diff --git a/examples/2D/denoising2D_BSD68/BSD68_reproducibility.ipynb b/examples/2D/denoising2D_BSD68/BSD68_reproducibility.ipynb
new file mode 100644
index 0000000..f010785
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+++ b/examples/2D/denoising2D_BSD68/BSD68_reproducibility.ipynb
@@ -0,0 +1,925 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Noise2Void - 2D Example for BSD68 Data\n",
+    "\n",
+    "The data used in this notebook is the same as presented in the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We import all our dependencies.\n",
+    "from n2v.models import N2VConfig, N2V\n",
+    "import numpy as np\n",
+    "from csbdeep.utils import plot_history\n",
+    "from n2v.utils.n2v_utils import manipulate_val_data\n",
+    "from n2v.internals.N2V_DataGenerator import N2V_DataGenerator\n",
+    "from matplotlib import pyplot as plt\n",
+    "import urllib\n",
+    "import os\n",
+    "import zipfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training Data Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a folder for our data\n",
+    "if not os.path.isdir('./data'):\n",
+    "    os.mkdir('data')\n",
+    "\n",
+    "# check if data has been downloaded already\n",
+    "zipPath=\"data/BSD68_reproducibility.zip\"\n",
+    "if not os.path.exists(zipPath):\n",
+    "    #download and unzip data\n",
+    "    data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/pbj89sV6n6SyM29/download', zipPath)\n",
+    "    with zipfile.ZipFile(zipPath, 'r') as zip_ref:\n",
+    "        zip_ref.extractall(\"data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3168, 180, 180, 1)\n",
+      "(4, 180, 180, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = np.load('data/BSD68_reproducibility_data/train/DCNN400_train_gaussian25.npy')\n",
+    "X_val = np.load('data/BSD68_reproducibility_data/val/DCNN400_validation_gaussian25.npy')\n",
+    "\n",
+    "# Adding channel dimension\n",
+    "X = X[..., np.newaxis]\n",
+    "print(X.shape)\n",
+    "X_val = X_val[..., np.newaxis]\n",
+    "print(X_val.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's look at one of our training and validation patches.\n",
+    "plt.figure(figsize=(14,7))\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.imshow(X[0,...,0], cmap='gray')\n",
+    "plt.title('Training Patch');\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.imshow(X_val[0,...,0], cmap='gray')\n",
+    "plt.title('Validation Patch');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Configure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'means': ['110.72957232412905'],\n",
+       " 'stds': ['63.656060106500874'],\n",
+       " 'n_dim': 2,\n",
+       " 'axes': 'YXC',\n",
+       " 'n_channel_in': 1,\n",
+       " 'n_channel_out': 1,\n",
+       " 'unet_residual': True,\n",
+       " 'unet_n_depth': 2,\n",
+       " 'unet_kern_size': 3,\n",
+       " 'unet_n_first': 96,\n",
+       " 'unet_last_activation': 'linear',\n",
+       " 'unet_input_shape': (None, None, 1),\n",
+       " 'train_loss': 'mse',\n",
+       " 'train_epochs': 200,\n",
+       " 'train_steps_per_epoch': 400,\n",
+       " 'train_learning_rate': 0.0004,\n",
+       " 'train_batch_size': 128,\n",
+       " 'train_tensorboard': True,\n",
+       " 'train_checkpoint': 'weights_best.h5',\n",
+       " 'train_reduce_lr': {'factor': 0.5, 'patience': 10},\n",
+       " 'batch_norm': True,\n",
+       " 'n2v_perc_pix': 0.198,\n",
+       " 'n2v_patch_shape': (64, 64),\n",
+       " 'n2v_manipulator': 'uniform_withCP',\n",
+       " 'n2v_neighborhood_radius': 2,\n",
+       " 'probabilistic': False}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "config = N2VConfig(X, unet_kern_size=3, \n",
+    "                   train_steps_per_epoch=400, train_epochs=200, train_loss='mse', batch_norm=True, \n",
+    "                   train_batch_size=128, n2v_perc_pix=0.198, n2v_patch_shape=(64, 64), \n",
+    "                   unet_n_first = 96,\n",
+    "                   unet_residual = True,\n",
+    "                   n2v_manipulator='uniform_withCP', n2v_neighborhood_radius=2)\n",
+    "\n",
+    "# Let's look at the parameters stored in the config-object.\n",
+    "vars(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# a name used to identify the model\n",
+    "model_name = 'BSD68_reproducability_5x5'\n",
+    "# the base directory in which our model will live\n",
+    "basedir = 'models'\n",
+    "# We are now creating our network model.\n",
+    "model = N2V(config, model_name, basedir=basedir)\n",
+    "model.prepare_for_training(metrics=())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training\n",
+    "\n",
+    "Training the model will likely take some time. We recommend to monitor the progress with TensorBoard, which allows you to inspect the losses during training. Furthermore, you can look at the predictions for some of the validation images, which can be helpful to recognize problems early on.\n",
+    "\n",
+    "You can start TensorBoard in a terminal from the current working directory with tensorboard --logdir=. Then connect to http://localhost:6006/ with your browser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/tbuchhol/Gitrepos/n2v/n2v/models/n2v_standard.py:173: UserWarning: small number of validation images (only 0.1% of all images)\n",
+      "  warnings.warn(\"small number of validation images (only %.1f%% of all images)\" % (100*frac_val))\n",
+      "Preparing validation data: 100%|██████████| 4/4 [00:00<00:00, 437.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 blind-spots will be generated per training patch of size (64, 64).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:\n",
+      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+      "For more information, please see:\n",
+      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+      "  * https://github.com/tensorflow/addons\n",
+      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+      "If you depend on functionality not listed there, please file an issue.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/csbdeep/utils/tf.py:240: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/csbdeep/utils/tf.py:268: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/tbuchhol/Programs/miniconda3/envs/n2v_pr_checks/lib/python3.6/site-packages/csbdeep/utils/tf.py:275: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
+      "\n",
+      "Epoch 1/200\n",
+      "400/400 [==============================] - 143s 357ms/step - loss: 0.2396 - val_loss: 0.2007\n",
+      "Epoch 2/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.2077 - val_loss: 0.1971\n",
+      "Epoch 3/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2057 - val_loss: 0.1978\n",
+      "Epoch 4/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2037 - val_loss: 0.2070\n",
+      "Epoch 5/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2022 - val_loss: 0.2158\n",
+      "Epoch 6/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2007 - val_loss: 0.2035\n",
+      "Epoch 7/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2004 - val_loss: 0.2033\n",
+      "Epoch 8/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2024 - val_loss: 0.1949\n",
+      "Epoch 9/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.2010 - val_loss: 0.1955\n",
+      "Epoch 10/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1998 - val_loss: 0.1960\n",
+      "Epoch 11/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1996 - val_loss: 0.2108\n",
+      "Epoch 12/200\n",
+      "400/400 [==============================] - 134s 336ms/step - loss: 0.1987 - val_loss: 0.1950\n",
+      "Epoch 13/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1984 - val_loss: 0.1889\n",
+      "Epoch 14/200\n",
+      "400/400 [==============================] - 135s 336ms/step - loss: 0.1990 - val_loss: 0.1922\n",
+      "Epoch 15/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1983 - val_loss: 0.1920\n",
+      "Epoch 16/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1978 - val_loss: 0.1934\n",
+      "Epoch 17/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1976 - val_loss: 0.2016\n",
+      "Epoch 18/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1974 - val_loss: 0.2002\n",
+      "Epoch 19/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1975 - val_loss: 0.1969\n",
+      "Epoch 20/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1970 - val_loss: 0.1978\n",
+      "Epoch 21/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1973 - val_loss: 0.1888\n",
+      "Epoch 22/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1978 - val_loss: 0.2135\n",
+      "Epoch 23/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1975 - val_loss: 0.1925\n",
+      "\n",
+      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.00019999999494757503.\n",
+      "Epoch 24/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1958 - val_loss: 0.1886\n",
+      "Epoch 25/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1957 - val_loss: 0.1967\n",
+      "Epoch 26/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1952 - val_loss: 0.1951\n",
+      "Epoch 27/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1951 - val_loss: 0.1897\n",
+      "Epoch 28/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1951 - val_loss: 0.1883\n",
+      "Epoch 29/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1954 - val_loss: 0.1913\n",
+      "Epoch 30/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1960 - val_loss: 0.1940\n",
+      "Epoch 31/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1951 - val_loss: 0.1944\n",
+      "Epoch 32/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1951 - val_loss: 0.2015\n",
+      "Epoch 33/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1942 - val_loss: 0.1884\n",
+      "Epoch 34/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1960 - val_loss: 0.1905\n",
+      "Epoch 35/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1957 - val_loss: 0.1890\n",
+      "Epoch 36/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1948 - val_loss: 0.1872\n",
+      "Epoch 37/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1953 - val_loss: 0.1892\n",
+      "Epoch 38/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1956 - val_loss: 0.2382\n",
+      "Epoch 39/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1953 - val_loss: 0.1869\n",
+      "Epoch 40/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1951 - val_loss: 0.1930\n",
+      "Epoch 41/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1949 - val_loss: 0.2207\n",
+      "Epoch 42/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1945 - val_loss: 0.1900\n",
+      "Epoch 43/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1947 - val_loss: 0.1886\n",
+      "Epoch 44/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1946 - val_loss: 0.1918\n",
+      "Epoch 45/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1940 - val_loss: 0.1891\n",
+      "Epoch 46/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1941 - val_loss: 0.1871\n",
+      "Epoch 47/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1945 - val_loss: 0.1927\n",
+      "Epoch 48/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1951 - val_loss: 0.1932\n",
+      "Epoch 49/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1943 - val_loss: 0.1980\n",
+      "\n",
+      "Epoch 00049: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-05.\n",
+      "Epoch 50/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1943 - val_loss: 0.1981\n",
+      "Epoch 51/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1933 - val_loss: 0.1910\n",
+      "Epoch 52/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1938 - val_loss: 0.1906\n",
+      "Epoch 53/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1934 - val_loss: 0.1883\n",
+      "Epoch 54/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1930 - val_loss: 0.1941\n",
+      "Epoch 55/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1937 - val_loss: 0.1894\n",
+      "Epoch 56/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1935 - val_loss: 0.1890\n",
+      "Epoch 57/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1938 - val_loss: 0.1898\n",
+      "Epoch 58/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1943 - val_loss: 0.1873\n",
+      "Epoch 59/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1940 - val_loss: 0.1936\n",
+      "\n",
+      "Epoch 00059: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
+      "Epoch 60/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1947\n",
+      "Epoch 61/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1930 - val_loss: 0.1930\n",
+      "Epoch 62/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1887\n",
+      "Epoch 63/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1930 - val_loss: 0.1958\n",
+      "Epoch 64/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1931 - val_loss: 0.1913\n",
+      "Epoch 65/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1937 - val_loss: 0.1895\n",
+      "Epoch 66/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1932 - val_loss: 0.1883\n",
+      "Epoch 67/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1931 - val_loss: 0.1939\n",
+      "Epoch 68/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1901\n",
+      "Epoch 69/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1894\n",
+      "\n",
+      "Epoch 00069: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
+      "Epoch 70/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1927 - val_loss: 0.1903\n",
+      "Epoch 71/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1934 - val_loss: 0.1932\n",
+      "Epoch 72/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1929 - val_loss: 0.1909\n",
+      "Epoch 73/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1927 - val_loss: 0.1903\n",
+      "Epoch 74/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1930 - val_loss: 0.1900\n",
+      "Epoch 75/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1905\n",
+      "Epoch 76/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1921 - val_loss: 0.1905\n",
+      "Epoch 77/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1926 - val_loss: 0.1895\n",
+      "Epoch 78/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1915\n",
+      "Epoch 79/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1923 - val_loss: 0.1931\n",
+      "\n",
+      "Epoch 00079: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
+      "Epoch 80/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 81/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1904\n",
+      "Epoch 82/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1923 - val_loss: 0.1903\n",
+      "Epoch 83/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1915 - val_loss: 0.1897\n",
+      "Epoch 84/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1901\n",
+      "Epoch 85/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1930 - val_loss: 0.1899\n",
+      "Epoch 86/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1928 - val_loss: 0.1903\n",
+      "Epoch 87/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1901\n",
+      "Epoch 88/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1921\n",
+      "Epoch 89/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1913\n",
+      "\n",
+      "Epoch 00089: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
+      "Epoch 90/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1914 - val_loss: 0.1917\n",
+      "Epoch 91/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1937 - val_loss: 0.1906\n",
+      "Epoch 92/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1899\n",
+      "Epoch 93/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1927 - val_loss: 0.1898\n",
+      "Epoch 94/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1896\n",
+      "Epoch 95/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1900\n",
+      "Epoch 96/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1922 - val_loss: 0.1901\n",
+      "Epoch 97/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1923 - val_loss: 0.1899\n",
+      "Epoch 98/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1910 - val_loss: 0.1903\n",
+      "Epoch 99/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00099: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
+      "Epoch 100/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1910\n",
+      "Epoch 101/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1917 - val_loss: 0.1909\n",
+      "Epoch 102/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1920 - val_loss: 0.1906\n",
+      "Epoch 103/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1915 - val_loss: 0.1910\n",
+      "Epoch 104/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1908\n",
+      "Epoch 105/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1906\n",
+      "Epoch 106/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1902\n",
+      "Epoch 107/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1924 - val_loss: 0.1910\n",
+      "Epoch 108/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1925 - val_loss: 0.1911\n",
+      "Epoch 109/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1900\n",
+      "\n",
+      "Epoch 00109: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n",
+      "Epoch 110/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1908\n",
+      "Epoch 111/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1906\n",
+      "Epoch 112/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1908\n",
+      "Epoch 113/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1905\n",
+      "Epoch 114/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1927 - val_loss: 0.1906\n",
+      "Epoch 115/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1921 - val_loss: 0.1906\n",
+      "Epoch 116/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1904\n",
+      "Epoch 117/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1906\n",
+      "Epoch 118/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1906\n",
+      "Epoch 119/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1913 - val_loss: 0.1910\n",
+      "\n",
+      "Epoch 00119: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-07.\n",
+      "Epoch 120/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1906\n",
+      "Epoch 121/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1927 - val_loss: 0.1905\n",
+      "Epoch 122/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 123/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1929 - val_loss: 0.1908\n",
+      "Epoch 124/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1915 - val_loss: 0.1907\n",
+      "Epoch 125/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1906\n",
+      "Epoch 126/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1905\n",
+      "Epoch 127/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1916 - val_loss: 0.1907\n",
+      "Epoch 128/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 129/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1922 - val_loss: 0.1908\n",
+      "\n",
+      "Epoch 00129: ReduceLROnPlateau reducing learning rate to 3.906249901319825e-07.\n",
+      "Epoch 130/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1925 - val_loss: 0.1908\n",
+      "Epoch 131/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1932 - val_loss: 0.1907\n",
+      "Epoch 132/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1914 - val_loss: 0.1907\n",
+      "Epoch 133/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1929 - val_loss: 0.1908\n",
+      "Epoch 134/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1907\n",
+      "Epoch 135/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1908\n",
+      "Epoch 136/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1907\n",
+      "Epoch 137/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1906\n",
+      "Epoch 138/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1924 - val_loss: 0.1906\n",
+      "Epoch 139/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1920 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00139: ReduceLROnPlateau reducing learning rate to 1.9531249506599124e-07.\n",
+      "Epoch 140/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1929 - val_loss: 0.1907\n",
+      "Epoch 141/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1907\n",
+      "Epoch 142/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 143/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 144/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 145/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1920 - val_loss: 0.1907\n",
+      "Epoch 146/200\n",
+      "400/400 [==============================] - 135s 337ms/step - loss: 0.1923 - val_loss: 0.1907\n",
+      "Epoch 147/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1906\n",
+      "Epoch 148/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "Epoch 149/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00149: ReduceLROnPlateau reducing learning rate to 9.765624753299562e-08.\n",
+      "Epoch 150/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 151/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1918 - val_loss: 0.1907\n",
+      "Epoch 152/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "Epoch 153/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "Epoch 154/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1918 - val_loss: 0.1907\n",
+      "Epoch 155/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 156/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1923 - val_loss: 0.1907\n",
+      "Epoch 157/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1918 - val_loss: 0.1907\n",
+      "Epoch 158/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "Epoch 159/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1924 - val_loss: 0.1908\n",
+      "\n",
+      "Epoch 00159: ReduceLROnPlateau reducing learning rate to 4.882812376649781e-08.\n",
+      "Epoch 160/200\n",
+      "400/400 [==============================] - 136s 340ms/step - loss: 0.1923 - val_loss: 0.1908\n",
+      "Epoch 161/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1929 - val_loss: 0.1907\n",
+      "Epoch 162/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1922 - val_loss: 0.1907\n",
+      "Epoch 163/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1922 - val_loss: 0.1907\n",
+      "Epoch 164/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "Epoch 165/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1907\n",
+      "Epoch 166/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1927 - val_loss: 0.1907\n",
+      "Epoch 167/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1932 - val_loss: 0.1908\n",
+      "Epoch 168/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1922 - val_loss: 0.1908\n",
+      "Epoch 169/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00169: ReduceLROnPlateau reducing learning rate to 2.4414061883248905e-08.\n",
+      "Epoch 170/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1920 - val_loss: 0.1907\n",
+      "Epoch 171/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "Epoch 172/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 173/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "Epoch 174/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "Epoch 175/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1920 - val_loss: 0.1908\n",
+      "Epoch 176/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1929 - val_loss: 0.1907\n",
+      "Epoch 177/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1917 - val_loss: 0.1907\n",
+      "Epoch 178/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 179/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1932 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00179: ReduceLROnPlateau reducing learning rate to 1.2207030941624453e-08.\n",
+      "Epoch 180/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1922 - val_loss: 0.1907\n",
+      "Epoch 181/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1927 - val_loss: 0.1907\n",
+      "Epoch 182/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 183/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "Epoch 184/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "Epoch 185/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1928 - val_loss: 0.1908\n",
+      "Epoch 186/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1921 - val_loss: 0.1907\n",
+      "Epoch 187/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1924 - val_loss: 0.1907\n",
+      "Epoch 188/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1927 - val_loss: 0.1907\n",
+      "Epoch 189/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1931 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00189: ReduceLROnPlateau reducing learning rate to 6.103515470812226e-09.\n",
+      "Epoch 190/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1930 - val_loss: 0.1907\n",
+      "Epoch 191/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 192/200\n",
+      "400/400 [==============================] - 135s 339ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 193/200\n",
+      "400/400 [==============================] - 136s 339ms/step - loss: 0.1925 - val_loss: 0.1907\n",
+      "Epoch 194/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1907\n",
+      "Epoch 195/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1908\n",
+      "Epoch 196/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1919 - val_loss: 0.1907\n",
+      "Epoch 197/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1907\n",
+      "Epoch 198/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1907\n",
+      "Epoch 199/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1923 - val_loss: 0.1907\n",
+      "\n",
+      "Epoch 00199: ReduceLROnPlateau reducing learning rate to 3.051757735406113e-09.\n",
+      "Epoch 200/200\n",
+      "400/400 [==============================] - 135s 338ms/step - loss: 0.1926 - val_loss: 0.1907\n",
+      "\n",
+      "Loading network weights from 'weights_best.h5'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We are ready to start training now.\n",
+    "history = model.train(X, X_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After training, lets plot training and validation loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['loss', 'lr', 'val_loss']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(sorted(list(history.history.keys())))\n",
+    "plt.figure(figsize=(16,5))\n",
+    "plot_history(history,['loss','val_loss']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compute PSNR to GT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "groundtruth_data = np.load('data/BSD68_reproducibility_data/test/bsd68_groundtruth.npy', allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = np.load('data/BSD68_reproducibility_data/test/bsd68_gaussian25.npy', allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def PSNR(gt, img):\n",
+    "    mse = np.mean(np.square(gt - img))\n",
+    "    return 20 * np.log10(255) - 10 * np.log10(mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Weights corresponding to the smallest validation loss\n",
+    "# Smallest validation loss does not necessarily correspond to best performance, \n",
+    "# because the loss is computed to noisy target pixels.\n",
+    "model.load_weights('weights_best.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "pred = []\n",
+    "psnrs = []\n",
+    "for gt, img in zip(groundtruth_data, test_data):\n",
+    "    p_ = model.predict(img.astype(np.float32), 'YX');\n",
+    "    pred.append(p_)\n",
+    "    psnrs.append(PSNR(gt, p_))\n",
+    "\n",
+    "psnrs = np.array(psnrs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PSNR: 27.28\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"PSNR:\", np.round(np.mean(psnrs), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The weights of the converged network. \n",
+    "model.load_weights('weights_last.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = []\n",
+    "psnrs = []\n",
+    "for gt, img in zip(groundtruth_data, test_data):\n",
+    "    p_ = model.predict(img.astype(np.float32), 'YX')\n",
+    "    pred.append(p_)\n",
+    "    psnrs.append(PSNR(gt, p_))\n",
+    "\n",
+    "psnrs = np.array(psnrs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PSNR: 27.71\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"PSNR:\", np.round(np.mean(psnrs), 2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:n2v_pr_checks] *",
+   "language": "python",
+   "name": "conda-env-n2v_pr_checks-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}