From 50df274c410e8fb9612d1ab83cca0a811b2ae3a4 Mon Sep 17 00:00:00 2001
From: drewoldag <47493171+drewoldag@users.noreply.github.com>
Date: Tue, 1 Oct 2024 16:05:19 -0700
Subject: [PATCH] Initial attempt at implementing resnet50 for use with CIFAR
 data.

---
 .gitignore                         |    5 +-
 .pre-commit-config.yaml            |    1 +
 docs/pre_executed/CNN_filter.ipynb | 1701 ++++++++++++++++++++++++++++
 example_config.toml                |   53 +-
 src/kbmod_ml/models/resnet50.py    |   65 ++
 5 files changed, 1811 insertions(+), 14 deletions(-)
 create mode 100644 docs/pre_executed/CNN_filter.ipynb
 create mode 100644 src/kbmod_ml/models/resnet50.py

diff --git a/.gitignore b/.gitignore
index 3b3bf2f..095ae72 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,4 +150,7 @@ _html/
 .initialize_new_project.sh
 
 # Model files
-**/*.pth
\ No newline at end of file
+**/*.pth
+
+# Run results
+results/
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b8a8974..f5a9a6c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -16,6 +16,7 @@ repos:
         name: Clear output from Jupyter notebooks
         description: Clear output from Jupyter notebooks.
         files: \.ipynb$
+        exclude: ^docs/pre_executed
         stages: [commit]
         language: system
         entry: jupyter nbconvert --clear-output
diff --git a/docs/pre_executed/CNN_filter.ipynb b/docs/pre_executed/CNN_filter.ipynb
new file mode 100644
index 0000000..a3273ff
--- /dev/null
+++ b/docs/pre_executed/CNN_filter.ipynb
@@ -0,0 +1,1701 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.ensemble import RandomForestClassifier as rfc\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "import os\n",
+    "from astropy.io import fits\n",
+    "import matplotlib.dates as mdates\n",
+    "import datetime as dt\n",
+    "from dateutil.parser import parse\n",
+    "import multiprocessing as mp\n",
+    "import pickle\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Conv2D, Flatten\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "\n",
+    "from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
+    "from tensorflow.keras.initializers import glorot_uniform\n",
+    "from tensorflow.keras.models import Model, load_model\n",
+    "import tensorflow.keras.backend as K\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.2.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate the training data for the CNN for KBMOD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make training set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### False Dataset\n",
+    "\n",
+    "Our false data is a set of postage stamps actually collected from KBMOD that were false positives coming through the pipeline. These were obtained by searching off-ecliptic trajectories in some Pointing Groups from 010 to 146 in the Lori Allen NEO dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(113549, 441)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_false = np.load('../data_files/aligned_false_positive_coadds.npy')\n",
+    "real_objects = np.load('../data_files/normed_individual_real.npy')\n",
+    "real_false_positives = np.load('../data_files/normed_individual_real_false_positives.npy')\n",
+    "\n",
+    "np.shape(full_false)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make real dataset\n",
+    "\n",
+    "Make some real Gaussians and add noise to use as truth for training. Add them to a background from real images and searched along a trajectory. Add some scatter in the alignment and some variance to the PSF size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from astropy.io import fits\n",
+    "import matplotlib.dates as mdates\n",
+    "import datetime as dt\n",
+    "from dateutil.parser import parse\n",
+    "import multiprocessing as mp\n",
+    "import pickle\n",
+    "\n",
+    "with open('../data_files/PickledPointings.pkl', 'rb') as f:\n",
+    "    Pointing_Groups = pickle.load(f)\n",
+    "\n",
+    "# Import the times\n",
+    "times=[[]]*len(Pointing_Groups)\n",
+    "for i,pointing in enumerate(Pointing_Groups):\n",
+    "    times[i]=np.array([0.]*len(Pointing_Groups[i]['date_obs']))\n",
+    "    for j in range(len(Pointing_Groups[i]['date_obs'])):\n",
+    "        foo = parse(Pointing_Groups[i]['date_obs'][j])\n",
+    "        times[i][j]= foo.timestamp()\n",
+    "        \n",
+    "def makeSyntheticCoadd(pgccd, doMedian=True, applyMask=False):\n",
+    "    \"\"\"\n",
+    "    This function generates a synthetic coadd for a given pointing group and ccd.\n",
+    "    It generates a random trajectory along the visits and uses those to get 21x21\n",
+    "    pixel stamps. It puts a gaussian with a semi-random PSF in each stamp, with\n",
+    "    some scatter for the central pixel. It adds a linear offset to the stamps.\n",
+    "    It then returns the median of all the stamps.\n",
+    "    \"\"\"\n",
+    "    pg_num = pgccd[0]\n",
+    "    ccd = pgccd[1]\n",
+    "    visit_list = Pointing_Groups[pg_num]['visit_id']\n",
+    "    cutout_size = 21 # pixels\n",
+    "    speed_lims = [100,300] # px/day\n",
+    "    ang_lims = [0,np.pi/2] # radians\n",
+    "    time_baseline = (times[pg_num][-1]-times[pg_num][0])/(3600*24) # days\n",
+    "    \n",
+    "    visit_num = len(visit_list)\n",
+    "    \n",
+    "    if ccd==2 or ccd==61:\n",
+    "        return([])\n",
+    "    all_data = []\n",
+    "    median_coadd = []\n",
+    "    good_visit_idx = []\n",
+    "    for i,visit_id in enumerate(visit_list):\n",
+    "        #imagePath = '/epyc/projects2/smotherh/DECAM_Data_Reduction/pointing_groups_hyak/Pointing_Group_{:03d}'\n",
+    "        imagePath = '/epyc/users/smotherh/pointing_groups/Pointing_Group_{:03d}'\n",
+    "        diffPath = os.path.join(imagePath.format(pg_num),'warps/{:02d}/{}.fits'.format(int(ccd),visit_id))\n",
+    "        try:\n",
+    "            hdul = fits.open(diffPath)\n",
+    "            data = hdul[1].data\n",
+    "            if applyMask:\n",
+    "                mask = hdul[2].data\n",
+    "                # Bits 0, 1, and 3 correspond to bad pixels, saturated pixels, and cosmic rays in DECam data\n",
+    "                bad_pixel = np.bitwise_and(np.array(mask).astype(int),(1<<0|1<<1|1<<3))\n",
+    "                data[np.where(bad_pixel)] = 0\n",
+    "            data[np.isnan(data)] = 0\n",
+    "            all_data.append(data)\n",
+    "            good_visit_idx.append(i)\n",
+    "        except:\n",
+    "            continue\n",
+    "    if len(all_data) > 0:\n",
+    "        data_size = np.shape(all_data[0])\n",
+    "    else:\n",
+    "        return(median_coadd)\n",
+    "    for j in range(50):\n",
+    "        current_night_time = -1e9\n",
+    "        # Set a 2 pixel potential velocity offset\n",
+    "        max_x_vel_offset = 4*(0.5-np.random.random())\n",
+    "        max_y_vel_offset = 4*(0.5-np.random.random())\n",
+    "        average_x_offset = 2*(1-2*np.random.random())\n",
+    "        average_y_offset = 2*(1-2*np.random.random())\n",
+    "        pg_object_list = []\n",
+    "        speed = (speed_lims[1]-speed_lims[0]) * np.random.random() + speed_lims[0]\n",
+    "        angle = (ang_lims[1]-ang_lims[0]) * np.random.random() + ang_lims[0]\n",
+    "        vel = [speed*np.cos(angle), speed*np.sin(angle)]\n",
+    "        starting_pixel_xy_max = [data_size[1] - 100 - speed*time_baseline, data_size[0] - 100 - speed*time_baseline]\n",
+    "        start_px = [50+starting_pixel_xy_max[0]*np.random.random(), 50+starting_pixel_xy_max[1]*np.random.random()]\n",
+    "        min_gauss_sigma = 1\n",
+    "        brightness_lims = [100,500]\n",
+    "        #object_brightness = (brightness_lims[1]-brightness_lims[0])*np.random.random() + brightness_lims[0]\n",
+    "        object_brightness = np.random.exponential(scale=brightness_lims[1])+brightness_lims[0]\n",
+    "        for idx,visit_id in enumerate(visit_list[good_visit_idx]):\n",
+    "            i = good_visit_idx[idx]\n",
+    "            if np.abs(times[pg_num][i] - current_night_time)>(3600*12):\n",
+    "                current_night_time = times[pg_num][i]\n",
+    "                avg_night_sigma = min_gauss_sigma+np.random.random()\n",
+    "            net_x_offset = 2*(1-2*np.random.random())+average_x_offset\n",
+    "            net_y_offset = 2*(1-2*np.random.random())+average_y_offset\n",
+    "            # Compute the values of various parameters for this specific stamp.\n",
+    "            # Uses the limits defined above.\n",
+    "            data = np.copy(all_data[idx])\n",
+    "            if len(data)<1:\n",
+    "                continue\n",
+    "            elapsed_time = (times[pg_num][i] - times[pg_num][0])/(3600*24)\n",
+    "            pixel = [start_px[0]+vel[0]*elapsed_time, start_px[1]+vel[1]*elapsed_time]\n",
+    "            min_x = int(pixel[0]-(cutout_size-1)/2)-1\n",
+    "            max_x = int(pixel[0]+(cutout_size-1)/2)\n",
+    "            min_y = int(pixel[1]-(cutout_size-1)/2)-1\n",
+    "            max_y = int(pixel[1]+(cutout_size-1)/2)\n",
+    "            cutout = np.copy(data[min_y:max_y,min_x:max_x])\n",
+    "            cutout[np.isnan(cutout)] = 0\n",
+    "            #cutout -= np.min(cutout)\n",
+    "            #cutout /= np.sum(cutout)\n",
+    "            cutout_edge = (cutout_size-1)/2                                                   \n",
+    "            x = np.linspace(-cutout_edge, cutout_edge, cutout_size)\n",
+    "            y = np.linspace(-cutout_edge, cutout_edge, cutout_size)\n",
+    "            x, y = np.meshgrid(x, y)\n",
+    "            sigma = 0.1*np.random.random()+avg_night_sigma\n",
+    "            gaussian_kernel = (object_brightness * (1/(2*np.pi*sigma*sigma) \n",
+    "                * np.exp(-((x-net_x_offset-max_x_vel_offset*(i/visit_num))**2/(2*sigma**2) + (y-net_y_offset-max_y_vel_offset*(i/visit_num))**2/(2*sigma**2))))\n",
+    "                * (1+(0.1*(np.random.random(cutout_size*cutout_size).reshape(cutout_size,cutout_size)-0.5)))) # add 3% noise to psf)\n",
+    "            # Add the synthetic object to the background except where pixel\n",
+    "            # values are exactly zero (i.e. have been masked out)\n",
+    "            sim_object = np.copy(cutout)\n",
+    "            sim_object[sim_object!=0] += gaussian_kernel[sim_object!=0]\n",
+    "            pg_object_list.append(sim_object)\n",
+    "        if len(pg_object_list)>0:\n",
+    "            if doMedian:\n",
+    "                median_coadd.append(np.median(pg_object_list, axis=0))\n",
+    "            else:\n",
+    "                median_coadd.append(pg_object_list)\n",
+    "        else:\n",
+    "            continue\n",
+    "    return(median_coadd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test the coadd generation\n",
+    "coadds = makeSyntheticCoadd([23,56],doMedian=True,applyMask=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x2304 with 50 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the test images\n",
+    "fig, ax = plt.subplots(nrows=10,ncols=5,figsize=[16,32])\n",
+    "ax = ax.reshape(-1)\n",
+    "for i,coadd in enumerate(coadds):\n",
+    "    ax[i].imshow(coadd)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate the full list of simulated stamps using\n",
+    "# the backgrounds from images not in the Search Sample\n",
+    "\n",
+    "known_object_pgccd_list = np.loadtxt('/epyc/users/smotherh/pointing_groups/known_object_list.txt').astype(int)\n",
+    "\n",
+    "foo = np.linspace(1,62,62).astype(int)\n",
+    "foo = foo[foo!=2]\n",
+    "ccd_list = foo[foo!=61]\n",
+    "pgccd_list = []\n",
+    "for pgccd in known_object_pgccd_list[0:15]:\n",
+    "    for ccd in ccd_list:\n",
+    "        pgccd_list.append([pgccd[0],ccd])\n",
+    "with mp.Pool(10) as pool:\n",
+    "    results = pool.map(makeSyntheticCoadd, pgccd_list)\n",
+    "\n",
+    "# Save the stamps\n",
+    "true_coadds = [result for result in results if (len(result)>0)]\n",
+    "true_coadds = np.concatenate(true_coadds,axis=0)\n",
+    "#np.save('simulated_true_coadds_2.npy', true_coadds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalize then save the false positive and simulated true stamps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/astro/users/smotherh/anaconda3/envs/python36/lib/python3.6/site-packages/ipykernel/__main__.py:19: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "full_false = np.load('../data_files/aligned_false_positive_coadds.npy')\n",
+    "simulated_positives = np.load('../data_files/simulated_true_coadds_2.npy').astype(float)\n",
+    "\n",
+    "#fake_positives = []\n",
+    "#false_positives = []\n",
+    "def norm_stamps(stamp_list):\n",
+    "    normed_stamps = []\n",
+    "    sigmaG_coeff =  0.7413\n",
+    "    for stamp in stamp_list:\n",
+    "        stamp = np.copy(stamp)\n",
+    "        stamp[np.isnan(stamp)] = 0\n",
+    "        per25,per50,per75 = np.percentile(stamp,[25,50,75])\n",
+    "        sigmaG = sigmaG_coeff * (per75 - per25)\n",
+    "        stamp[stamp<(per50-2*sigmaG)] = per50-2*sigmaG\n",
+    "        stamp -= np.min(stamp)\n",
+    "        stamp /= np.sum(stamp)\n",
+    "        stamp[np.isnan(stamp)] = 0\n",
+    "        normed_stamps.append(stamp.reshape(21,21))\n",
+    "    normed_stamps = np.array(normed_stamps)\n",
+    "    return(normed_stamps)\n",
+    "\n",
+    "false_positives = norm_stamps(full_false)\n",
+    "fake_positives = norm_stamps(simulated_positives)\n",
+    "#real_positives = norm_stamps(real_objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the normed stamps\n",
+    "#np.save('normed_individual_simulated_2.npy',fake_positives)\n",
+    "#np.save('normed_individual_false.npy',false_positives)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the CNN models and helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_true_false(true, false):\n",
+    "    fig_0,ax_0 = plt.subplots(nrows=2,ncols=5,figsize=[8,4])\n",
+    "    fig_1,ax_1 = plt.subplots(nrows=2,ncols=5,figsize=[8,4])\n",
+    "    ax_0 = ax_0.reshape(-1)\n",
+    "    ax_1 = ax_1.reshape(-1)\n",
+    "    true_int_list = np.random.choice(len(true),10,replace=False)\n",
+    "    false_int_list = np.random.choice(len(false),10,replace=False)\n",
+    "    for i, ax in enumerate(ax_0):\n",
+    "        ax.imshow(true[true_int_list[i]].reshape(21,21))\n",
+    "    for i, ax in enumerate(ax_1):\n",
+    "        ax.imshow(false[false_int_list[i]].reshape(21,21))\n",
+    "    fig_0.suptitle('True',fontsize=16)\n",
+    "    fig_1.suptitle('False',fontsize=16)\n",
+    "    fig_0.tight_layout()\n",
+    "    fig_1.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simple_model(input_shape=(21,21,1), n_classes: int = 2):\n",
+    "    model = tf.keras.models.Sequential(name='simple')\n",
+    "    model.add(tf.keras.layers.Conv2D(8, (3,3), activation='relu', input_shape=input_shape, name='conv1'))\n",
+    "    model.add(tf.keras.layers.BatchNormalization(axis = 3, name = 'bn1'))\n",
+    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(tf.keras.layers.Dropout(0.25))\n",
+    "    model.add(tf.keras.layers.Flatten())\n",
+    "    model.add(tf.keras.layers.Dense(64, activation='relu', name='fc_1'))\n",
+    "    activation = 'sigmoid' if n_classes == 1 else 'softmax'\n",
+    "    model.add(tf.keras.layers.Dense(n_classes, activation=activation, name='fc_out'))\n",
+    "    return(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def vgg6(input_shape=(21, 21, 1), n_classes: int = 2):\n",
+    "    \"\"\"\n",
+    "        VGG6\n",
+    "    :param input_shape:\n",
+    "    :param n_classes:\n",
+    "    :return:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    model = tf.keras.models.Sequential(name='VGG6')\n",
+    "    # input: 21x21 images with 1 channel -> (21, 21, 1) tensors.\n",
+    "    # this applies 16 convolution filters of size 3x3 each.\n",
+    "    model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=input_shape, name='conv1'))\n",
+    "    model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu', name='conv2'))\n",
+    "    model.add(tf.keras.layers.BatchNormalization(axis = 3, name = 'bn1'))\n",
+    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(tf.keras.layers.Dropout(0.25))\n",
+    "\n",
+    "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', name='conv3'))\n",
+    "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', name='conv4'))\n",
+    "    model.add(tf.keras.layers.BatchNormalization(axis = 3, name = 'bn2'))\n",
+    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(4, 4)))\n",
+    "    model.add(tf.keras.layers.Dropout(0.25))\n",
+    "\n",
+    "    model.add(tf.keras.layers.Flatten())\n",
+    "\n",
+    "    model.add(tf.keras.layers.Dense(256, activation='relu', name='fc_1'))\n",
+    "    model.add(tf.keras.layers.Dropout(0.5))\n",
+    "    # output layer\n",
+    "    activation = 'sigmoid' if n_classes == 1 else 'softmax'\n",
+    "    model.add(tf.keras.layers.Dense(n_classes, activation=activation, name='fc_out'))\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Resnet 50 model is the one currently used for kbmod filtering\n",
+    "\n",
+    "# Based on https://github.com/priya-dwivedi/Deep-Learning/blob/master/resnet_keras/Residual_Networks_yourself.ipynb\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
+    "from tensorflow.keras.initializers import glorot_uniform\n",
+    "from tensorflow.keras.models import Model, load_model\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "import tensorflow.keras.backend as K\n",
+    "K.set_image_data_format('channels_last')\n",
+    "def identity_block(X, f, filters, stage, block):\n",
+    "    \"\"\"\n",
+    "    Implementation of the identity block as defined in Figure 3\n",
+    "    \n",
+    "    Arguments:\n",
+    "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
+    "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
+    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
+    "    stage -- integer, used to name the layers, depending on their position in the network\n",
+    "    block -- string/character, used to name the layers, depending on their position in the network\n",
+    "    \n",
+    "    Returns:\n",
+    "    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # defining name basis\n",
+    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
+    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
+    "    \n",
+    "    # Retrieve Filters\n",
+    "    F1, F2, F3 = filters\n",
+    "    \n",
+    "    # Save the input value. You'll need this later to add back to the main path. \n",
+    "    X_shortcut = X\n",
+    "    \n",
+    "    # First component of main path\n",
+    "    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "    \n",
+    "    # Second component of main path (≈3 lines)\n",
+    "    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "    # Third component of main path (≈2 lines)\n",
+    "    X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
+    "\n",
+    "    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n",
+    "    X = Add()([X, X_shortcut])\n",
+    "    X = Activation('relu')(X)\n",
+    "    \n",
+    "    return X\n",
+    "\n",
+    "def convolutional_block(X, f, filters, stage, block, s = 2):\n",
+    "    \"\"\"\n",
+    "    Implementation of the convolutional block as defined in Figure 4\n",
+    "    \n",
+    "    Arguments:\n",
+    "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
+    "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
+    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
+    "    stage -- integer, used to name the layers, depending on their position in the network\n",
+    "    block -- string/character, used to name the layers, depending on their position in the network\n",
+    "    s -- Integer, specifying the stride to be used\n",
+    "    \n",
+    "    Returns:\n",
+    "    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # defining name basis\n",
+    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
+    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
+    "    \n",
+    "    # Retrieve Filters\n",
+    "    F1, F2, F3 = filters\n",
+    "    \n",
+    "    # Save the input value\n",
+    "    X_shortcut = X\n",
+    "\n",
+    "\n",
+    "    ##### MAIN PATH #####\n",
+    "    # First component of main path \n",
+    "    X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "    # Second component of main path (≈3 lines)\n",
+    "    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "\n",
+    "\n",
+    "    # Third component of main path (≈2 lines)\n",
+    "    X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
+    "\n",
+    "\n",
+    "    ##### SHORTCUT PATH #### (≈2 lines)\n",
+    "    X_shortcut = Conv2D(filters = F3, kernel_size = (1, 1), strides = (s,s), padding = 'valid', name = conv_name_base + '1',\n",
+    "                        kernel_initializer = glorot_uniform(seed=0))(X_shortcut)\n",
+    "    X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)\n",
+    "\n",
+    "    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n",
+    "    X = Add()([X, X_shortcut])\n",
+    "    X = Activation('relu')(X)\n",
+    "    \n",
+    "    \n",
+    "    return X\n",
+    "\n",
+    "def ResNet50(input_shape=(21, 21, 1), classes=2):\n",
+    "    \"\"\"\n",
+    "    Implementation of the popular ResNet50 the following architecture:\n",
+    "    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n",
+    "    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER\n",
+    "\n",
+    "    Arguments:\n",
+    "    input_shape -- shape of the images of the dataset\n",
+    "    classes -- integer, number of classes\n",
+    "\n",
+    "    Returns:\n",
+    "    model -- a Model() instance in Keras\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Define the input as a tensor with shape input_shape\n",
+    "    X_input = Input(input_shape)\n",
+    "\n",
+    "    # Zero-Padding\n",
+    "    X = ZeroPadding2D((3, 3))(X_input)\n",
+    "\n",
+    "    # Stage 1\n",
+    "    X = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', kernel_initializer=glorot_uniform(seed=0))(X)\n",
+    "    X = BatchNormalization(axis=3, name='bn_conv1')(X)\n",
+    "    X = Activation('relu')(X)\n",
+    "    X = MaxPooling2D((3, 3), strides=(2, 2))(X)\n",
+    "\n",
+    "    # Stage 2\n",
+    "    X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=2, block='a', s=1)\n",
+    "    X = identity_block(X, 3, [128, 128, 512], stage=2, block='b')\n",
+    "    X = identity_block(X, 3, [128, 128, 512], stage=2, block='c')\n",
+    "\n",
+    "    ### START CODE HERE ###\n",
+    "\n",
+    "    # Stage 3 (≈4 lines)\n",
+    "    X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)\n",
+    "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')\n",
+    "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')\n",
+    "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')\n",
+    "\n",
+    "    # Stage 4 (≈6 lines)\n",
+    "    X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)\n",
+    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')\n",
+    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')\n",
+    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')\n",
+    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')\n",
+    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')\n",
+    "\n",
+    "    # Stage 5 (≈3 lines)\n",
+    "    X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)\n",
+    "    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')\n",
+    "    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')\n",
+    "\n",
+    "    # AVGPOOL (≈1 line). Use \"X = AveragePooling2D(...)(X)\"\n",
+    "    #X = AveragePooling2D((2,2), name=\"avg_pool\")(X)\n",
+    "\n",
+    "    ### END CODE HERE ###\n",
+    "\n",
+    "    # output layer\n",
+    "    X = Flatten()(X)\n",
+    "    X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)\n",
+    "    \n",
+    "    \n",
+    "    # Create model\n",
+    "    model = Model(inputs = X_input, outputs = X, name='ResNet50')\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load the true and false data sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These are the current datasets used to train the CNN model\n",
+    "# used in KBMOD paper 2\n",
+    "\n",
+    "false_positives = np.load('../data_files/normed_individual_false.npy') #False stamps\n",
+    "false_positives[np.isnan(false_positives)] = 0\n",
+    "fake_positives = np.load('../data_files/normed_individual_simulated_2.npy') #True stamps\n",
+    "fake_positives[np.isnan(fake_positives)] = 0\n",
+    "n=40000\n",
+    "false_index = np.random.choice(false_positives.shape[0], n, replace=False)\n",
+    "true_index = np.random.choice(fake_positives.shape[0], n, replace=False)\n",
+    "false_positives = np.copy(false_positives[false_index])\n",
+    "fake_positives = np.copy(fake_positives[true_index])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_true_false(fake_positives, false_positives)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_stamps = np.vstack([false_positives, fake_positives])\n",
+    "stamp_class = np.zeros(len(false_positives) + len(fake_positives))\n",
+    "stamp_class[len(false_positives):] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rand_state = np.random.RandomState(32)\n",
+    "idx = rand_state.permutation(len(input_stamps))\n",
+    "input_stamps = input_stamps[idx]\n",
+    "stamp_class = stamp_class[idx]\n",
+    "stamp_class = to_categorical(stamp_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_len = np.int(0.7*len(stamp_class))\n",
+    "val_len = np.int(0.2*len(stamp_class))\n",
+    "test_len = len(stamp_class) - train_len - val_len\n",
+    "\n",
+    "train_data = input_stamps[:train_len]\n",
+    "train_class = stamp_class[:train_len]\n",
+    "train_data = train_data.reshape(-1,21,21,1)   #Reshape for CNN -  should work!!\n",
+    "val_data = input_stamps[train_len:train_len+val_len]\n",
+    "val_class = stamp_class[train_len:train_len+val_len]\n",
+    "val_data = val_data.reshape(-1,21,21,1)\n",
+    "test_data = input_stamps[train_len+val_len:]\n",
+    "test_class = stamp_class[train_len+val_len:]\n",
+    "test_data = test_data.reshape(-1,21,21,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train the CNNs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "219/219 [==============================] - 11s 51ms/step - loss: 0.2557 - accuracy: 0.8997 - val_loss: 0.9872 - val_accuracy: 0.5006\n",
+      "Epoch 2/20\n",
+      "219/219 [==============================] - 9s 43ms/step - loss: 0.1552 - accuracy: 0.9419 - val_loss: 1.4583 - val_accuracy: 0.5006\n",
+      "Epoch 3/20\n",
+      "219/219 [==============================] - 9s 43ms/step - loss: 0.1271 - accuracy: 0.9543 - val_loss: 0.7054 - val_accuracy: 0.7542\n",
+      "Epoch 4/20\n",
+      "219/219 [==============================] - 9s 43ms/step - loss: 0.1009 - accuracy: 0.9629 - val_loss: 0.4934 - val_accuracy: 0.9468\n",
+      "Epoch 5/20\n",
+      "219/219 [==============================] - 10s 43ms/step - loss: 0.0848 - accuracy: 0.9687 - val_loss: 0.0912 - val_accuracy: 0.9672\n",
+      "Epoch 6/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0794 - accuracy: 0.9717 - val_loss: 0.1077 - val_accuracy: 0.9629\n",
+      "Epoch 7/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0682 - accuracy: 0.9746 - val_loss: 0.0935 - val_accuracy: 0.9659\n",
+      "Epoch 8/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0621 - accuracy: 0.9767 - val_loss: 0.1343 - val_accuracy: 0.9559\n",
+      "Epoch 9/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0570 - accuracy: 0.9794 - val_loss: 0.1191 - val_accuracy: 0.9602\n",
+      "Epoch 10/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0541 - accuracy: 0.9811 - val_loss: 0.0895 - val_accuracy: 0.9688\n",
+      "Epoch 11/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0467 - accuracy: 0.9831 - val_loss: 0.0914 - val_accuracy: 0.9692\n",
+      "Epoch 12/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0459 - accuracy: 0.9836 - val_loss: 0.0935 - val_accuracy: 0.9707\n",
+      "Epoch 13/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0437 - accuracy: 0.9852 - val_loss: 0.1588 - val_accuracy: 0.9542\n",
+      "Epoch 14/20\n",
+      "219/219 [==============================] - 10s 44ms/step - loss: 0.0402 - accuracy: 0.9856 - val_loss: 0.1052 - val_accuracy: 0.9671\n",
+      "Epoch 15/20\n",
+      "219/219 [==============================] - 10s 45ms/step - loss: 0.0339 - accuracy: 0.9879 - val_loss: 0.1216 - val_accuracy: 0.9659\n",
+      "Epoch 16/20\n",
+      "219/219 [==============================] - 10s 45ms/step - loss: 0.0322 - accuracy: 0.9882 - val_loss: 0.1107 - val_accuracy: 0.9681\n",
+      "Epoch 17/20\n",
+      "219/219 [==============================] - 10s 45ms/step - loss: 0.0300 - accuracy: 0.9891 - val_loss: 0.1007 - val_accuracy: 0.9709\n",
+      "Epoch 18/20\n",
+      "219/219 [==============================] - 10s 45ms/step - loss: 0.0272 - accuracy: 0.9909 - val_loss: 0.1133 - val_accuracy: 0.9686\n",
+      "Epoch 19/20\n",
+      "219/219 [==============================] - 10s 45ms/step - loss: 0.0252 - accuracy: 0.9910 - val_loss: 0.2096 - val_accuracy: 0.9524\n",
+      "Epoch 20/20\n",
+      "219/219 [==============================] - 10s 46ms/step - loss: 0.0244 - accuracy: 0.9916 - val_loss: 0.1166 - val_accuracy: 0.9678\n"
+     ]
+    }
+   ],
+   "source": [
+    "resnet50_model = ResNet50(input_shape=(21,21,1))\n",
+    "sgd = tf.keras.optimizers.SGD(lr=0.005, momentum=0.05, nesterov=False)\n",
+    "resnet50_model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
+    "n_epochs=20\n",
+    "resnet_model_history = resnet50_model.fit(train_data, train_class, epochs=n_epochs, batch_size=256, verbose=1, validation_data=(val_data, val_class), shuffle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /epyc/opt/anaconda-2019/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
+      "Train on 56000 samples, validate on 16000 samples\n",
+      "Epoch 1/20\n",
+      "56000/56000 [==============================] - 6s 103us/sample - loss: 0.6877 - acc: 0.5889 - val_loss: 0.6924 - val_acc: 0.5872\n",
+      "Epoch 2/20\n",
+      "56000/56000 [==============================] - 2s 32us/sample - loss: 0.6834 - acc: 0.6942 - val_loss: 0.6917 - val_acc: 0.7707\n",
+      "Epoch 3/20\n",
+      "56000/56000 [==============================] - 2s 36us/sample - loss: 0.6797 - acc: 0.7391 - val_loss: 0.6899 - val_acc: 0.5597\n",
+      "Epoch 4/20\n",
+      "56000/56000 [==============================] - 2s 38us/sample - loss: 0.6745 - acc: 0.7686 - val_loss: 0.6860 - val_acc: 0.6582\n",
+      "Epoch 5/20\n",
+      "56000/56000 [==============================] - 2s 37us/sample - loss: 0.6693 - acc: 0.7941 - val_loss: 0.6788 - val_acc: 0.8084\n",
+      "Epoch 6/20\n",
+      "56000/56000 [==============================] - 2s 35us/sample - loss: 0.6634 - acc: 0.8167 - val_loss: 0.6683 - val_acc: 0.8713\n",
+      "Epoch 7/20\n",
+      "56000/56000 [==============================] - 2s 37us/sample - loss: 0.6564 - acc: 0.8298 - val_loss: 0.6563 - val_acc: 0.8566\n",
+      "Epoch 8/20\n",
+      "56000/56000 [==============================] - 2s 34us/sample - loss: 0.6479 - acc: 0.8389 - val_loss: 0.6434 - val_acc: 0.8828\n",
+      "Epoch 9/20\n",
+      "56000/56000 [==============================] - 2s 36us/sample - loss: 0.6374 - acc: 0.8428 - val_loss: 0.6297 - val_acc: 0.8896\n",
+      "Epoch 10/20\n",
+      "56000/56000 [==============================] - 2s 36us/sample - loss: 0.6242 - acc: 0.8469 - val_loss: 0.6136 - val_acc: 0.8843\n",
+      "Epoch 11/20\n",
+      "56000/56000 [==============================] - 2s 38us/sample - loss: 0.6083 - acc: 0.8507 - val_loss: 0.5946 - val_acc: 0.8857\n",
+      "Epoch 12/20\n",
+      "56000/56000 [==============================] - 2s 39us/sample - loss: 0.5889 - acc: 0.8500 - val_loss: 0.5736 - val_acc: 0.8883\n",
+      "Epoch 13/20\n",
+      "56000/56000 [==============================] - 2s 35us/sample - loss: 0.5667 - acc: 0.8484 - val_loss: 0.5483 - val_acc: 0.8838\n",
+      "Epoch 14/20\n",
+      "56000/56000 [==============================] - 2s 34us/sample - loss: 0.5412 - acc: 0.8480 - val_loss: 0.5234 - val_acc: 0.8603\n",
+      "Epoch 15/20\n",
+      "56000/56000 [==============================] - 2s 38us/sample - loss: 0.5148 - acc: 0.8519 - val_loss: 0.4959 - val_acc: 0.8911\n",
+      "Epoch 16/20\n",
+      "56000/56000 [==============================] - 2s 35us/sample - loss: 0.4868 - acc: 0.8547 - val_loss: 0.4644 - val_acc: 0.8774\n",
+      "Epoch 17/20\n",
+      "56000/56000 [==============================] - 2s 33us/sample - loss: 0.4591 - acc: 0.8570 - val_loss: 0.4364 - val_acc: 0.8822\n",
+      "Epoch 18/20\n",
+      "56000/56000 [==============================] - 2s 39us/sample - loss: 0.4347 - acc: 0.8609 - val_loss: 0.4102 - val_acc: 0.8778\n",
+      "Epoch 19/20\n",
+      "56000/56000 [==============================] - 2s 38us/sample - loss: 0.4110 - acc: 0.8645 - val_loss: 0.3888 - val_acc: 0.8907\n",
+      "Epoch 20/20\n",
+      "56000/56000 [==============================] - 2s 37us/sample - loss: 0.3882 - acc: 0.8690 - val_loss: 0.3675 - val_acc: 0.8894\n"
+     ]
+    }
+   ],
+   "source": [
+    "simple_cnn = simple_model(input_shape=(21,21,1))\n",
+    "sgd = tf.keras.optimizers.SGD(lr=0.005, momentum=0.05, nesterov=True)\n",
+    "simple_cnn.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
+    "n_epochs = 20\n",
+    "simple_model_history = simple_cnn.fit(train_data, train_class, epochs=n_epochs, batch_size=512, verbose=1, validation_data=(val_data, val_class), shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv1 (Conv2D)               (None, 19, 19, 8)         1808      \n",
+      "_________________________________________________________________\n",
+      "bn1 (BatchNormalization)     (None, 19, 19, 8)         32        \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_2 (MaxPooling2 (None, 9, 9, 8)           0         \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 9, 9, 8)           0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 648)               0         \n",
+      "_________________________________________________________________\n",
+      "fc_1 (Dense)                 (None, 64)                41536     \n",
+      "_________________________________________________________________\n",
+      "fc_out (Dense)               (None, 2)                 130       \n",
+      "=================================================================\n",
+      "Total params: 43,506\n",
+      "Trainable params: 43,490\n",
+      "Non-trainable params: 16\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "simple_cnn.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 56000 samples, validate on 16000 samples\n",
+      "Epoch 1/50\n",
+      "56000/56000 [==============================] - 1s 21us/sample - loss: 0.6614 - acc: 0.6661 - val_loss: 0.6932 - val_acc: 0.5006\n",
+      "Epoch 2/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.6093 - acc: 0.7179 - val_loss: 0.6930 - val_acc: 0.5006\n",
+      "Epoch 3/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.5619 - acc: 0.7598 - val_loss: 0.6909 - val_acc: 0.5161\n",
+      "Epoch 4/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.5255 - acc: 0.7852 - val_loss: 0.6717 - val_acc: 0.6351\n",
+      "Epoch 5/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.4944 - acc: 0.8059 - val_loss: 0.6431 - val_acc: 0.6749\n",
+      "Epoch 6/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.4645 - acc: 0.8222 - val_loss: 0.5529 - val_acc: 0.7294\n",
+      "Epoch 7/50\n",
+      "56000/56000 [==============================] - 1s 14us/sample - loss: 0.4412 - acc: 0.8342 - val_loss: 0.5314 - val_acc: 0.7199\n",
+      "Epoch 8/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.4259 - acc: 0.8424 - val_loss: 0.6181 - val_acc: 0.6482\n",
+      "Epoch 9/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.4069 - acc: 0.8501 - val_loss: 0.8149 - val_acc: 0.6106\n",
+      "Epoch 10/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3938 - acc: 0.8568 - val_loss: 0.6676 - val_acc: 0.6689\n",
+      "Epoch 11/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3782 - acc: 0.8619 - val_loss: 0.7142 - val_acc: 0.6329\n",
+      "Epoch 12/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3698 - acc: 0.8671 - val_loss: 0.7028 - val_acc: 0.6433\n",
+      "Epoch 13/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3599 - acc: 0.8704 - val_loss: 0.3480 - val_acc: 0.8473\n",
+      "Epoch 14/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3542 - acc: 0.8716 - val_loss: 1.4910 - val_acc: 0.5084\n",
+      "Epoch 15/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3458 - acc: 0.8738 - val_loss: 0.3679 - val_acc: 0.8616\n",
+      "Epoch 16/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3369 - acc: 0.8777 - val_loss: 1.0087 - val_acc: 0.6114\n",
+      "Epoch 17/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3305 - acc: 0.8809 - val_loss: 1.0070 - val_acc: 0.5527\n",
+      "Epoch 18/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3269 - acc: 0.8813 - val_loss: 0.7111 - val_acc: 0.6814\n",
+      "Epoch 19/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3212 - acc: 0.8833 - val_loss: 0.4883 - val_acc: 0.7783\n",
+      "Epoch 20/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.3157 - acc: 0.8829 - val_loss: 0.6107 - val_acc: 0.6122\n",
+      "Epoch 21/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3102 - acc: 0.8851 - val_loss: 1.3613 - val_acc: 0.5491\n",
+      "Epoch 22/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3060 - acc: 0.8866 - val_loss: 0.3600 - val_acc: 0.8241\n",
+      "Epoch 23/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.3050 - acc: 0.8869 - val_loss: 0.3172 - val_acc: 0.8610\n",
+      "Epoch 24/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.3004 - acc: 0.8895 - val_loss: 0.9303 - val_acc: 0.5694\n",
+      "Epoch 25/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.2942 - acc: 0.8901 - val_loss: 0.6000 - val_acc: 0.6544\n",
+      "Epoch 26/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.2925 - acc: 0.8899 - val_loss: 0.3304 - val_acc: 0.8490\n",
+      "Epoch 27/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.2840 - acc: 0.8932 - val_loss: 0.3271 - val_acc: 0.8214\n",
+      "Epoch 28/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2802 - acc: 0.8939 - val_loss: 1.4309 - val_acc: 0.5375\n",
+      "Epoch 29/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2768 - acc: 0.8951 - val_loss: 0.3645 - val_acc: 0.8000\n",
+      "Epoch 30/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2746 - acc: 0.8957 - val_loss: 1.0237 - val_acc: 0.5866\n",
+      "Epoch 31/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.2700 - acc: 0.8967 - val_loss: 0.5602 - val_acc: 0.6547\n",
+      "Epoch 32/50\n",
+      "56000/56000 [==============================] - 1s 12us/sample - loss: 0.2699 - acc: 0.8977 - val_loss: 0.6460 - val_acc: 0.6837\n",
+      "Epoch 33/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2630 - acc: 0.8992 - val_loss: 3.5722 - val_acc: 0.4991\n",
+      "Epoch 34/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2619 - acc: 0.8999 - val_loss: 0.3282 - val_acc: 0.8312\n",
+      "Epoch 35/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2582 - acc: 0.9001 - val_loss: 0.2500 - val_acc: 0.8976\n",
+      "Epoch 36/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2527 - acc: 0.9025 - val_loss: 3.3010 - val_acc: 0.5016\n",
+      "Epoch 37/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2510 - acc: 0.9021 - val_loss: 0.7224 - val_acc: 0.6697\n",
+      "Epoch 38/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2469 - acc: 0.9039 - val_loss: 0.2777 - val_acc: 0.8689\n",
+      "Epoch 39/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2448 - acc: 0.9035 - val_loss: 0.7621 - val_acc: 0.6724\n",
+      "Epoch 40/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2416 - acc: 0.9065 - val_loss: 3.8964 - val_acc: 0.4999\n",
+      "Epoch 41/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2426 - acc: 0.9063 - val_loss: 0.4187 - val_acc: 0.7680\n",
+      "Epoch 42/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2544 - acc: 0.9024 - val_loss: 1.8402 - val_acc: 0.5167\n",
+      "Epoch 43/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2519 - acc: 0.9034 - val_loss: 1.3408 - val_acc: 0.5769\n",
+      "Epoch 44/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2481 - acc: 0.9042 - val_loss: 0.3989 - val_acc: 0.8117\n",
+      "Epoch 45/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2457 - acc: 0.9043 - val_loss: 1.7283 - val_acc: 0.4999\n",
+      "Epoch 46/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2437 - acc: 0.9069 - val_loss: 2.0519 - val_acc: 0.5163\n",
+      "Epoch 47/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2385 - acc: 0.9077 - val_loss: 0.9827 - val_acc: 0.5773\n",
+      "Epoch 48/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2396 - acc: 0.9067 - val_loss: 0.2975 - val_acc: 0.8369\n",
+      "Epoch 49/50\n",
+      "56000/56000 [==============================] - 1s 14us/sample - loss: 0.2308 - acc: 0.9093 - val_loss: 1.4508 - val_acc: 0.6106\n",
+      "Epoch 50/50\n",
+      "56000/56000 [==============================] - 1s 13us/sample - loss: 0.2286 - acc: 0.9104 - val_loss: 2.1773 - val_acc: 0.5482\n"
+     ]
+    }
+   ],
+   "source": [
+    "vgg_model = vgg6(input_shape=(21,21,1))\n",
+    "sgd = tf.keras.optimizers.SGD(lr=0.001, momentum=0.0, nesterov=False)\n",
+    "vgg_model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
+    "n_epochs=50\n",
+    "vgg_model_history = vgg_model.fit(train_data, train_class, epochs=n_epochs, batch_size=512, verbose=1, validation_data=(val_data, val_class), shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv1 (Conv2D)               (None, 19, 19, 16)        160       \n",
+      "_________________________________________________________________\n",
+      "conv2 (Conv2D)               (None, 17, 17, 16)        2320      \n",
+      "_________________________________________________________________\n",
+      "bn1 (BatchNormalizationV1)   (None, 17, 17, 16)        64        \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_30 (MaxPooling (None, 8, 8, 16)          0         \n",
+      "_________________________________________________________________\n",
+      "dropout_40 (Dropout)         (None, 8, 8, 16)          0         \n",
+      "_________________________________________________________________\n",
+      "conv3 (Conv2D)               (None, 6, 6, 32)          4640      \n",
+      "_________________________________________________________________\n",
+      "conv4 (Conv2D)               (None, 4, 4, 32)          9248      \n",
+      "_________________________________________________________________\n",
+      "bn2 (BatchNormalizationV1)   (None, 4, 4, 32)          128       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_31 (MaxPooling (None, 1, 1, 32)          0         \n",
+      "_________________________________________________________________\n",
+      "dropout_41 (Dropout)         (None, 1, 1, 32)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_17 (Flatten)         (None, 32)                0         \n",
+      "_________________________________________________________________\n",
+      "fc_1 (Dense)                 (None, 256)               8448      \n",
+      "_________________________________________________________________\n",
+      "dropout_42 (Dropout)         (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "fc_out (Dense)               (None, 2)                 514       \n",
+      "=================================================================\n",
+      "Total params: 25,522\n",
+      "Trainable params: 25,426\n",
+      "Non-trainable params: 96\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "vgg_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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l5/1lw8VL4nU3J0piJj5HQwvShZ+8LV5558p43bbh9LR5qQqfI33x67vt9Hifc9alUyhH5levkOrtzfsTWmYGV2cvItL6WvYErZk9AQwARWDU3VfXo1Ei082BYgc/21v9gsElHXFp4HVDC5PbXVuIL1p7XnecE5+ajPyi3p+HsXNmPxLGnt+1PYwBLCjEs3UNefxL+HUnfzmM7fU4X36WxbXjuxP5+QBvnhM/z1/re6jq4xd2pks8u7f+mP1L3T2ewUJEZEYwisrGERFpfc08Zn+4X0MOfNfM7somX34OM7vMzNaY2ZqR4XhmHRGR6WysNk6eWyMc7pH9Oe6+ycyOBG41s5+5++2VC7j71cDVALPnLkundoiITFdeHrdvVod1ZO/um7J/twJfB86uR6NERKajEpbr1ggTPrI3s16g4O4D2f9fDnwkuc7Afjq/f3fV2MiLfyG5v/b9ce51ceHseJ/DcR64z0m/6G2D8bpWjL/Ct51TPStjTGEojh2YF7dp9/L47bJEej7AaKJScdeOONa9PX2osm9p9Zx3gKHZ8XNZ+oPdYczbahyDFILtlpr4sEpanrfwCdpFwNfNbGw7X3D3b9elVSLTTH/7Pn5j4Y+rxua17Q/Xe0lPXIq4liPausLYjmKcIJc6snxR984wNreQLnFc9PiIY1aNVMhQ4iCmI5F6WUqtCHQQr0uidHItzTyMM+HO3t3XAmfUsS0iItNaM2fjKPVSRKQO3NXZi4jMCK1+Ba2IiNCiY/YiIvIsxyi1aDbOIfO+WYz8UvVzuh270pMyl7riprrFP506NsTFm0bmplMke56M0wOHlsSFpvo2Die3OzorzgQYmht/WPbE1Zrp2Zr++TjcH2cYDCfSPXvSta/oejouVNXzdOIwJ5EsMTI/nbnRuTt4fZv4qEpmhmb+COrIXqSKQ63o2m2jnNJZPW1xUVtcDfKnw3H6JMCK9vjL9ECikmR/Ii2zkLiWMpWymEqtBNjr8QUkcy1+DVImmrI5VKOtqbTN6LVrsxpH7XU8QWtm5wNXAW3Ate5+ZZVl3gBcUd4z97j7m1LbVGcvElNFVzk0dTi0N7M24DPArwIbgDvN7GZ3f7BimRXA+ymXrNmZlaxJat4BJhGRacbdct1qOBt4zN3Xuvsw8CXg4nHLvBP4jLvvLO/Xt9baqDp7kepqVnQVqeRAqWS5bsDCsWrA2a3yM7YUWF9xf0P2WKWVwEoz+3czuyMb9knSMI5IdTUrumZ/oJcBLF2q46YZz4H8Y/bbEueBqm1k/ABRO7ACOBdYBvybmZ3m7uF0WvqEilSRp6Kru1/t7qvdfXV/v/6UZOwq2tq3GjYAR1fcXwaMn4NyA/ANdx9x958DD1Pu/ENTemRfGBql+/Gnq8b2n7QouW7HQJyV0P5gPKfmgbNOjLe5J50iObworqbZNhhnQjx9ZrpgVNtw/G7vWR6vV1wYt3dveipTOrrj9o7uSFSunJMqGAVucbZEMZFIMWt7/NHrfWxPcp9WrJ5GaqUapT9zmkhFVxGgXrmXdwIrzGw5sBF4IzA+0+Ym4BLgejNbSHlYZ21qoxrGEXmuQ67o2mbG7EL1L8YRj69xOK0j3ZA2ixfYXYq//OcW4vU2j+4NY+94/A1h7KPH3RTGAJ4uzgtjL+uJK3+m0iAnqivxugEMJSYyj1JavWZPnuvka03uPmpm7wa+Qzn18jp3f8DMPgKscfebs9jLzexByunB73P35FUx6uxFxlFFV5mwOl1V5e63ALeMe+xDFf934A+zWy7q7EVE6sHBSyqEJiIyA6izFxFpfU1cHEedvYhIvaizFxFpcYd2UdWUm9oSx+0FivOr5653b47TwQBG58S54Cw9Kgx1P7oljI0ck05O37ckThQ/MD++iKZjb/rrfcfpcbz7+IEw9uenfTOMrehMl8b4/I5fDmNfvf/MMDawPH2xUO/G+MPdtzFOOezaGae+Ffany10zHKxbrE+e/UQYFk5ivTeR5rerRpNv3P2LYezErvizfUbXxjB2+YbxZVaeVXx//Ddx6VnvCWMAi299Koz9zvsWhLG/+W+fD2Pn9cR/D+2JScNLNQ6xU6mZqWqitWjyEhGRmUDZOCIirc90ZC8i0uIcnaAVEWl9phO0IiIzgo7sRURmgMYlhNU0pZ19qbuNPc+rnnrZ9+Rgcl1vj38e7Tt+bhiz4pzERpO7pH0wXqDQF8f2H5X+KVfsjVMSR0bidLIFbXF66i90JlJTgYcHEiWkB+I0tLZj9iW3a+v7wljH3viTv3dJPCG2L4tTaQFmPV09Na60u0YJyUnkOCNUf1+7EhNVd1v8WQC4aPY9YWxVV/waFj3+PNx9+8owdvxdd4WxI+9IlwQvBlU/ARb8OP78/cVX3xzG/vBF8Xv6x6/7ehi7sPexMAawuD3+3A6Uqr8nxVp5lU2eZ19zxgUzu87MtprZ/RWP9ZvZrWb2aPbv/MltpohI8zPPd2uEPNPrXA+Mn9/wT4DvufsK4HvZfRGRmc1z3hqgZmefzbu5Y9zDFwM3ZP+/AXhNndslIiJ1NNEx+0XuvhnA3TdnkzJXVTkpc+csjfaISOtq5ouqJn2W5MpJmTu603OziohMW065XEKeWwNMtLPfYmaLAbJ/01W4RERmgiYes5/oMM7NwNuAK7N/v5FnpcJwid4N1Ssatu0ZSq88J04z694XVxVsf3pPGHvqV5ckd9n/UFx98cD8OLVtZE763WzfE6eozT0mTkHdVZoV79N3J/f5i/OeDGPrlsbDawMbEqmrwNIH4/dtz3Fx1dDRWfHRzaI74kqHAAeO7KkeaN6sN5khmnkYp2Znb2ZfBM4FFprZBuByyp38jWb2DuBJ4PWT2UiRZjfssCGojLvP4z+zo9vSefaPjxwRxvb5rjD2gvjYiNdd8O9h7K4PHsa1CkF+OsCC6+4IY9YZHxQs/IOlYexrF5wdxr5xw6owBnDzim+HsXmF6u9Xm+U4mpjOnb27XxKEXlbntoiITG/TubMXEZHaGnnBVB7q7EVE6kWTl4iItD4d2YuIzATq7DMOheHqZ+z3L0+n+BVG4lfRSnFscElcba97Z7oe6fC8iWUm1Cp8170tXuDXj7k7jL2wO55g+nuD/cl9fu+p54WxwQfnhbFlP0lni1jiJezaHQfnPBFP6rz32DjFFKDn6eqptqm2iEw6jdmLNCczuw64CNjq7qdlj/UD/wwcBzwBvMHdd9baVhvO7EL1L8ZFiW+hTcX0n+BFvdvDWCFxTeRoorD65UfGZYwvbjsnjFln+uDHEyWAfST+ck/57ElfCGPXfuXFYeyM3vUT2h9Al1V/noU8F3I0cWc/6eUSRJrY9aiiq9SRlfLdGkGdvcxYqugqM4k6e5GDHVTRFUhWdDWzNWa2ZvsOnTAQmro2jjp7kQmqrOi6oF9/SjNezlmqmnmmKpGZRBVdZeJ0ZC8ybYxVdIVDqOgqAjR1Zz/FefYe5sR3b4lL+wKMzIkr43Vuj0sRF+bF5f86dsbrAex+Xpz7X0pkofVuTKdojcQT23PjE2eGsZNP2hjGrlp3XnKfm7bFufSpwouD/XE5ZoDdx8UfoSPuSbwvB+Kdbj81UbIR6NpZPY3vUP+G6lnRtWDGrKAq4l9u++VwvQ8f+dPkdh8ZGQ5jl69/VRhb2Rf/IPnokfG1HAfOOyOMfeXqvwpjAG899YIwVhyKS2Hvet3zw9hVT8V/aLtH4jLjv7Pwh2GsLL6WY79Xf81LNT5hRnNf66E8e5mxVNFV6qqO4/Fmdj5wFdAGXOvuVwbLvQ74MvB8d1+T2qaGcURE6qUOwzhm1gZ8BrgAOAW4xMxOqbLcbOD3gR/naZo6exGReqnPmP3ZwGPuvtbdh4EvUb7+Y7yPAp8A0uPRGXX2IiJ1cgiplwvHrtHIbpdVbGYpUFnvYUP22LP7MTsTONrd/1/etmnMXkSkXvKP2W9z99VBrNqZ/me2bGYF4FPA2w+laersRUTqweuWjbMBOLri/jJgU8X92cBpwG1WzgA7CrjZzF6dOkk7pZ19qbPA/mXVU566t8apWQDd6xKFBxPV9to64pGqPStmJ/c596F4QmfvjF+6/UvTJXp3rozXHbltYRj7wMhr431uTuRzAjY3TuFLZRC0H0gfqnhiIHC0O5G22RPHFv/HQHKfNlI9bbMw2ri8t/KE49Xf11+Z/XC4XpTmN2agFKccf275N8PYiMevRaqy9zeu+XQY250oJQ7w0Xv+NYy9/82XhbHOt8alu297bEUY+7PV/xLGegvpz8LuUjrVe8Lqk41zJ7DCzJYDG4E3Am96Zhfuu4FnOgozuw14r7JxRESmSD3KJbj7KPBu4DvAQ8CN7v6AmX3EzF490bZpGEdEpF7qlGfv7rcAt4x77EPBsufm2aY6exGRemhgKYQ81NmLiNSBoWkJRURmBHX2IiIzgTr7srYDRfoeqZ7OWOpJT2bMzt1haPj0Y8NY17rxs849a962dIrf0LH9YWz3cXFlxtHedNXL0Z44NjInThlrv29uGFtyX6J0JbDn2MROE6yY/vQuuHdvGGtPvL4Dp4cTQHFgYVzNsNyo6g+X1jY2uawQHNb9w5YXhuscvyx9AeT39z6nJMozlsyLJw7/2sBpYezCvgfC2BMjcaXXh4eWhDGAT3/zwjD2W5/9fhi79gcvDWM+K/5cFxN5vzU+tvRZ4u+X6vu0Vp9w3MyuM7OtZnZ/xWNXmNlGM7s7u8XvsojITNACM1VdD5xf5fFPufuq7HZLlbiIyMwynScvcffbzey4yW+KiMj01syTlxzOIOe7zezebJhnfrSQmV02VtltuLj/MHYnItLcpvswTjWfBU4AVgGbgU9GC7r71e6+2t1Xd7ala8aIiExbeYdwplNn7+5b3L3o7iXgGsrF9kVEZrYm7uwnlHppZovdfXN297XA/anlRVpdB84RheoTob9y4b3hev01Drfe1R9PDj7icSrg83vWhrFlbXGa8wntcfXZl89aF8YALntLXDFzRzHe7u//+j1hrMfiqp9RiiRAl6WrwA75SBg74NGE9jkmHG/i1Muanb2ZfRE4l/LMKhuAy4FzzWwV5e+oJ4DfzrW3UgnbV7206MhR6XLDB156Yhib95NNYSxlZGmcRw/Qviv+gM5eH/+hjfYlSvsCR66JZxHbfWI81NW7OW7PvsXp6xR6tsafwpHeeL35d2xMbndwRZwvP9q3II51J3Kk56bzmec/WD1/30aa+OyYzAhWowx0I+XJxrmkysOfm4S2iIhMXyqEJiIyM0zrYRwREclJnb2ISOvTkb2IyEygzl5EpMV5c5dLmNLOvtjbya6zq5dJrZU+2f1EIp2xEKfx7T1tUa62VdN3b1weuW1OnP9bGk4nT287I851PPKO6iWgAbafOS9uz3D6kOLAgjidsWt3Ii1zSTo9NfWztWtjXJa6MJzOg07uc7T6X9Sh/oQ2s+uAi4Ct7n5a9tgVwDuBp7PFPnC4hf6O63g6jG0ppj8rDwwfFcY2DMeprZfNezCxz+p55ABHtMW9VRfp9N524r/Rxe3x+53KeS8lDpVHPM6zj3Lln9mux9vdVKz+t1LjT6zp8+wbWwBcpLGuRxVdpZ7c890aQMM4MmOpoqvUm47sRaaXQ67oun1HEw/WytRoxUJoIi1sQhVdF9QqciMzgpXy3RpBwzgiFdx9y9j/zewaID1JrEiFZs7G0eGISAUzW1xxVxVdJT9HJ2grRScwSrMTpRcB27I9Ds6LK2aOdscph+2D6Rfd++IKlLuXd4exrj3pr/f26oU/AbD9cWXLzoF4u6M96UqR3Tvjdbt3xilsg4vj5wnQvi9elx1x6mXn0HAYG33iyeQ+C8uWBism2lJFPSu6OhAlEB7bHs/Q1m3p962jK05JvnDWljDWZXGa5PKO+D1Np0GmP9eFROplSiplc2cp/mNJtWZfjeqTu0vx69NxGIfnzXyCVsM4MmOpoqvUnTp7EZHW1uwXVamzFxGpB/fpPXmJiIjk1Lx9vTp7EZF60TCOiEirc0DDOGWFUad7e/XUrsLAvuS6IyuqV8sEaH8oTtWb81BcnXJkYZxaCTCwMq4yOe+ROJ2ubV+cVggwOidOfbPE69C1K7xyn4696czdrhcAAAbRSURBVBS+WY9uC2PDSxLVNDvT6XRD8+OPkJ15bHLdcJ/HxtUcAYaL1f+gfGfjjl3uu2902/Kjn1qX3V0IxC/41FN70vK2p/YHunn7eh3Zi9SDux8x9n8zW+PuqxvZnkpqT1o921OvYRwzOx+4CmgDrnX3K8fF/xC4FBilXI77v7v7uudsqIKuoBURqRMrea5bchtmbcBngAuAU4BLzOyUcYv9FFjt7r8AfAX4RK22qbMXEamH+lW9PBt4zN3Xuvsw8CXg4oN25f4Ddx8bS74DWFZro+rsRerv6kY3YBy1J60u7SlfVOW5bjUsBdZX3N+QPRZ5B/CtWhvVmL1Inbl7U3Vmak9aXduTv6zOQjNbU3H/6op2VMu2qPoNYWZvBlYDL6m1Q3X2IiJ1kuOofcy2xEnhDcDRFfeXAc+piGdm5wF/CrzE3eMKihkN44iI1EP9xuzvBFaY2XIz6wTeCNxcuYCZnQn8HfBqd9+ap3k1j+zN7GjgH4CjKP9IudrdrzKzfuCfgeMol4J9g7vvTG5r3wE61jxaNTa86sRkO4b645Kk+08+KYzNf+RAGCu1p3PTPfFVmMylr/Ht3pG4LoCurni93fGXd6kr/VbuO+mIMNa5Oy5r6zVeo469cVnhrk17w1hpVvx+FvbH7QEYOqp6OWyvUS54KtRKmWtAe54ABoAiMDrVKY9mdh1wEbDV3U/LHjvkvmMS23IF8E7K6YsAH5j4JPP1qY3j7qNm9m7gO5Q/R9e5+wNm9hFgjbvfDPwfoA/4spU/90+6+6tT281zZD8K/JG7nwy8AHhXlgb0J8D33H0F8L3svsiMlTNlrhFe6u6rGpTbfj1w/rjHGtV3VGsLwKey12fVxDv6TJ0mL3H3W9x9pbuf4O4fzx77UNbR4+7nufuiinYnO3rI0dm7+2Z3/6/s/wPAQ5TPDF8M3JAtdgPwmprPQKS11UyZm2nc/XZgx7iHG9J3BG2p4w6aew7aQxqzN7PjgDOBHwOL3H0zlL8QgCPr3TiRaeZQU+amggPfNbO7zOyyBrdlTLP1He82s3vN7Dozi2uS5NHE0xLm7uzNrA/4KvAed99zCOtdZmZrzGzNsMfj5yItIHfK3BQ6x93Pojy09C4z+5UGt6fZfBY4AVgFbAY+eVhbq88J2kmRq7M3sw7KHf0/ufvXsoe3jE3OnP1b9Yywu1/t7qvdfXWnpeczFZnmcqXMTSV335T9uxX4OuWhpkbL1XdMBXff4u5Fdy8B13CYr4+VSrlujVCzs7fyqd7PAQ+5+19WhG4G3pb9/23AN+rfPJFppWbK3FQys14zmz32f+DlwP2Nak+Fpuk7xr50Mq/lcF4fp5yvmOfWAHkuqjoHeAtwn5ndnT32AeBK4EYzewfwJPD6WhvaU9q+7bt7/r6yMtuzpUVvP4RWT57mLr26oXENyTTH63PfQfcq2zSxmsp1EqXMNbBJi4CvZ6l57cAX3P3bU9kAM/sicC7lK0Y3AJczgb5jEttyrpmtotxVPwH89oS3T65SCA1j3sDGtXKp03pQe2prxjbJzDS3d4m/4OR858C/e9eH75rqz63KJYiI1EsTH9mrsxcRqYexMfsm1ejOvqmq36H21NJs7YHmbJPMUI3KtMmjoZ19S5c6rQO1p7ZmbJPMVI27YCqPRh/Zi4i0BkedvYjIjNC8oziNqWdvZueb2cNm9piZNbxappk9YWb3mdnd42aPmco2XGdmW83s/orH+s3sVjN7NPv38Op2HH57rjCzjdnrdLeZXTiF7TnazH5gZg+Z2QNm9j+zxxv2GomMV6dpCSfFlHf2KgMbup7mKQUbtQfqWQ720KjUtjS/ViiEVkcqA1tFM5WCTbSnYVRqW5qeOxRL+W4N0IjOXmVg82u2UrBQz3KwE6RS29K0dGR/EJWBnb7qWw52AiZaaltkSqizP4jKwObXNKVgof7lYA/V4ZTaFpl0DpQ8360BGtHZqwxsfk1TChbqXA720PetUtvS5By8lO/WAFOeZ68ysNU1UynYRHvqVg52AupWaltkUjgNO/maR0NLHIuItIq5nYv8hYvemGvZb2/4tEoci4hMW0188KzOXkSkLlQITUSk9TmgEsciIjOAjuxFRFqdN3U2jjp7EZF6cPAG5dDnoc5eRKReGnR1bB7q7EVE6kVj9iIiLc5d2TgiIjOCjuxFRFqd48VioxsRUmcvIlIPYyWOm5Q6exGRelHqpYhIa3PAdWQvItLi3HVkLyIyEzTzCVpNXiIiUgdm9m1gYc7Ft7n7+ZPZnvHU2YuIzACNmHBcRESmmDp7EZEZQJ29iMgMoM5eRGQGUGcvIjID/H/hWKBjzsepkwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def occlusion_test(input_stamp, model, kernel_size=5):\n",
+    "    i = 0\n",
+    "    j=0\n",
+    "    heatmap = []\n",
+    "    sigmaG_coeff = .7413\n",
+    "    for j in range(22-kernel_size):\n",
+    "        for i in range(22-kernel_size):\n",
+    "            img = np.copy(input_stamp)\n",
+    "            img[i:i+kernel_size,j:j+kernel_size] = 0\n",
+    "            per25,per50,per75 = np.percentile(img,[25,50,75])\n",
+    "            sigmaG = sigmaG_coeff * (per75 - per25)\n",
+    "            img[img<(per50-2*sigmaG)] = per50-2*sigmaG\n",
+    "            img -= np.min(img)\n",
+    "            img /= np.sum(img)\n",
+    "            keras_stamps = np.reshape(img,[-1,21,21,1])\n",
+    "            probs = np.concatenate(model.predict(keras_stamps))\n",
+    "            heatmap.append(probs[1])\n",
+    "    fig = plt.figure()\n",
+    "    unmasked_prob = model.predict(np.reshape(input_stamp,[-1,21,21,1]))[0][1]\n",
+    "    ax1 = fig.add_subplot(121)\n",
+    "    ax1.set_title('Predicted Prob: {:.2f}'.format(unmasked_prob))\n",
+    "    ax1.imshow(input_stamp)\n",
+    "    ax2 = fig.add_subplot(122)\n",
+    "    im = ax2.imshow(np.array(heatmap).reshape(22-kernel_size,22-kernel_size))\n",
+    "    fig.colorbar(im, ax=ax2)\n",
+    "occlusion_test(real_positives[4].reshape(21,21), resnet50_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Save the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#vgg_model.save('../data_files/vgg_masks.h5')\n",
+    "#resnet50_model.save('../data_files/resnet_2.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyze model results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "resnet50_model = tf.keras.models.load_model('../data_files/resnet_2.h5')\n",
+    "#test_model.predict(test_data)\n",
+    "#real_data_probs = test_model.predict(real_positives.reshape(-1,21,21,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x864 with 15 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "real_positives = real_positives.reshape(-1,21,21,1)\n",
+    "real_data_probs = resnet50_model.predict(real_positives)\n",
+    "fig, ax = plt.subplots(nrows=3,ncols=5,figsize=[16,12])\n",
+    "ax = ax.reshape(-1)\n",
+    "[tmp_ax.set_axis_off() for tmp_ax in ax]\n",
+    "for i,coadd in enumerate(real_positives):\n",
+    "    ax[i].set_axis_on()\n",
+    "    ax[i].imshow(coadd.reshape(21,21))\n",
+    "    ax[i].set_title('Predicted Prob: {:.2f}'.format(real_data_probs[i,1]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Accuracy')"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_history = resnet_model_history\n",
+    "import matplotlib.pyplot as plt\n",
+    "loss = model_history.history['loss']\n",
+    "val_loss = model_history.history['val_loss']\n",
+    "acc = model_history.history['acc']\n",
+    "val_acc = model_history.history['val_acc']\n",
+    "plt.figure()\n",
+    "plt.plot(np.arange(n_epochs), loss, label='Training Loss')\n",
+    "plt.plot(np.arange(n_epochs), val_loss, label='Validation Loss')\n",
+    "plt.title('Loss Curves')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "#plt.savefig('Keras_Loss.png')\n",
+    "plt.figure()\n",
+    "plt.plot(np.arange(n_epochs), acc, label='Training Accuracy')\n",
+    "plt.plot(np.arange(n_epochs), val_acc, label='Validation Accuracy')\n",
+    "plt.title('Accuracy Curves')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "#plt.savefig('figures/ResNet50_LC.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Stamp Counts')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "false_positives_classes = resnet50_model.predict(real_false_positives.reshape([-1,21,21,1]))\n",
+    "plt.hist(false_positives_classes[:,1])\n",
+    "plt.title('Final Test Probabilities')\n",
+    "plt.xlabel('Probability of Real Object')\n",
+    "plt.ylabel('Stamp Counts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05001601122310496"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo = false_positives_classes[:,1]\n",
+    "len(foo[foo>0.8])/len(foo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Stamp Counts')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classes = resnet50_model.predict(test_data)\n",
+    "plt.hist(classes[:,1])\n",
+    "plt.title('Final Test Probabilities')\n",
+    "plt.xlabel('Probability of Real Object')\n",
+    "plt.ylabel('Stamp Counts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 16))\n",
+    "for i in range(16):\n",
+    "    fig.add_subplot(4,4,i+1)\n",
+    "    plt.imshow(test_data[i].reshape(21,21))\n",
+    "    plt.title('True Class: %i, Predicted Class: %i' % (np.argmax(test_class[i]), np.argmax(classes[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpr = []\n",
+    "tpr = []\n",
+    "for cutoff in np.arange(0.01, 1.0, 0.01):\n",
+    "    correct_false = []\n",
+    "    correct_true = []\n",
+    "    false_positive = []\n",
+    "    false_negative = []\n",
+    "    index = 0\n",
+    "    for true_class, result_prob in zip(test_class, classes):\n",
+    "        if np.argmax(true_class) == 0.:\n",
+    "            if result_prob[1] < cutoff:\n",
+    "                correct_false.append(index)\n",
+    "            else:\n",
+    "                false_positive.append(index)\n",
+    "        else:\n",
+    "            if result_prob[1] < cutoff:\n",
+    "                false_negative.append(index)\n",
+    "            else:\n",
+    "                correct_true.append(index)\n",
+    "        index+=1\n",
+    "    fpr.append(len(false_positive)/len(np.where(test_class[:,1] == 0)[0]))\n",
+    "    tpr.append(len(correct_true)/len(np.where(test_class[:,1] == 1)[0]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,6))\n",
+    "plt.scatter(fpr, tpr, c=np.arange(0.01, 1.0, 0.01))\n",
+    "cbar = plt.colorbar()\n",
+    "plt.xlabel('False Positive Rate', size=18)\n",
+    "plt.ylabel('True Positive Rate', size=18)\n",
+    "cbar.set_label('Probability Cutoff', size=18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 0)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "correct_false = []\n",
+    "correct_true = []\n",
+    "false_positive = []\n",
+    "false_negative = []\n",
+    "index = 0\n",
+    "cutoff = 0.5\n",
+    "for true_class, result_prob in zip(test_class, classes):\n",
+    "    if np.argmax(true_class) == 0.:\n",
+    "        if result_prob[1] < cutoff:\n",
+    "            correct_false.append(index)\n",
+    "        else:\n",
+    "            false_positive.append(index)\n",
+    "    else:\n",
+    "        if result_prob[1] < cutoff:\n",
+    "            false_negative.append(index)\n",
+    "        else:\n",
+    "            correct_true.append(index)\n",
+    "    index+=1\n",
+    "keras_results = np.array([[len(correct_true), len(false_positive)], [len(false_negative), len(correct_false)]])\n",
+    "fig = plt.figure(figsize=(10,8))\n",
+    "sns.heatmap(keras_results, annot=True, annot_kws={'size':24}, cmap=plt.get_cmap('cividis'), fmt='g')\n",
+    "plt.xticks([0.5, 1.5], ['Real True', 'Real False'], size=24)\n",
+    "plt.yticks([0.5, 1.5], ['Predicted True', 'Predicted False'], size=24, va='center')\n",
+    "plt.ylim(2, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.961\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((len(correct_true)+len(correct_false))/np.sum(keras_results))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3800\n",
+      "194\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "cannot reshape array of size 441 into shape (25,21,21)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-30-bdfbbf9310bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0max_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstamp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mplot_stamps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfalse_negative\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\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;32m<ipython-input-30-bdfbbf9310bc>\u001b[0m in \u001b[0;36mplot_stamps\u001b[0;34m(stamps)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_stamps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstamps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mfig_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0max_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\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;32m----> 5\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstamp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstamps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 441 into shape (25,21,21)"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(len(correct_true))\n",
+    "print(len(false_negative))\n",
+    "def plot_stamps(stamps):\n",
+    "    fig_1,ax_1 = plt.subplots(nrows=5,ncols=5,figsize=[8,8])\n",
+    "    for i,stamp in enumerate(stamps.reshape(25,21,21)):\n",
+    "        j = i%5\n",
+    "        k = int(i/5)\n",
+    "        #print(np.shape(stamp),i)\n",
+    "        ax_1[j][k].imshow(stamp.reshape(21,21))\n",
+    "        \n",
+    "plot_stamps(test_data[false_negative[6]])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/epyc/opt/anaconda-2019/lib/python3.7/site-packages/matplotlib/text.py:1150: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if s != self._text:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x864 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 12))\n",
+    "for i in range(16):\n",
+    "    try:\n",
+    "        fig.add_subplot(4, 4, i+1)\n",
+    "        plt.imshow(test_data[false_negative[i]].reshape(21,21))\n",
+    "        plt.title(test_class[false_negative[i]])\n",
+    "    except:\n",
+    "        continue\n",
+    "plt.suptitle('False Negatives', size=18)\n",
+    "plt.subplots_adjust(top=0.95)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x864 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 12))\n",
+    "for i in r../data_files/16):\n",
+    "    try:\n",
+    "        fig.add_subplot(4, 4, i+1)\n",
+    "        plt.imshow(test_data[false_positive[i]].reshape(21,21))\n",
+    "        plt.title(test_class[false_positive[i]])\n",
+    "    except:\n",
+    "        continue\n",
+    "plt.suptitle('False Positives', size=18)\n",
+    "plt.subplots_adjust(top=0.95)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kbmod",
+   "language": "python",
+   "name": "kbmod"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/example_config.toml b/example_config.toml
index 768ef00..ffc332f 100644
--- a/example_config.toml
+++ b/example_config.toml
@@ -16,19 +16,22 @@ log_destination = "stderr"
 log_level = "info"     # Emit informational messages, warnings and all errors
 # log_level = "debug"    # Very verbose, emit all log messages.
 
+data_dir = "/home/drew/code/fibad/data/"
+results_dir = "./results" # Results get named <verb>-<timestamp> under this directory
+
 [download]
 sw = "22asec"
 sh = "22asec"
 filter = ["HSC-G", "HSC-R", "HSC-I", "HSC-Z", "HSC-Y"]
 type = "coadd"
 rerun = "pdr3_wide"
-username = "mtauraso@local"
-password = "cCw+nX53lmNLHMy+JbizpH/dl4t7sxljiNm6a7k1"
-max_connections = 2
-fits_file = "../hscplay/temp.fits"
-cutout_dir = "../hscplay/cutouts/"
+username = false
+password = false
+num_sources = -1 # Values below 1 here indicate all sources in the catalog will be downloaded
 offset = 0
-num_sources = 500
+concurrent_connections = 4
+stats_print_interval = 60
+fits_file = "./catalog.fits"
 
 # These control the downloader's HTTP requests and retries
 # `retry_wait` How long to wait before retrying a failed HTTP request in seconds. Default 30s
@@ -38,24 +41,47 @@ retries = 3
 # `timepout` How long should we wait to get a full HTTP response from the server. Default 3600s (1hr)
 timeout = 3600
 # `chunksize` How many sky location rectangles should we request in a single request. Default is 990
-chunksize = 990
+chunk_size = 990
+
+# Whether to retrieve the image layer
+image = true
+# Whether to retrieve the variance layer
+variance = false
+# Whether to retrieve the mask layer
+mask = false
 
 [model]
 # The name of the built-in model to use or the libpath to an external model
 # e.g. "user_package.submodule.ExternalModel" or "ExampleAutoencoder"
-name = "kbmod_ml.models.cnn.CNN"
+name = "kbmod_ml.models.resnet50.RESNET50"
 
-weights_filepath = "example_model.pth"
+weights_filepath = "resnet50.pth"
 epochs = 10
 
-[data_loader]
+num_classes = 10
+
+
+[data_set]
 # Name of the built-in data loader to use or the libpath to an external data loader
 # e.g. "user_package.submodule.ExternalDataLoader" or "HSCDataLoader"
 name = "CifarDataLoader"
-# name = "HSCDataLoader"
 
-# Directory path where the data is stored
-path = "/home/drew/code/fibad/data/"
+[data_loader]
+# Pixel dimensions used to crop all images prior to loading. Will prune any images that are too small.
+#
+# If not provided by user, the default of 'false' scans the directory for the smallest dimensioned files, and 
+# uses those pixel dimensions as the crop size.
+#
+#crop_to = [100,100]
+crop_to = false
+
+# Limit data loader to only particular filters when there are more in the data set.
+#
+# When not provided by the user, the number of filters will be automatically gleaned from the data set.
+# Defaults behavior is produced by the false value.
+#
+#filters = ["HSC-G", "HSC-R", "HSC-I", "HSC-Z", "HSC-Y"]
+filters = false
 
 # Default PyTorch DataLoader parameters
 batch_size = 10
@@ -63,4 +89,5 @@ shuffle = true
 num_workers = 10
 
 [predict]
+model_weights_file = false
 batch_size = 32
diff --git a/src/kbmod_ml/models/resnet50.py b/src/kbmod_ml/models/resnet50.py
new file mode 100644
index 0000000..83ff146
--- /dev/null
+++ b/src/kbmod_ml/models/resnet50.py
@@ -0,0 +1,65 @@
+# ruff: noqa: D101, D102
+
+# This example model is taken from the PyTorch CIFAR10 tutorial:
+# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a-convolutional-neural-network
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F  # noqa N812
+import torch.optim as optim
+from fibad.models.model_registry import fibad_model
+from torchvision.models import resnet50
+
+logger = logging.getLogger(__name__)
+
+
+@fibad_model
+class RESNET50(nn.Module):
+    def __init__(self, model_config, shape):
+        logger.info("This is an external model, not in FIBAD!!!")
+        super().__init__()
+
+        self.config = model_config
+
+        self.model = resnet50(pretrained=False, num_classes=self.config["model"]["num_classes"])
+
+        # Optimizer and criterion could be set directly, i.e. `self.optimizer = optim.SGD(...)`
+        # but we define them as methods as a way to allow for more flexibility in the future.
+        self.optimizer = self._optimizer()
+        self.criterion = self._criterion()
+
+    def forward(self, x):
+        return self.model(x)
+
+    def train_step(self, batch):
+        """This function contains the logic for a single training step. i.e. the
+        contents of the inner loop of a ML training process.
+
+        Parameters
+        ----------
+        batch : tuple
+            A tuple containing the inputs and labels for the current batch.
+
+        Returns
+        -------
+        Current loss value
+            The loss value for the current batch.
+        """
+        inputs, labels = batch
+
+        self.optimizer.zero_grad()
+        outputs = self(inputs)
+        loss = self.criterion(outputs, labels)
+        loss.backward()
+        self.optimizer.step()
+        return {"loss": loss.item()}
+
+    def _criterion(self):
+        return nn.CrossEntropyLoss()
+
+    def _optimizer(self):
+        return optim.SGD(self.parameters(), lr=0.001, momentum=0.9)
+
+    def save(self):
+        torch.save(self.state_dict(), self.config.get("weights_filepath"))