image-denoising.ipynb

{
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   "source": [
    "# Required Imports\n",
    "\n",
    "**Imports:**\n",
    "* numpy\n",
    "* Matplotlib - for visualization \n",
    "* Keras - Building and Training CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from keras.layers import Conv2D, Input, Dense, Dropout, MaxPool2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras.datasets import mnist, cifar10\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "start_time": "2021-12-09T14:14:05.656963",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Import dataset and Nomalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(train, _), (test, _) = mnist.load_data()\n",
    "\n",
    "# scaling input data\n",
    "train = train.reshape([-1,28,28,1]) / 255\n",
    "test = test.reshape([-1,28,28,1]) / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012074,
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     "exception": false,
     "start_time": "2021-12-09T14:14:06.332236",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Construct noisy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:14:06.374550Z",
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     "start_time": "2021-12-09T14:14:06.356455",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adding noise to data\n",
    "noise = 0.3\n",
    "train_noise = train + noise * np.random.normal(0, 1, size=train.shape)\n",
    "test_noise = test + noise * np.random.normal(0, 1, size=test.shape)\n",
    "\n",
    "train_noise = np.clip(train_noise, 0, 1)\n",
    "test_noise = np.clip(test_noise, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample noisy image\n",
    "\n",
    "rows = 5 # defining no. of rows in figure\n",
    "cols = 6 # defining no. of colums in figure\n",
    "subplot_size = 2\n",
    "\n",
    "f = plt.figure(figsize=(subplot_size*cols,subplot_size*rows)) # defining a figure \n",
    "\n",
    "for i in range(rows*cols): \n",
    "    f.add_subplot(rows,cols,i+1) # adding sub plot to figure on each iteration\n",
    "    plt.imshow(train_noise[i].reshape([28,28]),cmap=\"Reds\") \n",
    "    plt.axis(\"off\")\n",
    "plt.savefig(\"digits_noise.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:14:10.474774Z",
     "iopub.status.busy": "2021-12-09T14:14:10.474233Z",
     "iopub.status.idle": "2021-12-09T14:14:11.569856Z",
     "shell.execute_reply": "2021-12-09T14:14:11.570326Z",
     "shell.execute_reply.started": "2021-12-09T13:25:02.649633Z"
    },
    "papermill": {
     "duration": 1.121058,
     "end_time": "2021-12-09T14:14:11.570464",
     "exception": false,
     "start_time": "2021-12-09T14:14:10.449406",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "rows = 5 # defining no. of rows in figure\n",
    "cols = 6 # defining no. of colums in figure\n",
    "subplot_size = 2\n",
    "f = plt.figure(figsize=(subplot_size*cols, subplot_size*rows)) # defining a figure \n",
    "\n",
    "for i in range(rows*cols): \n",
    "    f.add_subplot(rows,cols,i+1) # adding sub plot to figure on each iteration\n",
    "    plt.imshow(train[i].reshape([28,28]),cmap=\"Reds\") \n",
    "    plt.axis(\"off\")\n",
    "plt.savefig(\"digits_original.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016798,
     "end_time": "2021-12-09T14:14:11.605318",
     "exception": false,
     "start_time": "2021-12-09T14:14:11.588520",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Define my model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:14:11.645693Z",
     "iopub.status.busy": "2021-12-09T14:14:11.645125Z",
     "iopub.status.idle": "2021-12-09T14:14:14.425500Z",
     "shell.execute_reply": "2021-12-09T14:14:14.424668Z",
     "shell.execute_reply.started": "2021-12-09T13:25:22.82238Z"
    },
    "papermill": {
     "duration": 2.803406,
     "end_time": "2021-12-09T14:14:14.425645",
     "exception": false,
     "start_time": "2021-12-09T14:14:11.622239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "inputs = Input(shape=(28,28,1))\n",
    "\n",
    "x = Conv2D(32, 3, activation='relu', padding='same')(inputs)\n",
    "x = MaxPool2D()(x)\n",
    "x = Dropout(0.3)(x)\n",
    "x = Conv2D(32, 3, activation='relu', padding='same')(x)\n",
    "encoded = MaxPool2D()(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:14:14.466180Z",
     "iopub.status.busy": "2021-12-09T14:14:14.465341Z",
     "iopub.status.idle": "2021-12-09T14:14:14.513247Z",
     "shell.execute_reply": "2021-12-09T14:14:14.512290Z",
     "shell.execute_reply.started": "2021-12-09T13:25:27.474803Z"
    },
    "papermill": {
     "duration": 0.071251,
     "end_time": "2021-12-09T14:14:14.513341",
     "exception": false,
     "start_time": "2021-12-09T14:14:14.442090",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = Conv2D(32, 3, activation='relu', padding='same')(encoded)\n",
    "x = UpSampling2D()(x)\n",
    "x = Dropout(0.3)(x)\n",
    "x = Conv2D(32, 3, activation='relu', padding='same')(x)\n",
    "x = UpSampling2D()(x)\n",
    "decoded = Conv2D(1, 3, activation='sigmoid', padding='same')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:14:14.558811Z",
     "iopub.status.busy": "2021-12-09T14:14:14.557967Z",
     "iopub.status.idle": "2021-12-09T14:14:14.570432Z",
     "shell.execute_reply": "2021-12-09T14:14:14.569339Z",
     "shell.execute_reply.started": "2021-12-09T13:25:29.872678Z"
    },
    "papermill": {
     "duration": 0.041364,
     "end_time": "2021-12-09T14:14:14.570612",
     "exception": false,
     "start_time": "2021-12-09T14:14:14.529248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 32)          9248      \n",
      "_________________________________________________________________\n",
      "up_sampling2d (UpSampling2D) (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 14, 14, 32)        9248      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 28, 28, 1)         289       \n",
      "=================================================================\n",
      "Total params: 28,353\n",
      "Trainable params: 28,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder = Model(inputs, decoded)\n",
    "autoencoder.compile(optimizer='rmsprop', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016183,
     "end_time": "2021-12-09T14:14:14.603543",
     "exception": false,
     "start_time": "2021-12-09T14:14:14.587360",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2021-12-09T14:14:14.645549Z",
     "iopub.status.busy": "2021-12-09T14:14:14.644559Z",
     "iopub.status.idle": "2021-12-09T14:16:03.594956Z",
     "shell.execute_reply": "2021-12-09T14:16:03.594473Z",
     "shell.execute_reply.started": "2021-12-09T13:25:40.369432Z"
    },
    "papermill": {
     "duration": 108.971688,
     "end_time": "2021-12-09T14:16:03.595069",
     "exception": false,
     "start_time": "2021-12-09T14:14:14.623381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "235/235 [==============================] - 2s 10ms/step - loss: 0.1847 - val_loss: 0.1107\n",
      "Epoch 2/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.1120 - val_loss: 0.0947\n",
      "Epoch 3/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.1027 - val_loss: 0.0967\n",
      "Epoch 4/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0982 - val_loss: 0.0893\n",
      "Epoch 5/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0954 - val_loss: 0.0887\n",
      "Epoch 6/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0935 - val_loss: 0.0856\n",
      "Epoch 7/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0920 - val_loss: 0.0837\n",
      "Epoch 8/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0909 - val_loss: 0.0863\n",
      "Epoch 9/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0901 - val_loss: 0.0825\n",
      "Epoch 10/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0894 - val_loss: 0.0829\n",
      "Epoch 11/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0888 - val_loss: 0.0822\n",
      "Epoch 12/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0883 - val_loss: 0.0817\n",
      "Epoch 13/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0878 - val_loss: 0.0811\n",
      "Epoch 14/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0875 - val_loss: 0.0815\n",
      "Epoch 15/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0871 - val_loss: 0.0813\n",
      "Epoch 16/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0868 - val_loss: 0.0818\n",
      "Epoch 17/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0865 - val_loss: 0.0803\n",
      "Epoch 18/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0863 - val_loss: 0.0814\n",
      "Epoch 19/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0861 - val_loss: 0.0810\n",
      "Epoch 20/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0859 - val_loss: 0.0813\n",
      "Epoch 21/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0857 - val_loss: 0.0796\n",
      "Epoch 22/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0855 - val_loss: 0.0814\n",
      "Epoch 23/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0854 - val_loss: 0.0799\n",
      "Epoch 24/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0852 - val_loss: 0.0821\n",
      "Epoch 25/50\n",
      "235/235 [==============================] - 2s 10ms/step - loss: 0.0852 - val_loss: 0.0805\n",
      "Epoch 26/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0850 - val_loss: 0.0814\n",
      "Epoch 27/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0849 - val_loss: 0.0792\n",
      "Epoch 28/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0848 - val_loss: 0.0796\n",
      "Epoch 29/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0847 - val_loss: 0.0797\n",
      "Epoch 30/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0846 - val_loss: 0.0794\n",
      "Epoch 31/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0845 - val_loss: 0.0795\n",
      "Epoch 32/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0845 - val_loss: 0.0798\n",
      "Epoch 33/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0844 - val_loss: 0.0790\n",
      "Epoch 34/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0843 - val_loss: 0.0795\n",
      "Epoch 35/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0842 - val_loss: 0.0789\n",
      "Epoch 36/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0842 - val_loss: 0.0797\n",
      "Epoch 37/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0841 - val_loss: 0.0790\n",
      "Epoch 38/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0841 - val_loss: 0.0798\n",
      "Epoch 39/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0840 - val_loss: 0.0795\n",
      "Epoch 40/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0840 - val_loss: 0.0791\n",
      "Epoch 41/50\n",
      "235/235 [==============================] - 2s 10ms/step - loss: 0.0839 - val_loss: 0.0800\n",
      "Epoch 42/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0839 - val_loss: 0.0787\n",
      "Epoch 43/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0839 - val_loss: 0.0797\n",
      "Epoch 44/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0838 - val_loss: 0.0794\n",
      "Epoch 45/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0838 - val_loss: 0.0790\n",
      "Epoch 46/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0837 - val_loss: 0.0788\n",
      "Epoch 47/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0837 - val_loss: 0.0794\n",
      "Epoch 48/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0837 - val_loss: 0.0791\n",
      "Epoch 49/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0836 - val_loss: 0.0792\n",
      "Epoch 50/50\n",
      "235/235 [==============================] - 2s 9ms/step - loss: 0.0836 - val_loss: 0.0794\n"
     ]
    }
   ],
   "source": [
    "epochs = 50\n",
    "batch_size = 256\n",
    "\n",
    "history = autoencoder.fit(train_noise,\n",
    "                train,\n",
    "                epochs=epochs,\n",
    "                batch_size=batch_size,\n",
    "                shuffle=True,\n",
    "                validation_data=(test_noise, test)\n",
    "               )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.50057,
     "end_time": "2021-12-09T14:16:04.595779",
     "exception": false,
     "start_time": "2021-12-09T14:16:04.095209",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Performance/ Visualise Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:16:05.625410Z",
     "iopub.status.busy": "2021-12-09T14:16:05.619839Z",
     "iopub.status.idle": "2021-12-09T14:16:06.021313Z",
     "shell.execute_reply": "2021-12-09T14:16:06.022331Z",
     "shell.execute_reply.started": "2021-12-09T13:27:37.241702Z"
    },
    "papermill": {
     "duration": 0.91609,
     "end_time": "2021-12-09T14:16:06.022540",
     "exception": false,
     "start_time": "2021-12-09T14:16:05.106450",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defining Figure\n",
    "f = plt.figure(figsize=(10,7))\n",
    "f.add_subplot()\n",
    "\n",
    "#Adding Subplot\n",
    "plt.plot(history.epoch, history.history['loss'], label = \"loss\") # Loss curve for training set\n",
    "plt.plot(history.epoch, history.history['val_loss'], label = \"val_loss\") # Loss curve for validation set\n",
    "\n",
    "plt.title(\"Loss Curve\",fontsize=18)\n",
    "plt.xlabel(\"Epochs\",fontsize=15)\n",
    "plt.ylabel(\"Loss\",fontsize=15)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "plt.savefig(\"Loss_curve.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.511196,
     "end_time": "2021-12-09T14:16:07.204933",
     "exception": false,
     "start_time": "2021-12-09T14:16:06.693737",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Sample few test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:16:08.220770Z",
     "iopub.status.busy": "2021-12-09T14:16:08.220149Z",
     "iopub.status.idle": "2021-12-09T14:16:08.388935Z",
     "shell.execute_reply": "2021-12-09T14:16:08.387657Z",
     "shell.execute_reply.started": "2021-12-09T13:27:56.264831Z"
    },
    "papermill": {
     "duration": 0.682915,
     "end_time": "2021-12-09T14:16:08.389050",
     "exception": false,
     "start_time": "2021-12-09T14:16:07.706135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Select few random test images\n",
    "num_imgs = 16\n",
    "rand = np.random.randint(1, 100)\n",
    "\n",
    "test_images = test_noise[rand:rand+num_imgs] # slicing\n",
    "test_desoided = autoencoder.predict(test_images) # predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-09T14:16:09.411036Z",
     "iopub.status.busy": "2021-12-09T14:16:09.410014Z",
     "iopub.status.idle": "2021-12-09T14:16:10.632319Z",
     "shell.execute_reply": "2021-12-09T14:16:10.632791Z",
     "shell.execute_reply.started": "2021-12-09T13:27:59.720478Z"
    },
    "papermill": {
     "duration": 1.736511,
     "end_time": "2021-12-09T14:16:10.632938",
     "exception": false,
     "start_time": "2021-12-09T14:16:08.896427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize test images with their denoised images\n",
    "\n",
    "rows = 2 # defining no. of rows in figure\n",
    "cols = 8 # defining no. of colums in figure\n",
    "\n",
    "f = plt.figure(figsize=(2*cols,2*rows*2)) # defining a figure \n",
    "\n",
    "for i in range(rows):\n",
    "    for j in range(cols): \n",
    "        f.add_subplot(rows*2,cols, (2*i*cols)+(j+1)) # adding sub plot to figure on each iteration\n",
    "        plt.imshow(test_images[i*cols + j].reshape([28,28]),cmap=\"Reds\") \n",
    "        plt.axis(\"off\")\n",
    "        \n",
    "    for j in range(cols): \n",
    "        f.add_subplot(rows*2,cols,((2*i+1)*cols)+(j+1)) # adding sub plot to figure on each iteration\n",
    "        plt.imshow(test_desoided[i*cols + j].reshape([28,28]),cmap=\"Reds\") \n",
    "        plt.axis(\"off\")\n",
    "        \n",
    "f.suptitle(\"Autoencoder Results\",fontsize=18)\n",
    "plt.savefig(\"test_results.png\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "papermill": {
   "duration": 135.127831,
   "end_time": "2021-12-09T14:16:11.373207",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-12-09T14:13:56.245376",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}