0s 505us/step - loss: 0.3586 - accuracy: 0.8700\n",
      "Epoch 66/250\n",
      "200/200 [==============================] - 0s 496us/step - loss: 0.3496 - accuracy: 0.9000\n",
      "Epoch 67/250\n",
      "200/200 [==============================] - 0s 498us/step - loss: 0.3467 - accuracy: 0.8900\n",
      "Epoch 68/250\n",
      "200/200 [==============================] - 0s 515us/step - loss: 0.3372 - accuracy: 0.9000\n",
      "Epoch 69/250\n",
      "200/200 [==============================] - 0s 464us/step - loss: 0.3301 - accuracy: 0.8950\n",
      "Epoch 70/250\n",
      "200/200 [==============================] - 0s 426us/step - loss: 0.3290 - accuracy: 0.9100\n",
      "Epoch 71/250\n",
      "200/200 [==============================] - 0s 392us/step - loss: 0.3335 - accuracy: 0.8850\n",
      "Epoch 72/250\n",
      "200/200 [==============================] - 0s 347us/step - loss: 0.3174 - accuracy: 0.9200\n",
      "Epoch 73/250\n",
      "200/200 [==============================] - 0s 404us/step - loss: 0.3192 - accuracy: 0.9000\n",
      "Epoch 74/250\n",
      "200/200 [==============================] - 0s 463us/step - loss: 0.3307 - accuracy: 0.8950\n",
      "Epoch 75/250\n",
      "200/200 [==============================] - 0s 457us/step - loss: 0.3096 - accuracy: 0.9150\n",
      "Epoch 76/250\n",
      "200/200 [==============================] - 0s 498us/step - loss: 0.3054 - accuracy: 0.9000\n",
      "Epoch 77/250\n",
      "200/200 [==============================] - 0s 472us/step - loss: 0.3035 - accuracy: 0.9050\n",
      "Epoch 78/250\n",
      "200/200 [==============================] - 0s 472us/step - loss: 0.2990 - accuracy: 0.9100\n",
      "Epoch 79/250\n",
      "200/200 [==============================] - 0s 510us/step - loss: 0.2871 - accuracy: 0.9250\n",
      "Epoch 80/250\n",
      "200/200 [==============================] - 0s 498us/step - loss: 0.2933 - accuracy: 0.9150\n",
      "Epoch 81/250\n",
      "200/200 [==============================] - 0s 459us/step - loss: 0.2756 - accuracy: 0.9150\n",
      "Epoch 82/250\n",
      "200/200 [==============================] - 0s 465us/step - loss: 0.2774 - accuracy: 0.9250\n",
      "Epoch 83/250\n",
      "200/200 [==============================] - 0s 437us/step - loss: 0.2705 - accuracy: 0.9350\n",
      "Epoch 84/250\n",
      "200/200 [==============================] - 0s 442us/step - loss: 0.2724 - accuracy: 0.9400\n",
      "Epoch 85/250\n",
      "200/200 [==============================] - 0s 482us/step - loss: 0.2598 - accuracy: 0.9350\n",
      "Epoch 86/250\n",
      "200/200 [==============================] - 0s 465us/step - loss: 0.2662 - accuracy: 0.9200\n",
      "Epoch 87/250\n",
      "200/200 [==============================] - 0s 460us/step - loss: 0.2602 - accuracy: 0.9250\n",
      "Epoch 88/250\n",
      "200/200 [==============================] - 0s 459us/step - loss: 0.2557 - accuracy: 0.9400\n",
      "Epoch 89/250\n",
      "200/200 [==============================] - 0s 433us/step - loss: 0.2483 - accuracy: 0.9300\n",
      "Epoch 90/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.2491 - accuracy: 0.9600\n",
      "Epoch 91/250\n",
      "200/200 [==============================] - 0s 462us/step - loss: 0.2372 - accuracy: 0.9450\n",
      "Epoch 92/250\n",
      "200/200 [==============================] - 0s 466us/step - loss: 0.2307 - accuracy: 0.9400\n",
      "Epoch 93/250\n",
      "200/200 [==============================] - 0s 438us/step - loss: 0.2310 - accuracy: 0.9600\n",
      "Epoch 94/250\n",
      "200/200 [==============================] - 0s 311us/step - loss: 0.2272 - accuracy: 0.9550\n",
      "Epoch 95/250\n",
      "200/200 [==============================] - 0s 384us/step - loss: 0.2290 - accuracy: 0.9400\n",
      "Epoch 96/250\n",
      "200/200 [==============================] - 0s 460us/step - loss: 0.2151 - accuracy: 0.9450\n",
      "Epoch 97/250\n",
      "200/200 [==============================] - 0s 449us/step - loss: 0.2235 - accuracy: 0.9400\n",
      "Epoch 98/250\n",
      "200/200 [==============================] - 0s 461us/step - loss: 0.2157 - accuracy: 0.9600\n",
      "Epoch 99/250\n",
      "200/200 [==============================] - 0s 478us/step - loss: 0.2048 - accuracy: 0.9550\n",
      "Epoch 100/250\n",
      "200/200 [==============================] - 0s 455us/step - loss: 0.2095 - accuracy: 0.9700\n",
      "Epoch 101/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.2106 - accuracy: 0.9650\n",
      "Epoch 102/250\n",
      "200/200 [==============================] - 0s 435us/step - loss: 0.1965 - accuracy: 0.9750\n",
      "Epoch 103/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.1993 - accuracy: 0.9500\n",
      "Epoch 104/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.1937 - accuracy: 0.9600\n",
      "Epoch 105/250\n",
      "200/200 [==============================] - 0s 451us/step - loss: 0.1873 - accuracy: 0.9650\n",
      "Epoch 106/250\n",
      "200/200 [==============================] - 0s 459us/step - loss: 0.1820 - accuracy: 0.9700\n",
      "Epoch 107/250\n",
      "200/200 [==============================] - 0s 463us/step - loss: 0.1848 - accuracy: 0.9650\n",
      "Epoch 108/250\n",
      "200/200 [==============================] - 0s 457us/step - loss: 0.1866 - accuracy: 0.9750\n",
      "Epoch 109/250\n",
      "200/200 [==============================] - 0s 481us/step - loss: 0.1998 - accuracy: 0.9450\n",
      "Epoch 110/250\n",
      "200/200 [==============================] - 0s 452us/step - loss: 0.1795 - accuracy: 0.9600\n",
      "Epoch 111/250\n",
      "200/200 [==============================] - 0s 490us/step - loss: 0.1729 - accuracy: 0.9750\n",
      "Epoch 112/250\n",
      "200/200 [==============================] - 0s 498us/step - loss: 0.1721 - accuracy: 0.9650\n",
      "Epoch 113/250\n",
      "200/200 [==============================] - 0s 502us/step - loss: 0.1731 - accuracy: 0.9700\n",
      "Epoch 114/250\n",
      "200/200 [==============================] - 0s 497us/step - loss: 0.1580 - accuracy: 0.9800\n",
      "Epoch 115/250\n",
      "200/200 [==============================] - 0s 498us/step - loss: 0.1579 - accuracy: 0.9900\n",
      "Epoch 116/250\n",
      "200/200 [==============================] - 0s 427us/step - loss: 0.1616 - accuracy: 0.9650\n",
      "Epoch 117/250\n",
      "200/200 [==============================] - 0s 499us/step - loss: 0.1610 - accuracy: 0.9550\n",
      "Epoch 118/250\n",
      "200/200 [==============================] - 0s 490us/step - loss: 0.1502 - accuracy: 0.9800\n",
      "Epoch 119/250\n",
      "200/200 [==============================] - 0s 506us/step - loss: 0.1555 - accuracy: 0.9750\n",
      "Epoch 120/250\n",
      "200/200 [==============================] - 0s 500us/step - loss: 0.1568 - accuracy: 0.9750\n",
      "Epoch 121/250\n",
      "200/200 [==============================] - 0s 488us/step - loss: 0.1441 - accuracy: 0.9900\n",
      "Epoch 122/250\n",
      "200/200 [==============================] - 0s 490us/step - loss: 0.1642 - accuracy: 0.9700\n",
      "Epoch 123/250\n",
      "200/200 [==============================] - 0s 475us/step - loss: 0.1393 - accuracy: 0.9900\n",
      "Epoch 124/250\n",
      "200/200 [==============================] - 0s 498us/step - loss: 0.1373 - accuracy: 0.9900\n",
      "Epoch 125/250\n",
      "200/200 [==============================] - 0s 479us/step - loss: 0.1321 - accuracy: 0.9900\n",
      "Epoch 126/250\n",
      "200/200 [==============================] - 0s 456us/step - loss: 0.1312 - accuracy: 0.9850\n",
      "Epoch 127/250\n",
      "200/200 [==============================] - 0s 497us/step - loss: 0.1396 - accuracy: 0.9900\n",
      "Epoch 128/250\n",
      "200/200 [==============================] - 0s 456us/step - loss: 0.1363 - accuracy: 0.9750\n",
      "Epoch 129/250\n",
      "200/200 [==============================] - 0s 439us/step - loss: 0.1262 - accuracy: 0.9900\n",
      "Epoch 130/250\n",
      "200/200 [==============================] - 0s 456us/step - loss: 0.1276 - accuracy: 0.9850\n",
      "Epoch 131/250\n",
      "200/200 [==============================] - 0s 461us/step - loss: 0.1258 - accuracy: 0.9850\n",
      "Epoch 132/250\n",
      "200/200 [==============================] - 0s 463us/step - loss: 0.1174 - accuracy: 0.9950\n",
      "Epoch 133/250\n",
      "200/200 [==============================] - 0s 451us/step - loss: 0.1150 - accuracy: 0.9950\n",
      "Epoch 134/250\n",
      "200/200 [==============================] - 0s 424us/step - loss: 0.1173 - accuracy: 0.9900\n",
      "Epoch 135/250\n",
      "200/200 [==============================] - ETA: 0s - loss: 0.1225 - accuracy: 0.98 - 0s 432us/step - loss: 0.1194 - accuracy: 0.9850\n",
      "Epoch 136/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.1117 - accuracy: 0.9950\n",
      "Epoch 137/250\n",
      "200/200 [==============================] - 0s 389us/step - loss: 0.1050 - accuracy: 0.9950\n",
      "Epoch 138/250\n",
      "200/200 [==============================] - 0s 381us/step - loss: 0.1082 - accuracy: 0.9950\n",
      "Epoch 139/250\n",
      "200/200 [==============================] - 0s 411us/step - loss: 0.1033 - accuracy: 0.9950\n",
      "Epoch 140/250\n",
      "200/200 [==============================] - 0s 417us/step - loss: 0.1166 - accuracy: 0.9850\n",
      "Epoch 141/250\n",
      "200/200 [==============================] - 0s 430us/step - loss: 0.1047 - accuracy: 0.9950\n",
      "Epoch 142/250\n",
      "200/200 [==============================] - 0s 443us/step - loss: 0.1026 - accuracy: 0.9900\n",
      "Epoch 143/250\n",
      "200/200 [==============================] - 0s 416us/step - loss: 0.1054 - accuracy: 0.9900\n",
      "Epoch 144/250\n",
      "200/200 [==============================] - 0s 407us/step - loss: 0.0973 - accuracy: 0.9950\n",
      "Epoch 145/250\n",
      "200/200 [==============================] - 0s 400us/step - loss: 0.0954 - accuracy: 0.9950\n",
      "Epoch 146/250\n",
      "200/200 [==============================] - 0s 428us/step - loss: 0.0893 - accuracy: 0.9950\n",
      "Epoch 147/250\n",
      "200/200 [==============================] - 0s 418us/step - loss: 0.1044 - accuracy: 0.9900\n",
      "Epoch 148/250\n",
      "200/200 [==============================] - 0s 457us/step - loss: 0.0847 - accuracy: 0.9950\n",
      "Epoch 149/250\n",
      "200/200 [==============================] - 0s 457us/step - loss: 0.0849 - accuracy: 0.9950\n",
      "Epoch 150/250\n",
      "200/200 [==============================] - 0s 404us/step - loss: 0.0972 - accuracy: 0.9850\n",
      "Epoch 151/250\n",
      "200/200 [==============================] - 0s 356us/step - loss: 0.0830 - accuracy: 0.9950\n",
      "Epoch 152/250\n",
      "200/200 [==============================] - 0s 416us/step - loss: 0.0796 - accuracy: 0.9950\n",
      "Epoch 153/250\n",
      "200/200 [==============================] - 0s 394us/step - loss: 0.0778 - accuracy: 0.9950\n",
      "Epoch 154/250\n",
      "200/200 [==============================] - 0s 394us/step - loss: 0.0912 - accuracy: 0.9900\n",
      "Epoch 155/250\n",
      "200/200 [==============================] - 0s 407us/step - loss: 0.0790 - accuracy: 0.9950\n",
      "Epoch 156/250\n",
      "200/200 [==============================] - 0s 390us/step - loss: 0.0781 - accuracy: 0.9950\n",
      "Epoch 157/250\n",
      "200/200 [==============================] - 0s 363us/step - loss: 0.0926 - accuracy: 0.9800\n",
      "Epoch 158/250\n",
      "200/200 [==============================] - 0s 378us/step - loss: 0.0720 - accuracy: 0.9950\n",
      "Epoch 159/250\n",
      "200/200 [==============================] - 0s 391us/step - loss: 0.0676 - accuracy: 0.9950\n",
      "Epoch 160/250\n",
      "200/200 [==============================] - 0s 440us/step - loss: 0.0676 - accuracy: 0.9950\n",
      "Epoch 161/250\n",
      "200/200 [==============================] - 0s 446us/step - loss: 0.0736 - accuracy: 0.9950\n",
      "Epoch 162/250\n",
      "200/200 [==============================] - 0s 449us/step - loss: 0.0688 - accuracy: 0.9950\n",
      "Epoch 163/250\n",
      "200/200 [==============================] - 0s 442us/step - loss: 0.0692 - accuracy: 0.9900\n",
      "Epoch 164/250\n",
      "200/200 [==============================] - 0s 343us/step - loss: 0.0648 - accuracy: 0.9950\n",
      "Epoch 165/250\n",
      "200/200 [==============================] - 0s 333us/step - loss: 0.0766 - accuracy: 0.9900\n",
      "Epoch 166/250\n",
      "200/200 [==============================] - 0s 366us/step - loss: 0.0605 - accuracy: 0.9950\n",
      "Epoch 167/250\n",
      "200/200 [==============================] - 0s 400us/step - loss: 0.0614 - accuracy: 0.9950\n",
      "Epoch 168/250\n",
      "200/200 [==============================] - 0s 408us/step - loss: 0.0589 - accuracy: 0.9950\n",
      "Epoch 169/250\n",
      "200/200 [==============================] - 0s 392us/step - loss: 0.0636 - accuracy: 0.9900\n",
      "Epoch 170/250\n",
      "200/200 [==============================] - 0s 381us/step - loss: 0.0588 - accuracy: 0.9950\n",
      "Epoch 171/250\n",
      "200/200 [==============================] - 0s 380us/step - loss: 0.0549 - accuracy: 0.9950\n",
      "Epoch 172/250\n",
      "200/200 [==============================] - 0s 387us/step - loss: 0.0545 - accuracy: 0.9950\n",
      "Epoch 173/250\n",
      "200/200 [==============================] - 0s 385us/step - loss: 0.0563 - accuracy: 0.9950\n",
      "Epoch 174/250\n",
      "200/200 [==============================] - 0s 410us/step - loss: 0.0595 - accuracy: 0.9900\n",
      "Epoch 175/250\n",
      "200/200 [==============================] - 0s 368us/step - loss: 0.0565 - accuracy: 0.9950\n",
      "Epoch 176/250\n",
      "200/200 [==============================] - 0s 429us/step - loss: 0.0567 - accuracy: 0.9950\n",
      "Epoch 177/250\n",
      "200/200 [==============================] - 0s 413us/step - loss: 0.0515 - accuracy: 0.9950\n",
      "Epoch 178/250\n",
      "200/200 [==============================] - 0s 381us/step - loss: 0.0895 - accuracy: 0.9800\n",
      "Epoch 179/250\n",
      "200/200 [==============================] - 0s 384us/step - loss: 0.0467 - accuracy: 0.9950\n",
      "Epoch 180/250\n",
      "200/200 [==============================] - 0s 385us/step - loss: 0.0466 - accuracy: 0.9950\n",
      "Epoch 181/250\n",
      "200/200 [==============================] - 0s 442us/step - loss: 0.0449 - accuracy: 0.9950\n",
      "Epoch 182/250\n",
      "200/200 [==============================] - 0s 461us/step - loss: 0.0466 - accuracy: 0.9950\n",
      "Epoch 183/250\n",
      "200/200 [==============================] - 0s 447us/step - loss: 0.0446 - accuracy: 0.9950\n",
      "Epoch 184/250\n",
      "200/200 [==============================] - 0s 420us/step - loss: 0.0466 - accuracy: 0.9950\n",
      "Epoch 185/250\n",
      "200/200 [==============================] - 0s 424us/step - loss: 0.0712 - accuracy: 0.9850\n",
      "Epoch 186/250\n",
      "200/200 [==============================] - 0s 382us/step - loss: 0.0427 - accuracy: 0.9950\n",
      "Epoch 187/250\n",
      "200/200 [==============================] - 0s 512us/step - loss: 0.0441 - accuracy: 0.9950\n",
      "Epoch 188/250\n",
      "200/200 [==============================] - 0s 393us/step - loss: 0.0418 - accuracy: 0.9950\n",
      "Epoch 189/250\n",
      "200/200 [==============================] - 0s 311us/step - loss: 0.0417 - accuracy: 0.9950\n",
      "Epoch 190/250\n",
      "200/200 [==============================] - 0s 384us/step - loss: 0.0434 - accuracy: 0.9950\n",
      "Epoch 191/250\n",
      "200/200 [==============================] - 0s 390us/step - loss: 0.0441 - accuracy: 0.9950\n",
      "Epoch 192/250\n",
      "200/200 [==============================] - 0s 371us/step - loss: 0.0430 - accuracy: 0.9950\n",
      "Epoch 193/250\n",
      "200/200 [==============================] - 0s 411us/step - loss: 0.0698 - accuracy: 0.9800\n",
      "Epoch 194/250\n",
      "200/200 [==============================] - 0s 513us/step - loss: 0.0394 - accuracy: 0.9950\n",
      "Epoch 195/250\n",
      "200/200 [==============================] - 0s 484us/step - loss: 0.0366 - accuracy: 0.9950\n",
      "Epoch 196/250\n",
      "200/200 [==============================] - 0s 479us/step - loss: 0.0392 - accuracy: 0.9950\n",
      "Epoch 197/250\n",
      "200/200 [==============================] - 0s 469us/step - loss: 0.0376 - accuracy: 0.9950\n",
      "Epoch 198/250\n",
      "200/200 [==============================] - 0s 490us/step - loss: 0.0386 - accuracy: 0.9950\n",
      "Epoch 199/250\n",
      "200/200 [==============================] - 0s 396us/step - loss: 0.0358 - accuracy: 0.9950\n",
      "Epoch 200/250\n",
      "200/200 [==============================] - 0s 532us/step - loss: 0.0349 - accuracy: 0.9950\n",
      "Epoch 201/250\n",
      "200/200 [==============================] - 0s 401us/step - loss: 0.0381 - accuracy: 0.9950\n",
      "Epoch 202/250\n",
      "200/200 [==============================] - 0s 424us/step - loss: 0.0363 - accuracy: 0.9950\n",
      "Epoch 203/250\n",
      "200/200 [==============================] - 0s 413us/step - loss: 0.0346 - accuracy: 0.9950\n",
      "Epoch 204/250\n",
      "200/200 [==============================] - 0s 368us/step - loss: 0.0583 - accuracy: 0.9800\n",
      "Epoch 205/250\n",
      "200/200 [==============================] - 0s 362us/step - loss: 0.0343 - accuracy: 0.9950\n",
      "Epoch 206/250\n",
      "200/200 [==============================] - 0s 322us/step - loss: 0.0323 - accuracy: 0.9950\n",
      "Epoch 207/250\n",
      "200/200 [==============================] - 0s 357us/step - loss: 0.0324 - accuracy: 0.9950\n",
      "Epoch 208/250\n",
      "200/200 [==============================] - 0s 374us/step - loss: 0.0321 - accuracy: 0.9950\n",
      "Epoch 209/250\n",
      "200/200 [==============================] - 0s 434us/step - loss: 0.0325 - accuracy: 0.9950\n",
      "Epoch 210/250\n",
      "200/200 [==============================] - 0s 458us/step - loss: 0.0324 - accuracy: 0.9950\n",
      "Epoch 211/250\n",
      "200/200 [==============================] - 0s 496us/step - loss: 0.0358 - accuracy: 0.9950\n",
      "Epoch 212/250\n",
      "200/200 [==============================] - 0s 392us/step - loss: 0.0354 - accuracy: 0.9950\n",
      "Epoch 213/250\n",
      "200/200 [==============================] - 0s 411us/step - loss: 0.0561 - accuracy: 0.9800\n",
      "Epoch 214/250\n",
      "200/200 [==============================] - 0s 396us/step - loss: 0.0300 - accuracy: 0.9950\n",
      "Epoch 215/250\n",
      "200/200 [==============================] - 0s 408us/step - loss: 0.0293 - accuracy: 0.9950\n",
      "Epoch 216/250\n",
      "200/200 [==============================] - 0s 420us/step - loss: 0.0290 - accuracy: 0.9950\n",
      "Epoch 217/250\n",
      "200/200 [==============================] - 0s 405us/step - loss: 0.0289 - accuracy: 0.9950\n",
      "Epoch 218/250\n",
      "200/200 [==============================] - 0s 393us/step - loss: 0.0296 - accuracy: 0.9950\n",
      "Epoch 219/250\n",
      "200/200 [==============================] - 0s 388us/step - loss: 0.0278 - accuracy: 0.9950\n",
      "Epoch 220/250\n",
      "200/200 [==============================] - 0s 377us/step - loss: 0.0306 - accuracy: 0.9950\n",
      "Epoch 221/250\n",
      "200/200 [==============================] - 0s 371us/step - loss: 0.0283 - accuracy: 0.9950\n",
      "Epoch 222/250\n",
      "200/200 [==============================] - 0s 353us/step - loss: 0.0266 - accuracy: 0.9950\n",
      "Epoch 223/250\n",
      "200/200 [==============================] - 0s 378us/step - loss: 0.0596 - accuracy: 0.9850\n",
      "Epoch 224/250\n",
      "200/200 [==============================] - 0s 362us/step - loss: 0.0801 - accuracy: 0.9850\n",
      "Epoch 225/250\n",
      "200/200 [==============================] - 0s 329us/step - loss: 0.0276 - accuracy: 0.9950\n",
      "Epoch 226/250\n",
      "200/200 [==============================] - 0s 322us/step - loss: 0.0270 - accuracy: 0.9950\n",
      "Epoch 227/250\n",
      "200/200 [==============================] - 0s 305us/step - loss: 0.0262 - accuracy: 0.9950\n",
      "Epoch 228/250\n",
      "200/200 [==============================] - 0s 369us/step - loss: 0.0259 - accuracy: 0.9950\n",
      "Epoch 229/250\n",
      "200/200 [==============================] - 0s 378us/step - loss: 0.0263 - accuracy: 0.9950\n",
      "Epoch 230/250\n",
      "200/200 [==============================] - 0s 386us/step - loss: 0.0256 - accuracy: 0.9950\n",
      "Epoch 231/250\n",
      "200/200 [==============================] - 0s 452us/step - loss: 0.0254 - accuracy: 0.9950\n",
      "Epoch 232/250\n",
      "200/200 [==============================] - 0s 416us/step - loss: 0.0269 - accuracy: 0.9950\n",
      "Epoch 233/250\n",
      "200/200 [==============================] - 0s 430us/step - loss: 0.0303 - accuracy: 0.9950\n",
      "Epoch 234/250\n",
      "200/200 [==============================] - 0s 430us/step - loss: 0.0245 - accuracy: 0.9950\n",
      "Epoch 235/250\n",
      "200/200 [==============================] - 0s 437us/step - loss: 0.0241 - accuracy: 0.9950\n",
      "Epoch 236/250\n",
      "200/200 [==============================] - 0s 464us/step - loss: 0.0259 - accuracy: 0.9950\n",
      "Epoch 237/250\n",
      "200/200 [==============================] - 0s 431us/step - loss: 0.0273 - accuracy: 0.9950\n",
      "Epoch 238/250\n",
      "200/200 [==============================] - 0s 440us/step - loss: 0.0245 - accuracy: 0.9950\n",
      "Epoch 239/250\n",
      "200/200 [==============================] - 0s 410us/step - loss: 0.0242 - accuracy: 0.9950\n",
      "Epoch 240/250\n",
      "200/200 [==============================] - 0s 441us/step - loss: 0.0235 - accuracy: 0.9950\n",
      "Epoch 241/250\n",
      "200/200 [==============================] - 0s 406us/step - loss: 0.0243 - accuracy: 0.9950\n",
      "Epoch 242/250\n",
      "200/200 [==============================] - 0s 335us/step - loss: 0.0223 - accuracy: 0.9950\n",
      "Epoch 243/250\n",
      "200/200 [==============================] - 0s 354us/step - loss: 0.0710 - accuracy: 0.9750\n",
      "Epoch 244/250\n",
      "200/200 [==============================] - 0s 340us/step - loss: 0.0320 - accuracy: 0.9950\n",
      "Epoch 245/250\n",
      "200/200 [==============================] - 0s 330us/step - loss: 0.0223 - accuracy: 0.9950\n",
      "Epoch 246/250\n",
      "200/200 [==============================] - 0s 355us/step - loss: 0.0221 - accuracy: 0.9950\n",
      "Epoch 247/250\n",
      "200/200 [==============================] - 0s 343us/step - loss: 0.0217 - accuracy: 0.9950\n",
      "Epoch 248/250\n",
      "200/200 [==============================] - 0s 366us/step - loss: 0.0213 - accuracy: 0.9950\n",
      "Epoch 249/250\n",
      "200/200 [==============================] - 0s 357us/step - loss: 0.0210 - accuracy: 0.9950\n",
      "Epoch 250/250\n",
      "200/200 [==============================] - 0s 350us/step - loss: 0.0222 - accuracy: 0.9950\n"
     ]
    }
   ],
   "source": [
    "# now we fit to our training data, like before\n",
    "my_RNN_classifier.fit(training_data, training_decisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p7kc5I2xB5ya"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on the validation data\n",
    "\n",
    "predictions = my_RNN_classifier.predict(validation_data)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l9ihnQ0zHS6Z"
   },
   "source": [
    "##### Excercise 4.0\n",
    "\n",
    "Get the percentage of the predictions that were made correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vZyVFSJDF6hD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(validation_decisions == predictions)/ len(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MUU4_IKwGh4n"
   },
   "source": [
    "This is not as quite as good as logistic regression yet. There are a lot of parameters to choose for RNNs, and we haven't tried that yet.\n",
    "\n",
    "\n",
    "#### Looking inside the model\n",
    "\n",
    "Within the `my_RNN_classifier` object you just fit, there is an actual Keras model. We can look at it, change its parameters, and use Keras methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cEu_9fToHM_g"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x7fe55d194780>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put the cursor after model, and press tab to see the methods we can call\n",
    "my_RNN_classifier.model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J9Z-T3diH9ze"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 94.40000057220459 %\n"
     ]
    }
   ],
   "source": [
    "# for example, we can use Keras's in-built method for calculating accuracy\n",
    "from keras.utils import np_utils\n",
    "acc = my_RNN_classifier.model.test_on_batch(validation_data, \n",
    "                                      np_utils.to_categorical(validation_decisions))\n",
    "print(\"Accuracy is {} %\".format(100*acc[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IPVi_UFOLOM6"
   },
   "source": [
    "#### Excercise 4.1: Tuning the RNN\n",
    "\n",
    "Train an RNN decoder again, but this time use 1000 hidden units instead of 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i3IIljjbLfRD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 99.59999918937683 %\n"
     ]
    }
   ],
   "source": [
    "# first we instantiate the decoder\n",
    "my_RNN_classifier = decoders.SimpleRNNClassification(units =1000,\n",
    "                                                      dropout = 0,\n",
    "                                                      num_epochs = 250,\n",
    "                                                      verbose = 0)\n",
    "# now we fit to our training data, like before\n",
    "my_RNN_classifier.fit(training_data, training_decisions)\n",
    "acc = my_RNN_classifier.model.test_on_batch(validation_data, \n",
    "                                      np_utils.to_categorical(validation_decisions))\n",
    "print(\"Accuracy is {} %\".format(100*acc[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aQSc7xswSqTZ"
   },
   "source": [
    "## 5. Compare many methods on actual neural data\n",
    "\n",
    "### Preliminaries\n",
    "\n",
    "**STOP** Make sure you're running a **GPU runtime** for this section. (Runtime -> Change runtime type). Otherwise running the RNNs will take forever.\n",
    "\n",
    "First we need to download the data. I created a script to download and process the Steinmetz data into the format we need. Since the processing script takes about 15 minutes, we'll skip that step, and you can just download the processed data from my Google Drive.\n",
    "\n",
    "This dataset is specifically `Cori_2016-12-18`. If you're interested in loading a different session, I've copied my downloading and preprocessing script in the bottom of this notebook and you can run it right here."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u0Wt68jUSqTZ"
   },
   "source": [
    "!pip install googledrivedownloader\n",
    "\n",
    "from google_drive_downloader import GoogleDriveDownloader as gdd\n",
    "\n",
    "gdd.download_file_from_google_drive(file_id='1W3TwEtC0Z6Qmbfuz8_AWRiQHfuDb9FIS',\n",
    "                                    dest_path='./Binned_data.zip',\n",
    "                                    unzip=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sARtnvbYSqTe"
   },
   "outputs": [],
   "source": [
    "binned_spikes = np.load('binned_spikes.npy')\n",
    "choices = np.load('choices.npy')+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228, 1089, 50)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binned_spikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(choices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binned_spikes[0,0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe3f85e71d0>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot some examples\n",
    "figure = plt.figure(figsize=(9,2))\n",
    "ax = figure.add_subplot(111)\n",
    "ax.scatter(np.arange(binned_spikes.shape[2]),np.mean(np.mean(binned_spikes[choices==2,:,:],axis=0),axis=0),marker='|')\n",
    "ax.axhline(y=0.075,c='k',alpha=.3)\n",
    "ax.scatter(np.arange(binned_spikes.shape[2]),np.mean(np.mean(binned_spikes[choices==1,:,:],axis=0),axis=0),marker='|')\n",
    "ax.scatter(np.arange(binned_spikes.shape[2]),np.mean(np.mean(binned_spikes[choices==0,:,:],axis=0),axis=0),marker='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6LQ2cxR-SqTi"
   },
   "source": [
    "### Predicting choices from neural population recordings\n",
    "\n",
    "We're going to try to predict the mouse's choices from the neuropixel recordings in the 1 second preceeding each choice. `binned_spikes.npy` is a numpy array containing the binned spike rates, and `choices.npy` contains the animal's choices. \n",
    "\n",
    "There are 228 trials, 1089 neurons, and 50 bins per trial (for a time of 20ms per bin).\n",
    "\n",
    "This is not a great situation for machine learning. As a rule of thumb, we would like more trials than datapoints per trial. (If doing this for realz, would suggest running PCA on this data and using the top components as inputs). But, it's what we got, so here goes!\n",
    "\n",
    "#### Exercise 5.0\n",
    "\n",
    "Print out the shape of `binned_spikes` and `choices`. Also print out the first 10 choices.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "korkcjUISqTi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(228, 1089, 50) (228,)\n",
      "[2. 2. 0. 0. 0. 1. 0. 0. 0. 2.]\n"
     ]
    }
   ],
   "source": [
    "print(binned_spikes.shape, choices.shape)\n",
    "print(choices[:10])\n",
    "\n",
    "# Need to rearrange array since format is wrong \n",
    "binned_spikes = np.moveaxis(binned_spikes, 1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LNDeXvE_cU94"
   },
   "source": [
    "You'll notice that `choices` takes values 0, 1, and 2. We're going to try to decode this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VqEcdu4tX6oF"
   },
   "source": [
    "#### Excercise 5.1\n",
    "\n",
    "Split this data into test and validation splits.\n",
    "\n",
    "(We'll skip the final testing phase in this exercise. In reality, make sure to do cross-validation and use both validation and testing data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RuovaxSOX7Y8"
   },
   "outputs": [],
   "source": [
    "# make training and validation data\n",
    "# Use an 80/20 split.|\n",
    "split = int(binned_spikes.shape[0]*4/5)\n",
    "\n",
    "\n",
    "training_spikes = binned_spikes[:split,:,:]\n",
    "validation_spikes = binned_spikes[split:,:,:]\n",
    "\n",
    "training_choices = choices[:split]\n",
    "validation_choices = choices[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 50, 1089)"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_spikes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H1HVdWFEZrZl"
   },
   "source": [
    "##### A fitting demo.\n",
    "\n",
    "This is basically what we did above, but with actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LPjqE8_OSqTs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " validation accuracy: 67.3913043478261 %\n"
     ]
    }
   ],
   "source": [
    "my_RNN_classifier = decoders.SimpleRNNClassification(units = 500,\n",
    "                                                     dropout = 10,\n",
    "                                                     num_epochs = 50,\n",
    "                                                     verbose = 0)\n",
    "\n",
    "my_RNN_classifier.fit(training_spikes, training_choices)\n",
    "\n",
    "predictions = my_RNN_classifier.predict(validation_spikes)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bpzpvy6PaKCv"
   },
   "source": [
    "Note that the train accuracy reached about 100% in these 10 epochs, but the validation accuracy is quite low. Sounds like overfitting! We might want to try some regularization, like dropout."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xma2jUT5SqTx"
   },
   "source": [
    "### Excercise 5.1\n",
    "\n",
    "Fit each of the following decoders:\n",
    "\n",
    "1.   A Gated Recurrent Unit (`decoders.GRUClassification`)\n",
    "2.   An LSTM (`decoders.LSTMClassification`)\n",
    "3.   Gradient boosted trees (XGBoost) (`decoders.XGBoostClassification`)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GvvopGtKaw0k"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "182/182 [==============================] - 3s 18ms/step - loss: 1.1397 - accuracy: 0.4505\n",
      "Epoch 2/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.5376 - accuracy: 0.8187\n",
      "Epoch 3/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.2968 - accuracy: 0.8791\n",
      "Epoch 4/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.0586 - accuracy: 1.0000\n",
      "Epoch 5/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.0194 - accuracy: 1.0000\n",
      "Epoch 6/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.0062 - accuracy: 1.0000\n",
      "Epoch 7/10\n",
      "182/182 [==============================] - 3s 16ms/step - loss: 0.0028 - accuracy: 1.0000\n",
      "Epoch 8/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 6.9450e-04 - accuracy: 1.0000\n",
      "Epoch 10/10\n",
      "182/182 [==============================] - 3s 15ms/step - loss: 0.1953 - accuracy: 0.9670\n",
      "\n",
      " validation accuracy: 63.04347826086957 %\n"
     ]
    }
   ],
   "source": [
    "# GRU\n",
    "my_gru_classifier = decoders.GRUClassification(units = 500,\n",
    "                                                 dropout = 5,\n",
    "                                                 num_epochs = 10,\n",
    "                                                 verbose = 1)\n",
    "\n",
    "my_gru_classifier.fit(training_spikes, training_choices)\n",
    "\n",
    "predictions = my_gru_classifier.predict(validation_spikes)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kaW868Paaw5C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " validation accuracy: 82.6086956521739 %\n"
     ]
    }
   ],
   "source": [
    "#LSTM\n",
    "my_lstm_classifier = decoders.LSTMClassification(units = 500,\n",
    "                             dropout = 10,\n",
    "                             num_epochs = 50,\n",
    "                             verbose = 0)\n",
    "my_lstm_classifier.fit(training_spikes, training_choices)\n",
    "predictions = my_lstm_classifier.predict(validation_spikes)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jjoE3Qo3aw2n"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:00:13] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "\n",
      " validation accuracy: 56.52173913043478 %\n"
     ]
    }
   ],
   "source": [
    "# XGBoost\n",
    "\n",
    "# XGBoost is powerful but does not model time dependencies.\n",
    "# Like for the logistic regression above, we have to flatten\n",
    "# the inputs into shape (n_trials, n_neurons x n_time_bins)\n",
    "\n",
    "flat_train_data = np.reshape(training_spikes, (len(training_spikes),-1))\n",
    "flat_val_data = np.reshape(validation_spikes, (len(validation_spikes),-1))\n",
    "\n",
    "# now train it like above\n",
    "my_xgboost_classifier = decoders.XGBoostClassification(max_depth=3, num_round=300, eta=.3, gpu=-1)\n",
    "\n",
    "my_xgboost_classifier.fit(flat_train_data, training_choices)\n",
    "predictions = my_xgboost_classifier.predict(flat_val_data)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape flattened training: (182, 54450)\n",
      "Shape flattened validation: (46, 54450)\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape flattened training: {flat_train_data.shape}')\n",
    "print(f'Shape flattened validation: {flat_val_data.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mH2-UxhRLJEg"
   },
   "source": [
    "### Extra credit exercise 1: reduce the dimension of data with PCA\n",
    "\n",
    "Create an instance of PCA (loading the module from scikit-learn). Then, fit it on your training data. Then transform both the validation and train data. (Note that we don't fit the PCA using train data.)\n",
    "\n",
    "You can either reduce the dimension across neurons only, preserving temporal structure, or decide to reduce across all dimensions.\n",
    "\n",
    "Does validation accuracy improve?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 50, 1089)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_spikes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce/transform over neurons only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9100, 1089)"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_training_spikes = np.reshape(training_spikes, ( -1, training_spikes.shape[2]))\n",
    "reshaped_training_spikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02430849 0.04567579 0.06079312 0.07387148 0.08429196 0.09400826\n",
      " 0.10350246 0.11224482 0.12092523 0.12921989]\n"
     ]
    }
   ],
   "source": [
    "# Fit PCA\n",
    "n_components_pca = 10\n",
    "pca = PCA(n_components=n_components_pca, whiten=True)\n",
    "matrix_pca = pca.fit(reshaped_training_spikes)\n",
    "print(np.cumsum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9100, 10)"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_training = pca.transform(reshaped_training_spikes)\n",
    "transformed_training.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2300, 1089)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2300, 10)"
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform validation set \n",
    "reshaped_validation_spikes = np.reshape(validation_spikes,( -1, validation_spikes.shape[2]))\n",
    "print(reshaped_validation_spikes.shape)\n",
    "reshaped_validation = pca.transform(reshaped_validation_spikes)\n",
    "reshaped_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape back, such that you get the original number of trials/ timepoints ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape PCA reduced training: (182, 50, 10)\n",
      "Shape PCA reduced validation: (46, 50, 10)\n"
     ]
    }
   ],
   "source": [
    "transformed_spikes_training   = np.reshape(transformed_training,(training_spikes.shape[0],training_spikes.shape[1],-1))\n",
    "transformed_spikes_validation = np.reshape(reshaped_validation ,(validation_spikes.shape[0],validation_spikes.shape[1],-1))\n",
    "print(f'Shape PCA reduced training: {transformed_spikes_training.shape}')\n",
    "print(f'Shape PCA reduced validation: {transformed_spikes_validation.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ML models: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " validation accuracy: 67.3913043478261 %\n"
     ]
    }
   ],
   "source": [
    "my_RNN_classifier = decoders.SimpleRNNClassification(units = 500,\n",
    "                                                     dropout = 10,\n",
    "                                                     num_epochs = 50,\n",
    "                                                     verbose = 0)\n",
    "\n",
    "my_RNN_classifier.fit(transformed_spikes_training, training_choices)\n",
    "\n",
    "predictions = my_RNN_classifier.predict(transformed_spikes_validation)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " validation accuracy: 69.56521739130434 %\n"
     ]
    }
   ],
   "source": [
    "#LSTM\n",
    "my_lstm_classifier = decoders.LSTMClassification(units = 500,\n",
    "                                                     dropout = 10,\n",
    "                                                     num_epochs = 50,\n",
    "                                                     verbose = 0)\n",
    "my_lstm_classifier.fit(transformed_spikes_training, training_choices)\n",
    "predictions = my_lstm_classifier.predict(transformed_spikes_validation)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce / transform over all dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 54450)"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_training_spikes = np.reshape(training_spikes, (training_spikes.shape[0],-1))\n",
    "reshaped_training_spikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components_pca = 30\n",
    "pca = PCA(n_components=n_components_pca, whiten=True)\n",
    "matrix_pca = pca.fit(reshaped_training_spikes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01703977 0.02969729 0.04092998 0.05112367 0.06000028 0.06805362\n",
      " 0.07601335 0.08352066 0.09101455 0.09822035 0.1052543  0.11213682\n",
      " 0.11885986 0.12547493 0.13200415 0.1384892  0.14489236 0.15127304\n",
      " 0.15759912 0.16377982 0.16992756 0.17603187 0.182119   0.18818048\n",
      " 0.1941813  0.20013499 0.2060264  0.21188697 0.217733   0.22347749]\n"
     ]
    }
   ],
   "source": [
    "print(np.cumsum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 30)"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_training = pca.transform(reshaped_training_spikes)\n",
    "transformed_training.shape\n",
    "# No need to reshape back! This has now been reduced to 2D over both neurons and timepoints for each neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46, 54450)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(46, 30)"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform validation set \n",
    "reshaped_validation_spikes = np.reshape(validation_spikes,(validation_spikes.shape[0],-1))\n",
    "print(reshaped_validation_spikes.shape)\n",
    "reshaped_validation = pca.transform(reshaped_validation_spikes)\n",
    "reshaped_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape PCA reduced training: (182, 30)\n",
      "Shape PCA reduced validation: (46, 30)\n"
     ]
    }
   ],
   "source": [
    "transformed_spikes_training   = np.reshape(transformed_training,(training_spikes.shape[0],-1))\n",
    "transformed_spikes_validation = np.reshape(reshaped_validation ,(validation_spikes.shape[0],-1))\n",
    "print(f'Shape PCA reduced training: {transformed_spikes_training.shape}')\n",
    "print(f'Shape PCA reduced validation: {transformed_spikes_validation.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:04:56] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "\n",
      " validation accuracy: 45.65217391304348 %\n"
     ]
    }
   ],
   "source": [
    "# XGBoost\n",
    "\n",
    "# XGBoost is powerful but does not model time dependencies.\n",
    "# Like for the logistic regression above, we have to flatten\n",
    "# the inputs into shape (n_trials, n_neurons x n_time_bins)\n",
    "\n",
    "flat_train_data = transformed_spikes_training\n",
    "flat_val_data = transformed_spikes_validation\n",
    "\n",
    "# now train it like above\n",
    "my_xgboost_classifier = decoders.XGBoostClassification(max_depth=10, num_round=500, eta=.3, gpu=-1)\n",
    "\n",
    "my_xgboost_classifier.fit(flat_train_data, training_choices)\n",
    "predictions = my_xgboost_classifier.predict(flat_val_data)\n",
    "accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8dl5XbyPLdBp"
   },
   "source": [
    "### Extra credit exercise 2: Set up a script to perform k-fold CV\n",
    "\n",
    "Using your favorite methods (e.g. scikit learn) or the script we wrote above, create a method to perform 5-fold cross-validation on the Neuropixels data. The function should take parameters to create a model as input, and it should output the average validation accuracy across the 5 validation sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "#binned_spikes\n",
    "#choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11400, 1089)"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_spikes = np.reshape(binned_spikes, (-1, binned_spikes.shape[2]))\n",
    "reshaped_spikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228, 50, 1089)"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binned_spikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit PCA\n",
    "#n_components_pca = 10\n",
    "#pca = PCA(n_components = n_components_pca, whiten = True)\n",
    "#matrix_pca = pca.fit(reshaped_training_spikes)\n",
    "#print(np.cumsum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformed_training = pca.transform(reshaped_training_spikes)\n",
    "#transformed_training.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform validation set \n",
    "#reshaped_validation_spikes = np.reshape(validation_spikes,( -1, validation_spikes.shape[2]))\n",
    "#print(reshaped_validation_spikes.shape)\n",
    "#reshaped_validation = pca.transform(reshaped_validation_spikes)\n",
    "#reshaped_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformed_spikes_training   = np.reshape(transformed_training,(training_spikes.shape[0],training_spikes.shape[1],-1))\n",
    "#transformed_spikes_validation = np.reshape(reshaped_validation ,(validation_spikes.shape[0],validation_spikes.shape[1],-1))\n",
    "#print(f'Shape PCA reduced training: {transformed_spikes_training.shape}')\n",
    "#print(f'Shape PCA reduced validation: {transformed_spikes_validation.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_RNN_classifier = decoders.SimpleRNNClassification(units = 500,\n",
    "                                                     dropout = 10,\n",
    "                                                     num_epochs = 20,\n",
    "                                                     verbose = 0)\n",
    "\n",
    "#my_RNN_classifier.fit(transformed_spikes_training, training_choices)\n",
    "\n",
    "#predictions = my_RNN_classifier.predict(transformed_spikes_validation)\n",
    "#accuracy = np.sum(predictions == validation_choices) / float(len(predictions))\n",
    "\n",
    "#print(\"\\n validation accuracy: {} %\".format(100*accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scorer(estimator,X,y):\n",
    "    predictions = estimator.predict(X)\n",
    "    accuracy = np.sum(predictions == y) / float(len(predictions))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cv_indices(cv, X, ax, n_splits, lw=30):\n",
    "    \"\"\"Create a sample plot for indices of a cross-validation object.\"\"\"\n",
    "\n",
    "    # Generate the training/testing visualizations for each CV split\n",
    "    for ii, (tr, tt) in enumerate(cv.split(X=X)):\n",
    "        # Fill in indices with the training/test groups\n",
    "        indices = np.array([np.nan] * len(X))\n",
    "        indices[tt] = 1\n",
    "        indices[tr] = 0\n",
    "\n",
    "        # Visualize the results\n",
    "        ax.scatter(range(len(indices)), [ii + .5] * len(indices),\n",
    "                   c=indices, marker='_', lw=lw, cmap='Set1',\n",
    "                   vmin=-.2, vmax=1.2)\n",
    "        \n",
    "    # Formatting\n",
    "    yticklabels = list(range(n_splits)) \n",
    "    ax.set(yticks=np.arange(n_splits+2) + .5, yticklabels=yticklabels,\n",
    "           xlabel='Sample index', ylabel=\"CV iteration\",\n",
    "           ylim=[n_splits+.2, -.2])\n",
    "    ax.set_title('{}'.format(type(cv).__name__), fontsize=15)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crossvalidated(estimator, data, choices, n_splits=2, verbose=True):\n",
    "    ''' Perform Kfold crossvalidation and return accuracy'''\n",
    "    kf = KFold(n_splits=n_splits)\n",
    "    \n",
    "    all_accuracies = []\n",
    "    for train, test in kf.split(data): \n",
    "        \n",
    "        training_spikes =   data[train,:,:]\n",
    "        validation_spikes = data[test,:,:]\n",
    "        training_choices =  choices[train]\n",
    "        validation_choices= choices[test]\n",
    "        \n",
    "        reshaped_training_spikes = np.reshape(training_spikes, ( -1, training_spikes.shape[2]))\n",
    "        \n",
    "        # Fit PCA\n",
    "        n_components_pca = 10\n",
    "        pca = PCA(n_components=n_components_pca, whiten=True)\n",
    "        matrix_pca = pca.fit(reshaped_training_spikes)\n",
    "        \n",
    "        transformed_training = pca.transform(reshaped_training_spikes)\n",
    "    \n",
    "        # Transform validation set \n",
    "        reshaped_validation_spikes = np.reshape(validation_spikes,( -1, validation_spikes.shape[2]))\n",
    "        transformed_validation = pca.transform(reshaped_validation_spikes)\n",
    "    \n",
    "    \n",
    "        transformed_spikes_training   = np.reshape(transformed_training, (training_spikes.shape[0],training_spikes.shape[1],-1))\n",
    "        transformed_spikes_validation = np.reshape(transformed_validation, (validation_spikes.shape[0],validation_spikes.shape[1],-1))\n",
    "        if verbose:\n",
    "            print(f'Shape PCA reduced training: {transformed_spikes_training.shape}')\n",
    "            print(f'Shape PCA reduced validation: {transformed_spikes_validation.shape}')\n",
    "        \n",
    "        \n",
    "        RNN_classifier = decoders.SimpleRNNClassification(units = 500,\n",
    "                                                           dropout = 10,\n",
    "                                                            num_epochs = 50,\n",
    "                                                             verbose = 0)\n",
    "        RNN_classifier.fit(transformed_spikes_training, training_choices)\n",
    "\n",
    "        accuracy = scorer(RNN_classifier,transformed_spikes_validation,validation_choices)\n",
    "        if verbose: print(\"\\n validation accuracy: {} %\".format(100*accuracy))\n",
    "        all_accuracies.append(accuracy)\n",
    "    return all_accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape PCA reduced training: (199, 50, 10)\n",
      "Shape PCA reduced validation: (29, 50, 10)\n",
      "\n",
      " validation accuracy: 72.41379310344827 %\n",
      "Shape PCA reduced training: (199, 50, 10)\n",
      "Shape PCA reduced validation: (29, 50, 10)\n",
      "\n",
      " validation accuracy: 82.75862068965517 %\n",
      "Shape PCA reduced training: (199, 50, 10)\n",
      "Shape PCA reduced validation: (29, 50, 10)\n",
      "\n",
      " validation accuracy: 79.3103448275862 %\n",
      "Shape PCA reduced training: (199, 50, 10)\n",
      "Shape PCA reduced validation: (29, 50, 10)\n",
      "\n",
      " validation accuracy: 62.06896551724138 %\n",
      "Shape PCA reduced training: (200, 50, 10)\n",
      "Shape PCA reduced validation: (28, 50, 10)\n",
      "\n",
      " validation accuracy: 89.28571428571429 %\n",
      "Shape PCA reduced training: (200, 50, 10)\n",
      "Shape PCA reduced validation: (28, 50, 10)\n",
      "\n",
      " validation accuracy: 64.28571428571429 %\n",
      "Shape PCA reduced training: (200, 50, 10)\n",
      "Shape PCA reduced validation: (28, 50, 10)\n",
      "\n",
      " validation accuracy: 78.57142857142857 %\n",
      "Shape PCA reduced training: (200, 50, 10)\n",
      "Shape PCA reduced validation: (28, 50, 10)\n",
      "\n",
      " validation accuracy: 53.57142857142857 %\n",
      "[0.7241379310344828, 0.8275862068965517, 0.7931034482758621, 0.6206896551724138, 0.8928571428571429, 0.6428571428571429, 0.7857142857142857, 0.5357142857142857]\n"
     ]
    }
   ],
   "source": [
    "all_accuracies = crossvalidated('estimator', binned_spikes, choices, n_splits=8)\n",
    "print(all_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe406e44da0>"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_splits = 8\n",
    "fig, ax = plt.subplots()\n",
    "cv = KFold(n_splits)\n",
    "plot_cv_indices(cv, binned_spikes, ax, n_splits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rEpxVLe431kH"
   },
   "source": [
    "### Extra credit exercise 3: find the best decoder\n",
    "\n",
    "Search across methods and hyperparameters for the best decoder. Try hyperopt, or random searches.\n",
    "\n",
    "If you're fancy and have lots of compute time, maybe try automated ML. (e.g. autosklearn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_parameters = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Neural decoding.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}