diff --git a/3 - PixelCNNs Blind spot and Gated PixelCNNs/PixelCNN_Blind_spot.ipynb b/3 - PixelCNNs Blind spot and Gated PixelCNNs/PixelCNN_Blind_spot.ipynb
index d582176..fe0b1df 100644
--- a/3 - PixelCNNs Blind spot and Gated PixelCNNs/PixelCNN_Blind_spot.ipynb	
+++ b/3 - PixelCNNs Blind spot and Gated PixelCNNs/PixelCNN_Blind_spot.ipynb	
@@ -2,19 +2,29 @@
   "nbformat": 4,
   "nbformat_minor": 0,
   "metadata": {
+    "accelerator": "GPU",
     "colab": {
-      "name": "PixelCNN - Blind_spot",
+      "name": "PixelCNN_Blind_spot.ipynb",
       "provenance": [],
       "collapsed_sections": []
     },
     "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+      "display_name": "pixel-cnn",
+      "language": "python",
+      "name": "pixel-cnn"
     },
     "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
+    }
   },
   "cells": [
     {
@@ -67,6 +77,7 @@
       },
       "source": [
         "import random as rn\n",
+        "import time\n",
         "\n",
         "import matplotlib\n",
         "import matplotlib.pyplot as plt\n",
@@ -78,11 +89,34 @@
         "from tensorflow.keras import initializers\n",
         "from tensorflow.keras.utils import Progbar\n",
         "\n",
-        "tf.keras.backend.set_floatx('float64')"
+        "tf.keras.backend.set_floatx('float32')"
       ],
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bbikfnF5d6rO",
+        "outputId": "bc7e894f-1000-46fb-8090-76ca6731f105"
+      },
+      "source": [
+        "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Num GPUs Available:  1\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -152,7 +186,7 @@
       "source": [
         "As we saw above, the original PixelCNN had a problem with its masked convolution that lead to a blind spot. Given a specific pixel, the use of masked convolution was not able to capture the information of all previous pixels. As we can see on the image below, the information on *j* is never used to predict *m*; and the information on *j, n, o* are not used to predict *q*.  \n",
         "\n",
-        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       ]
     },
     {
@@ -231,11 +265,11 @@
         "\n",
         "        center = self.kernel_size // 2\n",
         "\n",
-        "        mask = np.ones(self.kernel.shape, dtype=np.float64)\n",
+        "        mask = np.ones(self.kernel.shape, dtype=np.float32)\n",
         "        mask[center, center + (self.mask_type == 'B'):, :, :] = 0.\n",
         "        mask[center + 1:, :, :, :] = 0.\n",
         "\n",
-        "        self.mask = tf.constant(mask, dtype=tf.float64, name='mask')\n",
+        "        self.mask = tf.constant(mask, dtype=tf.float32, name='mask')\n",
         "\n",
         "    def call(self, input):\n",
         "        masked_kernel = tf.math.multiply(self.mask, self.kernel)\n",
@@ -267,7 +301,7 @@
         "  gradients = tape.gradient(loss, data)\n",
         "\n",
         "  gradients = np.abs(gradients.numpy().squeeze())\n",
-        "  gradients = (gradients > 0).astype('float64')\n",
+        "  gradients = (gradients > 0).astype('float32')\n",
         "  gradients[5, 5] = 0.5\n",
         "\n",
         "  fig = plt.figure()\n",
@@ -303,7 +337,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "APMjCAwveKo6"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "APMjCAwveKo6",
+        "outputId": "64cb00b6-86d5-46e7-b330-90d22d1e4f75"
       },
       "source": [
         "# 1 layer PixelCNN\n",
@@ -314,12 +353,30 @@
         "plot_receptive_field(model, data)\n"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "aUHo0N7-6IYa"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "aUHo0N7-6IYa",
+        "outputId": "f1919267-8f98-4821-e0ce-f34aed6684d2"
       },
       "source": [
         "# 2 layer PixelCNN\n",
@@ -332,12 +389,30 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "RFMjAntD6Kot"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "RFMjAntD6Kot",
+        "outputId": "3248d446-1d4e-4180-8390-bb83a426a31a"
       },
       "source": [
         "# 3 layer PixelCNN\n",
@@ -351,12 +426,30 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "zbsQRrol6O6W"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "zbsQRrol6O6W",
+        "outputId": "85ebce1c-ee36-484b-8696-22187c9dc7c2"
       },
       "source": [
         "# 4 layer PixelCNN\n",
@@ -371,12 +464,30 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "uaDjY7gK6aBa"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "uaDjY7gK6aBa",
+        "outputId": "62af3cbd-4098-4c6f-8519-8fdba1146397"
       },
       "source": [
         "# 5 layer PixelCNN\n",
@@ -392,7 +503,20 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -412,7 +536,8 @@
         "id": "2zK_0-Ck7_sW"
       },
       "source": [
-        "![stacks.png](data:image/png;base64,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)"
+        "\n",
+        "![Screenshot 2021-07-30 at 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)"
       ]
     },
     {
@@ -492,7 +617,7 @@
         "                                    shape=(self.filters,),\n",
         "                                    initializer=self.bias_initializer,\n",
         "                                    trainable=True)\n",
-        "        mask = np.ones(self.kernel.shape, dtype=np.float64)\n",
+        "        mask = np.ones(self.kernel.shape, dtype=np.float32)\n",
         "\n",
         "        # Get centre of the filter for even or odd dimensions\n",
         "        if kernel_h % 2 != 0:\n",
@@ -512,7 +637,7 @@
         "            mask[center_h, center_w + (self.mask_type == 'B'):, :, :] = 0.\n",
         "            mask[center_h + 1:, :, :] = 0.\n",
         "\n",
-        "        self.mask = tf.constant(mask, dtype=tf.float64, name='mask')\n",
+        "        self.mask = tf.constant(mask, dtype=tf.float32, name='mask')\n",
         "\n",
         "    def call(self, input):\n",
         "        masked_kernel = tf.math.multiply(self.mask, self.kernel)\n",
@@ -621,13 +746,31 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "dhFgEQI6clrA"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "dhFgEQI6clrA",
+        "outputId": "515b8c20-9c38-45f7-e201-e85bb5e3d527"
       },
       "source": [
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -645,7 +788,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "S8TfMRxqg5tb"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "S8TfMRxqg5tb",
+        "outputId": "8862c405-8866-4c71-d47d-c5cf02444917"
       },
       "source": [
         "# 2 layers Gated PixelCNN\n",
@@ -657,12 +805,30 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "8ljKvZsrg6yx"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "8ljKvZsrg6yx",
+        "outputId": "4973d4b5-df97-47b5-8e45-44a7b46a154e"
       },
       "source": [
         "# 3 layers Gated PixelCNN\n",
@@ -675,12 +841,30 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "kExc-22eg-MA"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "kExc-22eg-MA",
+        "outputId": "9510a043-a775-4afc-b790-d24fee5b6608"
       },
       "source": [
         "# 4 layers Gated PixelCNN\n",
@@ -694,12 +878,30 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "wHImyVeXhBUT"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "wHImyVeXhBUT",
+        "outputId": "b6ff01ed-5da3-4108-b43d-ac7075ffc57b"
       },
       "source": [
         "# 5 layers Gated PixelCNN\n",
@@ -714,7 +916,20 @@
         "plot_receptive_field(model, data)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -756,7 +971,11 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "Yf6g0Mqnh2JU"
+        "id": "Yf6g0Mqnh2JU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a88d2066-a0d5-48b1-a116-475d96e813d0"
       },
       "source": [
         "# Loading data\n",
@@ -773,7 +992,17 @@
         "x_test = x_test.reshape(x_test.shape[0], height, width, n_channel)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+            "11493376/11490434 [==============================] - 0s 0us/step\n",
+            "11501568/11490434 [==============================] - 0s 0us/step\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -946,24 +1175,646 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "9-D01ij3i2q2"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9-D01ij3i2q2",
+        "outputId": "17e67af1-45bd-44a7-acf9-8c264bfd8a86"
       },
       "source": [
         "# Training loop\n",
-        "n_epochs = 1\n",
+        "n_epochs = 100\n",
         "n_iter = int(np.ceil(x_train_quantised.shape[0] / batch_size))\n",
         "for epoch in range(n_epochs):\n",
         "    progbar = Progbar(n_iter)\n",
         "    print('Epoch {:}/{:}'.format(epoch + 1, n_epochs))\n",
         "\n",
+        "    start_epoch = time.time()\n",
         "    for i_iter, (batch_x, batch_y) in enumerate(train_dataset):\n",
+        "        start = time.time()\n",
+        "        epoch_time = time.time() - start_epoch\n",
         "        optimizer.lr = optimizer.lr * lr_decay\n",
         "        loss = train_step(batch_x, batch_y)\n",
         "\n",
-        "        progbar.add(1, values=[('loss', loss)])"
+        "        iter_time = time.time() - start\n",
+        "        if i_iter % 100 == 0:\n",
+        "                    print('EPOCH {:3d}: ITER {:4d}/{:4d} TIME: {:.2f} LOSS: {:.4f}'.format(epoch,\n",
+        "                                                                                           i_iter, n_iter,\n",
+        "                                                                                           iter_time,\n",
+        "                                                                                           loss))\n",
+        "    print('EPOCH {:3d}: TIME: {:.2f} ETA: {:.2f}'.format(epoch,\n",
+        "                                                                 epoch_time,\n",
+        "                                                                 epoch_time * (n_epochs - epoch)))"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/100\n",
+            "EPOCH   0: ITER    0/ 313 TIME: 1.96 LOSS: 0.6935\n",
+            "EPOCH   0: ITER  100/ 313 TIME: 0.23 LOSS: 0.1087\n",
+            "EPOCH   0: ITER  200/ 313 TIME: 0.23 LOSS: 0.1002\n",
+            "EPOCH   0: ITER  300/ 313 TIME: 0.23 LOSS: 0.0938\n",
+            "EPOCH   0: TIME: 73.04 ETA: 7304.36\n",
+            "Epoch 2/100\n",
+            "EPOCH   1: ITER    0/ 313 TIME: 0.01 LOSS: 0.0959\n",
+            "EPOCH   1: ITER  100/ 313 TIME: 0.23 LOSS: 0.0955\n",
+            "EPOCH   1: ITER  200/ 313 TIME: 0.22 LOSS: 0.0909\n",
+            "EPOCH   1: ITER  300/ 313 TIME: 0.23 LOSS: 0.0906\n",
+            "EPOCH   1: TIME: 71.52 ETA: 7080.22\n",
+            "Epoch 3/100\n",
+            "EPOCH   2: ITER    0/ 313 TIME: 0.01 LOSS: 0.0876\n",
+            "EPOCH   2: ITER  100/ 313 TIME: 0.23 LOSS: 0.0892\n",
+            "EPOCH   2: ITER  200/ 313 TIME: 0.23 LOSS: 0.0896\n",
+            "EPOCH   2: ITER  300/ 313 TIME: 0.22 LOSS: 0.0843\n",
+            "EPOCH   2: TIME: 71.80 ETA: 7036.33\n",
+            "Epoch 4/100\n",
+            "EPOCH   3: ITER    0/ 313 TIME: 0.01 LOSS: 0.0861\n",
+            "EPOCH   3: ITER  100/ 313 TIME: 0.23 LOSS: 0.0870\n",
+            "EPOCH   3: ITER  200/ 313 TIME: 0.22 LOSS: 0.0874\n",
+            "EPOCH   3: ITER  300/ 313 TIME: 0.23 LOSS: 0.0870\n",
+            "EPOCH   3: TIME: 71.83 ETA: 6967.87\n",
+            "Epoch 5/100\n",
+            "EPOCH   4: ITER    0/ 313 TIME: 0.01 LOSS: 0.0851\n",
+            "EPOCH   4: ITER  100/ 313 TIME: 0.23 LOSS: 0.0841\n",
+            "EPOCH   4: ITER  200/ 313 TIME: 0.23 LOSS: 0.0838\n",
+            "EPOCH   4: ITER  300/ 313 TIME: 0.23 LOSS: 0.0848\n",
+            "EPOCH   4: TIME: 71.82 ETA: 6894.81\n",
+            "Epoch 6/100\n",
+            "EPOCH   5: ITER    0/ 313 TIME: 0.02 LOSS: 0.0845\n",
+            "EPOCH   5: ITER  100/ 313 TIME: 0.23 LOSS: 0.0839\n",
+            "EPOCH   5: ITER  200/ 313 TIME: 0.23 LOSS: 0.0840\n",
+            "EPOCH   5: ITER  300/ 313 TIME: 0.22 LOSS: 0.0840\n",
+            "EPOCH   5: TIME: 71.86 ETA: 6826.89\n",
+            "Epoch 7/100\n",
+            "EPOCH   6: ITER    0/ 313 TIME: 0.02 LOSS: 0.0834\n",
+            "EPOCH   6: ITER  100/ 313 TIME: 0.23 LOSS: 0.0820\n",
+            "EPOCH   6: ITER  200/ 313 TIME: 0.23 LOSS: 0.0820\n",
+            "EPOCH   6: ITER  300/ 313 TIME: 0.23 LOSS: 0.0854\n",
+            "EPOCH   6: TIME: 71.88 ETA: 6756.76\n",
+            "Epoch 8/100\n",
+            "EPOCH   7: ITER    0/ 313 TIME: 0.01 LOSS: 0.0844\n",
+            "EPOCH   7: ITER  100/ 313 TIME: 0.23 LOSS: 0.0837\n",
+            "EPOCH   7: ITER  200/ 313 TIME: 0.23 LOSS: 0.0831\n",
+            "EPOCH   7: ITER  300/ 313 TIME: 0.23 LOSS: 0.0827\n",
+            "EPOCH   7: TIME: 71.84 ETA: 6680.99\n",
+            "Epoch 9/100\n",
+            "EPOCH   8: ITER    0/ 313 TIME: 0.01 LOSS: 0.0849\n",
+            "EPOCH   8: ITER  100/ 313 TIME: 0.22 LOSS: 0.0820\n",
+            "EPOCH   8: ITER  200/ 313 TIME: 0.23 LOSS: 0.0814\n",
+            "EPOCH   8: ITER  300/ 313 TIME: 0.23 LOSS: 0.0820\n",
+            "EPOCH   8: TIME: 71.86 ETA: 6611.41\n",
+            "Epoch 10/100\n",
+            "EPOCH   9: ITER    0/ 313 TIME: 0.01 LOSS: 0.0848\n",
+            "EPOCH   9: ITER  100/ 313 TIME: 0.23 LOSS: 0.0837\n",
+            "EPOCH   9: ITER  200/ 313 TIME: 0.23 LOSS: 0.0830\n",
+            "EPOCH   9: ITER  300/ 313 TIME: 0.23 LOSS: 0.0833\n",
+            "EPOCH   9: TIME: 71.89 ETA: 6541.66\n",
+            "Epoch 11/100\n",
+            "EPOCH  10: ITER    0/ 313 TIME: 0.01 LOSS: 0.0819\n",
+            "EPOCH  10: ITER  100/ 313 TIME: 0.23 LOSS: 0.0815\n",
+            "EPOCH  10: ITER  200/ 313 TIME: 0.23 LOSS: 0.0804\n",
+            "EPOCH  10: ITER  300/ 313 TIME: 0.23 LOSS: 0.0834\n",
+            "EPOCH  10: TIME: 71.90 ETA: 6470.99\n",
+            "Epoch 12/100\n",
+            "EPOCH  11: ITER    0/ 313 TIME: 0.01 LOSS: 0.0852\n",
+            "EPOCH  11: ITER  100/ 313 TIME: 0.23 LOSS: 0.0813\n",
+            "EPOCH  11: ITER  200/ 313 TIME: 0.22 LOSS: 0.0816\n",
+            "EPOCH  11: ITER  300/ 313 TIME: 0.22 LOSS: 0.0832\n",
+            "EPOCH  11: TIME: 71.92 ETA: 6400.92\n",
+            "Epoch 13/100\n",
+            "EPOCH  12: ITER    0/ 313 TIME: 0.01 LOSS: 0.0818\n",
+            "EPOCH  12: ITER  100/ 313 TIME: 0.23 LOSS: 0.0827\n",
+            "EPOCH  12: ITER  200/ 313 TIME: 0.23 LOSS: 0.0809\n",
+            "EPOCH  12: ITER  300/ 313 TIME: 0.24 LOSS: 0.0819\n",
+            "EPOCH  12: TIME: 71.94 ETA: 6330.71\n",
+            "Epoch 14/100\n",
+            "EPOCH  13: ITER    0/ 313 TIME: 0.02 LOSS: 0.0795\n",
+            "EPOCH  13: ITER  100/ 313 TIME: 0.23 LOSS: 0.0841\n",
+            "EPOCH  13: ITER  200/ 313 TIME: 0.23 LOSS: 0.0803\n",
+            "EPOCH  13: ITER  300/ 313 TIME: 0.24 LOSS: 0.0814\n",
+            "EPOCH  13: TIME: 71.94 ETA: 6259.08\n",
+            "Epoch 15/100\n",
+            "EPOCH  14: ITER    0/ 313 TIME: 0.01 LOSS: 0.0800\n",
+            "EPOCH  14: ITER  100/ 313 TIME: 0.23 LOSS: 0.0796\n",
+            "EPOCH  14: ITER  200/ 313 TIME: 0.23 LOSS: 0.0814\n",
+            "EPOCH  14: ITER  300/ 313 TIME: 0.22 LOSS: 0.0811\n",
+            "EPOCH  14: TIME: 71.93 ETA: 6185.70\n",
+            "Epoch 16/100\n",
+            "EPOCH  15: ITER    0/ 313 TIME: 0.01 LOSS: 0.0806\n",
+            "EPOCH  15: ITER  100/ 313 TIME: 0.22 LOSS: 0.0819\n",
+            "EPOCH  15: ITER  200/ 313 TIME: 0.23 LOSS: 0.0794\n",
+            "EPOCH  15: ITER  300/ 313 TIME: 0.22 LOSS: 0.0815\n",
+            "EPOCH  15: TIME: 72.00 ETA: 6119.93\n",
+            "Epoch 17/100\n",
+            "EPOCH  16: ITER    0/ 313 TIME: 0.01 LOSS: 0.0829\n",
+            "EPOCH  16: ITER  100/ 313 TIME: 0.22 LOSS: 0.0835\n",
+            "EPOCH  16: ITER  200/ 313 TIME: 0.23 LOSS: 0.0788\n",
+            "EPOCH  16: ITER  300/ 313 TIME: 0.23 LOSS: 0.0810\n",
+            "EPOCH  16: TIME: 71.98 ETA: 6046.57\n",
+            "Epoch 18/100\n",
+            "EPOCH  17: ITER    0/ 313 TIME: 0.02 LOSS: 0.0803\n",
+            "EPOCH  17: ITER  100/ 313 TIME: 0.23 LOSS: 0.0792\n",
+            "EPOCH  17: ITER  200/ 313 TIME: 0.22 LOSS: 0.0829\n",
+            "EPOCH  17: ITER  300/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  17: TIME: 72.03 ETA: 5978.30\n",
+            "Epoch 19/100\n",
+            "EPOCH  18: ITER    0/ 313 TIME: 0.01 LOSS: 0.0798\n",
+            "EPOCH  18: ITER  100/ 313 TIME: 0.23 LOSS: 0.0816\n",
+            "EPOCH  18: ITER  200/ 313 TIME: 0.23 LOSS: 0.0806\n",
+            "EPOCH  18: ITER  300/ 313 TIME: 0.24 LOSS: 0.0819\n",
+            "EPOCH  18: TIME: 71.98 ETA: 5901.95\n",
+            "Epoch 20/100\n",
+            "EPOCH  19: ITER    0/ 313 TIME: 0.01 LOSS: 0.0814\n",
+            "EPOCH  19: ITER  100/ 313 TIME: 0.23 LOSS: 0.0786\n",
+            "EPOCH  19: ITER  200/ 313 TIME: 0.24 LOSS: 0.0806\n",
+            "EPOCH  19: ITER  300/ 313 TIME: 0.23 LOSS: 0.0790\n",
+            "EPOCH  19: TIME: 71.94 ETA: 5827.09\n",
+            "Epoch 21/100\n",
+            "EPOCH  20: ITER    0/ 313 TIME: 0.01 LOSS: 0.0824\n",
+            "EPOCH  20: ITER  100/ 313 TIME: 0.23 LOSS: 0.0777\n",
+            "EPOCH  20: ITER  200/ 313 TIME: 0.23 LOSS: 0.0794\n",
+            "EPOCH  20: ITER  300/ 313 TIME: 0.23 LOSS: 0.0826\n",
+            "EPOCH  20: TIME: 71.91 ETA: 5753.18\n",
+            "Epoch 22/100\n",
+            "EPOCH  21: ITER    0/ 313 TIME: 0.01 LOSS: 0.0789\n",
+            "EPOCH  21: ITER  100/ 313 TIME: 0.22 LOSS: 0.0790\n",
+            "EPOCH  21: ITER  200/ 313 TIME: 0.23 LOSS: 0.0780\n",
+            "EPOCH  21: ITER  300/ 313 TIME: 0.23 LOSS: 0.0808\n",
+            "EPOCH  21: TIME: 71.92 ETA: 5681.50\n",
+            "Epoch 23/100\n",
+            "EPOCH  22: ITER    0/ 313 TIME: 0.01 LOSS: 0.0788\n",
+            "EPOCH  22: ITER  100/ 313 TIME: 0.22 LOSS: 0.0786\n",
+            "EPOCH  22: ITER  200/ 313 TIME: 0.23 LOSS: 0.0806\n",
+            "EPOCH  22: ITER  300/ 313 TIME: 0.23 LOSS: 0.0804\n",
+            "EPOCH  22: TIME: 72.03 ETA: 5618.20\n",
+            "Epoch 24/100\n",
+            "EPOCH  23: ITER    0/ 313 TIME: 0.01 LOSS: 0.0789\n",
+            "EPOCH  23: ITER  100/ 313 TIME: 0.23 LOSS: 0.0811\n",
+            "EPOCH  23: ITER  200/ 313 TIME: 0.22 LOSS: 0.0779\n",
+            "EPOCH  23: ITER  300/ 313 TIME: 0.23 LOSS: 0.0794\n",
+            "EPOCH  23: TIME: 72.03 ETA: 5546.62\n",
+            "Epoch 25/100\n",
+            "EPOCH  24: ITER    0/ 313 TIME: 0.01 LOSS: 0.0772\n",
+            "EPOCH  24: ITER  100/ 313 TIME: 0.22 LOSS: 0.0775\n",
+            "EPOCH  24: ITER  200/ 313 TIME: 0.22 LOSS: 0.0784\n",
+            "EPOCH  24: ITER  300/ 313 TIME: 0.23 LOSS: 0.0786\n",
+            "EPOCH  24: TIME: 72.08 ETA: 5477.93\n",
+            "Epoch 26/100\n",
+            "EPOCH  25: ITER    0/ 313 TIME: 0.01 LOSS: 0.0794\n",
+            "EPOCH  25: ITER  100/ 313 TIME: 0.24 LOSS: 0.0797\n",
+            "EPOCH  25: ITER  200/ 313 TIME: 0.23 LOSS: 0.0792\n",
+            "EPOCH  25: ITER  300/ 313 TIME: 0.22 LOSS: 0.0780\n",
+            "EPOCH  25: TIME: 72.07 ETA: 5405.62\n",
+            "Epoch 27/100\n",
+            "EPOCH  26: ITER    0/ 313 TIME: 0.01 LOSS: 0.0811\n",
+            "EPOCH  26: ITER  100/ 313 TIME: 0.23 LOSS: 0.0785\n",
+            "EPOCH  26: ITER  200/ 313 TIME: 0.23 LOSS: 0.0789\n",
+            "EPOCH  26: ITER  300/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  26: TIME: 72.10 ETA: 5335.31\n",
+            "Epoch 28/100\n",
+            "EPOCH  27: ITER    0/ 313 TIME: 0.01 LOSS: 0.0789\n",
+            "EPOCH  27: ITER  100/ 313 TIME: 0.22 LOSS: 0.0793\n",
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+            "EPOCH  27: ITER  300/ 313 TIME: 0.24 LOSS: 0.0809\n",
+            "EPOCH  27: TIME: 72.09 ETA: 5262.89\n",
+            "Epoch 29/100\n",
+            "EPOCH  28: ITER    0/ 313 TIME: 0.01 LOSS: 0.0776\n",
+            "EPOCH  28: ITER  100/ 313 TIME: 0.23 LOSS: 0.0750\n",
+            "EPOCH  28: ITER  200/ 313 TIME: 0.22 LOSS: 0.0798\n",
+            "EPOCH  28: ITER  300/ 313 TIME: 0.23 LOSS: 0.0773\n",
+            "EPOCH  28: TIME: 72.11 ETA: 5192.16\n",
+            "Epoch 30/100\n",
+            "EPOCH  29: ITER    0/ 313 TIME: 0.01 LOSS: 0.0780\n",
+            "EPOCH  29: ITER  100/ 313 TIME: 0.23 LOSS: 0.0789\n",
+            "EPOCH  29: ITER  200/ 313 TIME: 0.23 LOSS: 0.0759\n",
+            "EPOCH  29: ITER  300/ 313 TIME: 0.23 LOSS: 0.0766\n",
+            "EPOCH  29: TIME: 72.16 ETA: 5123.11\n",
+            "Epoch 31/100\n",
+            "EPOCH  30: ITER    0/ 313 TIME: 0.01 LOSS: 0.0802\n",
+            "EPOCH  30: ITER  100/ 313 TIME: 0.23 LOSS: 0.0791\n",
+            "EPOCH  30: ITER  200/ 313 TIME: 0.23 LOSS: 0.0774\n",
+            "EPOCH  30: ITER  300/ 313 TIME: 0.23 LOSS: 0.0782\n",
+            "EPOCH  30: TIME: 72.18 ETA: 5052.79\n",
+            "Epoch 32/100\n",
+            "EPOCH  31: ITER    0/ 313 TIME: 0.01 LOSS: 0.0772\n",
+            "EPOCH  31: ITER  100/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  31: ITER  200/ 313 TIME: 0.24 LOSS: 0.0788\n",
+            "EPOCH  31: ITER  300/ 313 TIME: 0.24 LOSS: 0.0785\n",
+            "EPOCH  31: TIME: 72.15 ETA: 4978.21\n",
+            "Epoch 33/100\n",
+            "EPOCH  32: ITER    0/ 313 TIME: 0.01 LOSS: 0.0781\n",
+            "EPOCH  32: ITER  100/ 313 TIME: 0.23 LOSS: 0.0764\n",
+            "EPOCH  32: ITER  200/ 313 TIME: 0.22 LOSS: 0.0790\n",
+            "EPOCH  32: ITER  300/ 313 TIME: 0.24 LOSS: 0.0779\n",
+            "EPOCH  32: TIME: 72.13 ETA: 4904.99\n",
+            "Epoch 34/100\n",
+            "EPOCH  33: ITER    0/ 313 TIME: 0.01 LOSS: 0.0767\n",
+            "EPOCH  33: ITER  100/ 313 TIME: 0.24 LOSS: 0.0753\n",
+            "EPOCH  33: ITER  200/ 313 TIME: 0.23 LOSS: 0.0782\n",
+            "EPOCH  33: ITER  300/ 313 TIME: 0.23 LOSS: 0.0792\n",
+            "EPOCH  33: TIME: 72.17 ETA: 4835.59\n",
+            "Epoch 35/100\n",
+            "EPOCH  34: ITER    0/ 313 TIME: 0.01 LOSS: 0.0772\n",
+            "EPOCH  34: ITER  100/ 313 TIME: 0.22 LOSS: 0.0796\n",
+            "EPOCH  34: ITER  200/ 313 TIME: 0.23 LOSS: 0.0784\n",
+            "EPOCH  34: ITER  300/ 313 TIME: 0.23 LOSS: 0.0792\n",
+            "EPOCH  34: TIME: 72.19 ETA: 4764.25\n",
+            "Epoch 36/100\n",
+            "EPOCH  35: ITER    0/ 313 TIME: 0.01 LOSS: 0.0767\n",
+            "EPOCH  35: ITER  100/ 313 TIME: 0.23 LOSS: 0.0767\n",
+            "EPOCH  35: ITER  200/ 313 TIME: 0.23 LOSS: 0.0800\n",
+            "EPOCH  35: ITER  300/ 313 TIME: 0.23 LOSS: 0.0789\n",
+            "EPOCH  35: TIME: 72.17 ETA: 4691.24\n",
+            "Epoch 37/100\n",
+            "EPOCH  36: ITER    0/ 313 TIME: 0.01 LOSS: 0.0782\n",
+            "EPOCH  36: ITER  100/ 313 TIME: 0.23 LOSS: 0.0760\n",
+            "EPOCH  36: ITER  200/ 313 TIME: 0.22 LOSS: 0.0798\n",
+            "EPOCH  36: ITER  300/ 313 TIME: 0.23 LOSS: 0.0774\n",
+            "EPOCH  36: TIME: 72.18 ETA: 4619.76\n",
+            "Epoch 38/100\n",
+            "EPOCH  37: ITER    0/ 313 TIME: 0.01 LOSS: 0.0759\n",
+            "EPOCH  37: ITER  100/ 313 TIME: 0.23 LOSS: 0.0758\n",
+            "EPOCH  37: ITER  200/ 313 TIME: 0.23 LOSS: 0.0784\n",
+            "EPOCH  37: ITER  300/ 313 TIME: 0.23 LOSS: 0.0792\n",
+            "EPOCH  37: TIME: 72.19 ETA: 4548.23\n",
+            "Epoch 39/100\n",
+            "EPOCH  38: ITER    0/ 313 TIME: 0.01 LOSS: 0.0786\n",
+            "EPOCH  38: ITER  100/ 313 TIME: 0.23 LOSS: 0.0757\n",
+            "EPOCH  38: ITER  200/ 313 TIME: 0.23 LOSS: 0.0800\n",
+            "EPOCH  38: ITER  300/ 313 TIME: 0.23 LOSS: 0.0796\n",
+            "EPOCH  38: TIME: 72.20 ETA: 4476.56\n",
+            "Epoch 40/100\n",
+            "EPOCH  39: ITER    0/ 313 TIME: 0.01 LOSS: 0.0774\n",
+            "EPOCH  39: ITER  100/ 313 TIME: 0.23 LOSS: 0.0796\n",
+            "EPOCH  39: ITER  200/ 313 TIME: 0.23 LOSS: 0.0775\n",
+            "EPOCH  39: ITER  300/ 313 TIME: 0.23 LOSS: 0.0759\n",
+            "EPOCH  39: TIME: 72.21 ETA: 4404.64\n",
+            "Epoch 41/100\n",
+            "EPOCH  40: ITER    0/ 313 TIME: 0.01 LOSS: 0.0769\n",
+            "EPOCH  40: ITER  100/ 313 TIME: 0.23 LOSS: 0.0784\n",
+            "EPOCH  40: ITER  200/ 313 TIME: 0.23 LOSS: 0.0777\n",
+            "EPOCH  40: ITER  300/ 313 TIME: 0.24 LOSS: 0.0756\n",
+            "EPOCH  40: TIME: 72.23 ETA: 4333.55\n",
+            "Epoch 42/100\n",
+            "EPOCH  41: ITER    0/ 313 TIME: 0.01 LOSS: 0.0789\n",
+            "EPOCH  41: ITER  100/ 313 TIME: 0.23 LOSS: 0.0767\n",
+            "EPOCH  41: ITER  200/ 313 TIME: 0.23 LOSS: 0.0782\n",
+            "EPOCH  41: ITER  300/ 313 TIME: 0.22 LOSS: 0.0780\n",
+            "EPOCH  41: TIME: 72.18 ETA: 4258.54\n",
+            "Epoch 43/100\n",
+            "EPOCH  42: ITER    0/ 313 TIME: 0.01 LOSS: 0.0789\n",
+            "EPOCH  42: ITER  100/ 313 TIME: 0.22 LOSS: 0.0774\n",
+            "EPOCH  42: ITER  200/ 313 TIME: 0.24 LOSS: 0.0764\n",
+            "EPOCH  42: ITER  300/ 313 TIME: 0.23 LOSS: 0.0750\n",
+            "EPOCH  42: TIME: 72.18 ETA: 4186.24\n",
+            "Epoch 44/100\n",
+            "EPOCH  43: ITER    0/ 313 TIME: 0.01 LOSS: 0.0745\n",
+            "EPOCH  43: ITER  100/ 313 TIME: 0.23 LOSS: 0.0746\n",
+            "EPOCH  43: ITER  200/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  43: ITER  300/ 313 TIME: 0.24 LOSS: 0.0759\n",
+            "EPOCH  43: TIME: 72.22 ETA: 4116.50\n",
+            "Epoch 45/100\n",
+            "EPOCH  44: ITER    0/ 313 TIME: 0.01 LOSS: 0.0774\n",
+            "EPOCH  44: ITER  100/ 313 TIME: 0.23 LOSS: 0.0770\n",
+            "EPOCH  44: ITER  200/ 313 TIME: 0.23 LOSS: 0.0751\n",
+            "EPOCH  44: ITER  300/ 313 TIME: 0.23 LOSS: 0.0753\n",
+            "EPOCH  44: TIME: 72.21 ETA: 4043.61\n",
+            "Epoch 46/100\n",
+            "EPOCH  45: ITER    0/ 313 TIME: 0.01 LOSS: 0.0763\n",
+            "EPOCH  45: ITER  100/ 313 TIME: 0.23 LOSS: 0.0761\n",
+            "EPOCH  45: ITER  200/ 313 TIME: 0.23 LOSS: 0.0783\n",
+            "EPOCH  45: ITER  300/ 313 TIME: 0.22 LOSS: 0.0768\n",
+            "EPOCH  45: TIME: 72.27 ETA: 3974.83\n",
+            "Epoch 47/100\n",
+            "EPOCH  46: ITER    0/ 313 TIME: 0.01 LOSS: 0.0756\n",
+            "EPOCH  46: ITER  100/ 313 TIME: 0.23 LOSS: 0.0764\n",
+            "EPOCH  46: ITER  200/ 313 TIME: 0.24 LOSS: 0.0765\n",
+            "EPOCH  46: ITER  300/ 313 TIME: 0.24 LOSS: 0.0752\n",
+            "EPOCH  46: TIME: 72.29 ETA: 3903.56\n",
+            "Epoch 48/100\n",
+            "EPOCH  47: ITER    0/ 313 TIME: 0.01 LOSS: 0.0753\n",
+            "EPOCH  47: ITER  100/ 313 TIME: 0.24 LOSS: 0.0760\n",
+            "EPOCH  47: ITER  200/ 313 TIME: 0.23 LOSS: 0.0770\n",
+            "EPOCH  47: ITER  300/ 313 TIME: 0.24 LOSS: 0.0771\n",
+            "EPOCH  47: TIME: 72.23 ETA: 3828.34\n",
+            "Epoch 49/100\n",
+            "EPOCH  48: ITER    0/ 313 TIME: 0.01 LOSS: 0.0766\n",
+            "EPOCH  48: ITER  100/ 313 TIME: 0.23 LOSS: 0.0775\n",
+            "EPOCH  48: ITER  200/ 313 TIME: 0.23 LOSS: 0.0758\n",
+            "EPOCH  48: ITER  300/ 313 TIME: 0.26 LOSS: 0.0754\n",
+            "EPOCH  48: TIME: 72.29 ETA: 3758.84\n",
+            "Epoch 50/100\n",
+            "EPOCH  49: ITER    0/ 313 TIME: 0.01 LOSS: 0.0790\n",
+            "EPOCH  49: ITER  100/ 313 TIME: 0.23 LOSS: 0.0792\n",
+            "EPOCH  49: ITER  200/ 313 TIME: 0.23 LOSS: 0.0770\n",
+            "EPOCH  49: ITER  300/ 313 TIME: 0.23 LOSS: 0.0774\n",
+            "EPOCH  49: TIME: 72.25 ETA: 3684.74\n",
+            "Epoch 51/100\n",
+            "EPOCH  50: ITER    0/ 313 TIME: 0.01 LOSS: 0.0765\n",
+            "EPOCH  50: ITER  100/ 313 TIME: 0.22 LOSS: 0.0764\n",
+            "EPOCH  50: ITER  200/ 313 TIME: 0.23 LOSS: 0.0758\n",
+            "EPOCH  50: ITER  300/ 313 TIME: 0.24 LOSS: 0.0775\n",
+            "EPOCH  50: TIME: 72.19 ETA: 3609.62\n",
+            "Epoch 52/100\n",
+            "EPOCH  51: ITER    0/ 313 TIME: 0.01 LOSS: 0.0760\n",
+            "EPOCH  51: ITER  100/ 313 TIME: 0.23 LOSS: 0.0767\n",
+            "EPOCH  51: ITER  200/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  51: ITER  300/ 313 TIME: 0.23 LOSS: 0.0755\n",
+            "EPOCH  51: TIME: 72.22 ETA: 3539.02\n",
+            "Epoch 53/100\n",
+            "EPOCH  52: ITER    0/ 313 TIME: 0.01 LOSS: 0.0753\n",
+            "EPOCH  52: ITER  100/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  52: ITER  200/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  52: ITER  300/ 313 TIME: 0.24 LOSS: 0.0759\n",
+            "EPOCH  52: TIME: 72.25 ETA: 3468.24\n",
+            "Epoch 54/100\n",
+            "EPOCH  53: ITER    0/ 313 TIME: 0.01 LOSS: 0.0745\n",
+            "EPOCH  53: ITER  100/ 313 TIME: 0.23 LOSS: 0.0732\n",
+            "EPOCH  53: ITER  200/ 313 TIME: 0.24 LOSS: 0.0751\n",
+            "EPOCH  53: ITER  300/ 313 TIME: 0.23 LOSS: 0.0759\n",
+            "EPOCH  53: TIME: 72.25 ETA: 3395.62\n",
+            "Epoch 55/100\n",
+            "EPOCH  54: ITER    0/ 313 TIME: 0.01 LOSS: 0.0775\n",
+            "EPOCH  54: ITER  100/ 313 TIME: 0.23 LOSS: 0.0777\n",
+            "EPOCH  54: ITER  200/ 313 TIME: 0.23 LOSS: 0.0736\n",
+            "EPOCH  54: ITER  300/ 313 TIME: 0.23 LOSS: 0.0761\n",
+            "EPOCH  54: TIME: 72.24 ETA: 3323.22\n",
+            "Epoch 56/100\n",
+            "EPOCH  55: ITER    0/ 313 TIME: 0.01 LOSS: 0.0762\n",
+            "EPOCH  55: ITER  100/ 313 TIME: 0.23 LOSS: 0.0750\n",
+            "EPOCH  55: ITER  200/ 313 TIME: 0.22 LOSS: 0.0751\n",
+            "EPOCH  55: ITER  300/ 313 TIME: 0.24 LOSS: 0.0769\n",
+            "EPOCH  55: TIME: 72.26 ETA: 3251.48\n",
+            "Epoch 57/100\n",
+            "EPOCH  56: ITER    0/ 313 TIME: 0.01 LOSS: 0.0743\n",
+            "EPOCH  56: ITER  100/ 313 TIME: 0.24 LOSS: 0.0749\n",
+            "EPOCH  56: ITER  200/ 313 TIME: 0.24 LOSS: 0.0763\n",
+            "EPOCH  56: ITER  300/ 313 TIME: 0.23 LOSS: 0.0757\n",
+            "EPOCH  56: TIME: 72.23 ETA: 3178.03\n",
+            "Epoch 58/100\n",
+            "EPOCH  57: ITER    0/ 313 TIME: 0.01 LOSS: 0.0753\n",
+            "EPOCH  57: ITER  100/ 313 TIME: 0.23 LOSS: 0.0749\n",
+            "EPOCH  57: ITER  200/ 313 TIME: 0.23 LOSS: 0.0761\n",
+            "EPOCH  57: ITER  300/ 313 TIME: 0.22 LOSS: 0.0778\n",
+            "EPOCH  57: TIME: 72.24 ETA: 3106.16\n",
+            "Epoch 59/100\n",
+            "EPOCH  58: ITER    0/ 313 TIME: 0.01 LOSS: 0.0735\n",
+            "EPOCH  58: ITER  100/ 313 TIME: 0.23 LOSS: 0.0762\n",
+            "EPOCH  58: ITER  200/ 313 TIME: 0.23 LOSS: 0.0771\n",
+            "EPOCH  58: ITER  300/ 313 TIME: 0.22 LOSS: 0.0729\n",
+            "EPOCH  58: TIME: 72.19 ETA: 3031.95\n",
+            "Epoch 60/100\n",
+            "EPOCH  59: ITER    0/ 313 TIME: 0.01 LOSS: 0.0760\n",
+            "EPOCH  59: ITER  100/ 313 TIME: 0.23 LOSS: 0.0719\n",
+            "EPOCH  59: ITER  200/ 313 TIME: 0.22 LOSS: 0.0783\n",
+            "EPOCH  59: ITER  300/ 313 TIME: 0.23 LOSS: 0.0748\n",
+            "EPOCH  59: TIME: 72.23 ETA: 2961.49\n",
+            "Epoch 61/100\n",
+            "EPOCH  60: ITER    0/ 313 TIME: 0.01 LOSS: 0.0741\n",
+            "EPOCH  60: ITER  100/ 313 TIME: 0.22 LOSS: 0.0732\n",
+            "EPOCH  60: ITER  200/ 313 TIME: 0.23 LOSS: 0.0759\n",
+            "EPOCH  60: ITER  300/ 313 TIME: 0.23 LOSS: 0.0755\n",
+            "EPOCH  60: TIME: 72.28 ETA: 2891.34\n",
+            "Epoch 62/100\n",
+            "EPOCH  61: ITER    0/ 313 TIME: 0.01 LOSS: 0.0755\n",
+            "EPOCH  61: ITER  100/ 313 TIME: 0.23 LOSS: 0.0759\n",
+            "EPOCH  61: ITER  200/ 313 TIME: 0.23 LOSS: 0.0755\n",
+            "EPOCH  61: ITER  300/ 313 TIME: 0.23 LOSS: 0.0727\n",
+            "EPOCH  61: TIME: 72.30 ETA: 2819.78\n",
+            "Epoch 63/100\n",
+            "EPOCH  62: ITER    0/ 313 TIME: 0.01 LOSS: 0.0761\n",
+            "EPOCH  62: ITER  100/ 313 TIME: 0.23 LOSS: 0.0736\n",
+            "EPOCH  62: ITER  200/ 313 TIME: 0.24 LOSS: 0.0746\n",
+            "EPOCH  62: ITER  300/ 313 TIME: 0.24 LOSS: 0.0730\n",
+            "EPOCH  62: TIME: 72.22 ETA: 2744.35\n",
+            "Epoch 64/100\n",
+            "EPOCH  63: ITER    0/ 313 TIME: 0.01 LOSS: 0.0739\n",
+            "EPOCH  63: ITER  100/ 313 TIME: 0.23 LOSS: 0.0732\n",
+            "EPOCH  63: ITER  200/ 313 TIME: 0.23 LOSS: 0.0743\n",
+            "EPOCH  63: ITER  300/ 313 TIME: 0.24 LOSS: 0.0744\n",
+            "EPOCH  63: TIME: 72.25 ETA: 2673.27\n",
+            "Epoch 65/100\n",
+            "EPOCH  64: ITER    0/ 313 TIME: 0.01 LOSS: 0.0744\n",
+            "EPOCH  64: ITER  100/ 313 TIME: 0.23 LOSS: 0.0755\n",
+            "EPOCH  64: ITER  200/ 313 TIME: 0.24 LOSS: 0.0759\n",
+            "EPOCH  64: ITER  300/ 313 TIME: 0.24 LOSS: 0.0727\n",
+            "EPOCH  64: TIME: 72.28 ETA: 2602.18\n",
+            "Epoch 66/100\n",
+            "EPOCH  65: ITER    0/ 313 TIME: 0.02 LOSS: 0.0740\n",
+            "EPOCH  65: ITER  100/ 313 TIME: 0.23 LOSS: 0.0744\n",
+            "EPOCH  65: ITER  200/ 313 TIME: 0.22 LOSS: 0.0750\n",
+            "EPOCH  65: ITER  300/ 313 TIME: 0.23 LOSS: 0.0752\n",
+            "EPOCH  65: TIME: 72.27 ETA: 2529.62\n",
+            "Epoch 67/100\n",
+            "EPOCH  66: ITER    0/ 313 TIME: 0.01 LOSS: 0.0762\n",
+            "EPOCH  66: ITER  100/ 313 TIME: 0.23 LOSS: 0.0728\n",
+            "EPOCH  66: ITER  200/ 313 TIME: 0.23 LOSS: 0.0743\n",
+            "EPOCH  66: ITER  300/ 313 TIME: 0.24 LOSS: 0.0738\n",
+            "EPOCH  66: TIME: 72.25 ETA: 2456.67\n",
+            "Epoch 68/100\n",
+            "EPOCH  67: ITER    0/ 313 TIME: 0.01 LOSS: 0.0744\n",
+            "EPOCH  67: ITER  100/ 313 TIME: 0.23 LOSS: 0.0745\n",
+            "EPOCH  67: ITER  200/ 313 TIME: 0.23 LOSS: 0.0761\n",
+            "EPOCH  67: ITER  300/ 313 TIME: 0.23 LOSS: 0.0757\n",
+            "EPOCH  67: TIME: 72.27 ETA: 2385.02\n",
+            "Epoch 69/100\n",
+            "EPOCH  68: ITER    0/ 313 TIME: 0.01 LOSS: 0.0753\n",
+            "EPOCH  68: ITER  100/ 313 TIME: 0.23 LOSS: 0.0752\n",
+            "EPOCH  68: ITER  200/ 313 TIME: 0.23 LOSS: 0.0746\n",
+            "EPOCH  68: ITER  300/ 313 TIME: 0.24 LOSS: 0.0744\n",
+            "EPOCH  68: TIME: 72.25 ETA: 2312.10\n",
+            "Epoch 70/100\n",
+            "EPOCH  69: ITER    0/ 313 TIME: 0.02 LOSS: 0.0734\n",
+            "EPOCH  69: ITER  100/ 313 TIME: 0.23 LOSS: 0.0757\n",
+            "EPOCH  69: ITER  200/ 313 TIME: 0.23 LOSS: 0.0767\n",
+            "EPOCH  69: ITER  300/ 313 TIME: 0.23 LOSS: 0.0741\n",
+            "EPOCH  69: TIME: 72.34 ETA: 2242.65\n",
+            "Epoch 71/100\n",
+            "EPOCH  70: ITER    0/ 313 TIME: 0.01 LOSS: 0.0746\n",
+            "EPOCH  70: ITER  100/ 313 TIME: 0.23 LOSS: 0.0734\n",
+            "EPOCH  70: ITER  200/ 313 TIME: 0.24 LOSS: 0.0740\n",
+            "EPOCH  70: ITER  300/ 313 TIME: 0.23 LOSS: 0.0721\n",
+            "EPOCH  70: TIME: 72.33 ETA: 2170.00\n",
+            "Epoch 72/100\n",
+            "EPOCH  71: ITER    0/ 313 TIME: 0.01 LOSS: 0.0748\n",
+            "EPOCH  71: ITER  100/ 313 TIME: 0.23 LOSS: 0.0753\n",
+            "EPOCH  71: ITER  200/ 313 TIME: 0.23 LOSS: 0.0750\n",
+            "EPOCH  71: ITER  300/ 313 TIME: 0.24 LOSS: 0.0739\n",
+            "EPOCH  71: TIME: 72.33 ETA: 2097.68\n",
+            "Epoch 73/100\n",
+            "EPOCH  72: ITER    0/ 313 TIME: 0.01 LOSS: 0.0728\n",
+            "EPOCH  72: ITER  100/ 313 TIME: 0.24 LOSS: 0.0725\n",
+            "EPOCH  72: ITER  200/ 313 TIME: 0.23 LOSS: 0.0754\n",
+            "EPOCH  72: ITER  300/ 313 TIME: 0.24 LOSS: 0.0747\n",
+            "EPOCH  72: TIME: 72.40 ETA: 2027.31\n",
+            "Epoch 74/100\n",
+            "EPOCH  73: ITER    0/ 313 TIME: 0.01 LOSS: 0.0736\n",
+            "EPOCH  73: ITER  100/ 313 TIME: 0.23 LOSS: 0.0715\n",
+            "EPOCH  73: ITER  200/ 313 TIME: 0.23 LOSS: 0.0737\n",
+            "EPOCH  73: ITER  300/ 313 TIME: 0.23 LOSS: 0.0742\n",
+            "EPOCH  73: TIME: 72.40 ETA: 1954.91\n",
+            "Epoch 75/100\n",
+            "EPOCH  74: ITER    0/ 313 TIME: 0.01 LOSS: 0.0731\n",
+            "EPOCH  74: ITER  100/ 313 TIME: 0.23 LOSS: 0.0740\n",
+            "EPOCH  74: ITER  200/ 313 TIME: 0.23 LOSS: 0.0728\n",
+            "EPOCH  74: ITER  300/ 313 TIME: 0.23 LOSS: 0.0758\n",
+            "EPOCH  74: TIME: 72.39 ETA: 1882.14\n",
+            "Epoch 76/100\n",
+            "EPOCH  75: ITER    0/ 313 TIME: 0.02 LOSS: 0.0740\n",
+            "EPOCH  75: ITER  100/ 313 TIME: 0.23 LOSS: 0.0732\n",
+            "EPOCH  75: ITER  200/ 313 TIME: 0.23 LOSS: 0.0754\n",
+            "EPOCH  75: ITER  300/ 313 TIME: 0.24 LOSS: 0.0744\n",
+            "EPOCH  75: TIME: 72.36 ETA: 1809.04\n",
+            "Epoch 77/100\n",
+            "EPOCH  76: ITER    0/ 313 TIME: 0.01 LOSS: 0.0757\n",
+            "EPOCH  76: ITER  100/ 313 TIME: 0.23 LOSS: 0.0721\n",
+            "EPOCH  76: ITER  200/ 313 TIME: 0.23 LOSS: 0.0727\n",
+            "EPOCH  76: ITER  300/ 313 TIME: 0.23 LOSS: 0.0742\n",
+            "EPOCH  76: TIME: 72.41 ETA: 1737.84\n",
+            "Epoch 78/100\n",
+            "EPOCH  77: ITER    0/ 313 TIME: 0.01 LOSS: 0.0732\n",
+            "EPOCH  77: ITER  100/ 313 TIME: 0.24 LOSS: 0.0722\n",
+            "EPOCH  77: ITER  200/ 313 TIME: 0.23 LOSS: 0.0720\n",
+            "EPOCH  77: ITER  300/ 313 TIME: 0.23 LOSS: 0.0746\n",
+            "EPOCH  77: TIME: 72.41 ETA: 1665.35\n",
+            "Epoch 79/100\n",
+            "EPOCH  78: ITER    0/ 313 TIME: 0.01 LOSS: 0.0724\n",
+            "EPOCH  78: ITER  100/ 313 TIME: 0.22 LOSS: 0.0743\n",
+            "EPOCH  78: ITER  200/ 313 TIME: 0.23 LOSS: 0.0728\n",
+            "EPOCH  78: ITER  300/ 313 TIME: 0.23 LOSS: 0.0760\n",
+            "EPOCH  78: TIME: 72.32 ETA: 1591.12\n",
+            "Epoch 80/100\n",
+            "EPOCH  79: ITER    0/ 313 TIME: 0.01 LOSS: 0.0721\n",
+            "EPOCH  79: ITER  100/ 313 TIME: 0.24 LOSS: 0.0734\n",
+            "EPOCH  79: ITER  200/ 313 TIME: 0.24 LOSS: 0.0766\n",
+            "EPOCH  79: ITER  300/ 313 TIME: 0.23 LOSS: 0.0734\n",
+            "EPOCH  79: TIME: 72.40 ETA: 1520.47\n",
+            "Epoch 81/100\n",
+            "EPOCH  80: ITER    0/ 313 TIME: 0.01 LOSS: 0.0720\n",
+            "EPOCH  80: ITER  100/ 313 TIME: 0.23 LOSS: 0.0733\n",
+            "EPOCH  80: ITER  200/ 313 TIME: 0.23 LOSS: 0.0733\n",
+            "EPOCH  80: ITER  300/ 313 TIME: 0.24 LOSS: 0.0730\n",
+            "EPOCH  80: TIME: 72.43 ETA: 1448.55\n",
+            "Epoch 82/100\n",
+            "EPOCH  81: ITER    0/ 313 TIME: 0.01 LOSS: 0.0719\n",
+            "EPOCH  81: ITER  100/ 313 TIME: 0.24 LOSS: 0.0705\n",
+            "EPOCH  81: ITER  200/ 313 TIME: 0.24 LOSS: 0.0742\n",
+            "EPOCH  81: ITER  300/ 313 TIME: 0.24 LOSS: 0.0738\n",
+            "EPOCH  81: TIME: 72.42 ETA: 1375.96\n",
+            "Epoch 83/100\n",
+            "EPOCH  82: ITER    0/ 313 TIME: 0.01 LOSS: 0.0744\n",
+            "EPOCH  82: ITER  100/ 313 TIME: 0.24 LOSS: 0.0745\n",
+            "EPOCH  82: ITER  200/ 313 TIME: 0.24 LOSS: 0.0721\n",
+            "EPOCH  82: ITER  300/ 313 TIME: 0.22 LOSS: 0.0731\n",
+            "EPOCH  82: TIME: 72.34 ETA: 1302.03\n",
+            "Epoch 84/100\n",
+            "EPOCH  83: ITER    0/ 313 TIME: 0.01 LOSS: 0.0723\n",
+            "EPOCH  83: ITER  100/ 313 TIME: 0.23 LOSS: 0.0739\n",
+            "EPOCH  83: ITER  200/ 313 TIME: 0.22 LOSS: 0.0731\n",
+            "EPOCH  83: ITER  300/ 313 TIME: 0.23 LOSS: 0.0727\n",
+            "EPOCH  83: TIME: 72.35 ETA: 1229.99\n",
+            "Epoch 85/100\n",
+            "EPOCH  84: ITER    0/ 313 TIME: 0.01 LOSS: 0.0745\n",
+            "EPOCH  84: ITER  100/ 313 TIME: 0.23 LOSS: 0.0733\n",
+            "EPOCH  84: ITER  200/ 313 TIME: 0.23 LOSS: 0.0728\n",
+            "EPOCH  84: ITER  300/ 313 TIME: 0.23 LOSS: 0.0718\n",
+            "EPOCH  84: TIME: 72.39 ETA: 1158.19\n",
+            "Epoch 86/100\n",
+            "EPOCH  85: ITER    0/ 313 TIME: 0.01 LOSS: 0.0734\n",
+            "EPOCH  85: ITER  100/ 313 TIME: 0.23 LOSS: 0.0727\n",
+            "EPOCH  85: ITER  200/ 313 TIME: 0.23 LOSS: 0.0740\n",
+            "EPOCH  85: ITER  300/ 313 TIME: 0.23 LOSS: 0.0725\n",
+            "EPOCH  85: TIME: 72.34 ETA: 1085.15\n",
+            "Epoch 87/100\n",
+            "EPOCH  86: ITER    0/ 313 TIME: 0.01 LOSS: 0.0733\n",
+            "EPOCH  86: ITER  100/ 313 TIME: 0.24 LOSS: 0.0721\n",
+            "EPOCH  86: ITER  200/ 313 TIME: 0.22 LOSS: 0.0756\n",
+            "EPOCH  86: ITER  300/ 313 TIME: 0.23 LOSS: 0.0712\n",
+            "EPOCH  86: TIME: 72.31 ETA: 1012.36\n",
+            "Epoch 88/100\n",
+            "EPOCH  87: ITER    0/ 313 TIME: 0.01 LOSS: 0.0714\n",
+            "EPOCH  87: ITER  100/ 313 TIME: 0.23 LOSS: 0.0727\n",
+            "EPOCH  87: ITER  200/ 313 TIME: 0.23 LOSS: 0.0722\n",
+            "EPOCH  87: ITER  300/ 313 TIME: 0.23 LOSS: 0.0739\n",
+            "EPOCH  87: TIME: 72.38 ETA: 940.99\n",
+            "Epoch 89/100\n",
+            "EPOCH  88: ITER    0/ 313 TIME: 0.01 LOSS: 0.0717\n",
+            "EPOCH  88: ITER  100/ 313 TIME: 0.23 LOSS: 0.0721\n",
+            "EPOCH  88: ITER  200/ 313 TIME: 0.24 LOSS: 0.0721\n",
+            "EPOCH  88: ITER  300/ 313 TIME: 0.24 LOSS: 0.0730\n",
+            "EPOCH  88: TIME: 72.32 ETA: 867.87\n",
+            "Epoch 90/100\n",
+            "EPOCH  89: ITER    0/ 313 TIME: 0.01 LOSS: 0.0717\n",
+            "EPOCH  89: ITER  100/ 313 TIME: 0.24 LOSS: 0.0720\n",
+            "EPOCH  89: ITER  200/ 313 TIME: 0.22 LOSS: 0.0729\n",
+            "EPOCH  89: ITER  300/ 313 TIME: 0.23 LOSS: 0.0704\n",
+            "EPOCH  89: TIME: 72.34 ETA: 795.69\n",
+            "Epoch 91/100\n",
+            "EPOCH  90: ITER    0/ 313 TIME: 0.01 LOSS: 0.0739\n",
+            "EPOCH  90: ITER  100/ 313 TIME: 0.23 LOSS: 0.0717\n",
+            "EPOCH  90: ITER  200/ 313 TIME: 0.23 LOSS: 0.0735\n",
+            "EPOCH  90: ITER  300/ 313 TIME: 0.23 LOSS: 0.0715\n",
+            "EPOCH  90: TIME: 72.34 ETA: 723.40\n",
+            "Epoch 92/100\n",
+            "EPOCH  91: ITER    0/ 313 TIME: 0.01 LOSS: 0.0733\n",
+            "EPOCH  91: ITER  100/ 313 TIME: 0.23 LOSS: 0.0724\n",
+            "EPOCH  91: ITER  200/ 313 TIME: 0.23 LOSS: 0.0725\n",
+            "EPOCH  91: ITER  300/ 313 TIME: 0.24 LOSS: 0.0740\n",
+            "EPOCH  91: TIME: 72.30 ETA: 650.73\n",
+            "Epoch 93/100\n",
+            "EPOCH  92: ITER    0/ 313 TIME: 0.01 LOSS: 0.0705\n",
+            "EPOCH  92: ITER  100/ 313 TIME: 0.22 LOSS: 0.0740\n",
+            "EPOCH  92: ITER  200/ 313 TIME: 0.23 LOSS: 0.0723\n",
+            "EPOCH  92: ITER  300/ 313 TIME: 0.23 LOSS: 0.0729\n",
+            "EPOCH  92: TIME: 72.31 ETA: 578.46\n",
+            "Epoch 94/100\n",
+            "EPOCH  93: ITER    0/ 313 TIME: 0.01 LOSS: 0.0727\n",
+            "EPOCH  93: ITER  100/ 313 TIME: 0.24 LOSS: 0.0712\n",
+            "EPOCH  93: ITER  200/ 313 TIME: 0.24 LOSS: 0.0720\n",
+            "EPOCH  93: ITER  300/ 313 TIME: 0.24 LOSS: 0.0704\n",
+            "EPOCH  93: TIME: 72.35 ETA: 506.43\n",
+            "Epoch 95/100\n",
+            "EPOCH  94: ITER    0/ 313 TIME: 0.01 LOSS: 0.0724\n",
+            "EPOCH  94: ITER  100/ 313 TIME: 0.24 LOSS: 0.0713\n",
+            "EPOCH  94: ITER  200/ 313 TIME: 0.23 LOSS: 0.0725\n",
+            "EPOCH  94: ITER  300/ 313 TIME: 0.23 LOSS: 0.0723\n",
+            "EPOCH  94: TIME: 72.43 ETA: 434.59\n",
+            "Epoch 96/100\n",
+            "EPOCH  95: ITER    0/ 313 TIME: 0.01 LOSS: 0.0731\n",
+            "EPOCH  95: ITER  100/ 313 TIME: 0.23 LOSS: 0.0720\n",
+            "EPOCH  95: ITER  200/ 313 TIME: 0.24 LOSS: 0.0714\n",
+            "EPOCH  95: ITER  300/ 313 TIME: 0.24 LOSS: 0.0713\n",
+            "EPOCH  95: TIME: 72.40 ETA: 362.02\n",
+            "Epoch 97/100\n",
+            "EPOCH  96: ITER    0/ 313 TIME: 0.01 LOSS: 0.0728\n",
+            "EPOCH  96: ITER  100/ 313 TIME: 0.23 LOSS: 0.0714\n",
+            "EPOCH  96: ITER  200/ 313 TIME: 0.23 LOSS: 0.0712\n",
+            "EPOCH  96: ITER  300/ 313 TIME: 0.23 LOSS: 0.0709\n",
+            "EPOCH  96: TIME: 72.41 ETA: 289.64\n",
+            "Epoch 98/100\n",
+            "EPOCH  97: ITER    0/ 313 TIME: 0.01 LOSS: 0.0722\n",
+            "EPOCH  97: ITER  100/ 313 TIME: 0.24 LOSS: 0.0728\n",
+            "EPOCH  97: ITER  200/ 313 TIME: 0.22 LOSS: 0.0716\n",
+            "EPOCH  97: ITER  300/ 313 TIME: 0.23 LOSS: 0.0738\n",
+            "EPOCH  97: TIME: 72.39 ETA: 217.16\n",
+            "Epoch 99/100\n",
+            "EPOCH  98: ITER    0/ 313 TIME: 0.01 LOSS: 0.0735\n",
+            "EPOCH  98: ITER  100/ 313 TIME: 0.23 LOSS: 0.0707\n",
+            "EPOCH  98: ITER  200/ 313 TIME: 0.24 LOSS: 0.0713\n",
+            "EPOCH  98: ITER  300/ 313 TIME: 0.24 LOSS: 0.0727\n",
+            "EPOCH  98: TIME: 72.38 ETA: 144.77\n",
+            "Epoch 100/100\n",
+            "EPOCH  99: ITER    0/ 313 TIME: 0.01 LOSS: 0.0741\n",
+            "EPOCH  99: ITER  100/ 313 TIME: 0.23 LOSS: 0.0725\n",
+            "EPOCH  99: ITER  200/ 313 TIME: 0.23 LOSS: 0.0736\n",
+            "EPOCH  99: ITER  300/ 313 TIME: 0.23 LOSS: 0.0715\n",
+            "EPOCH  99: TIME: 72.32 ETA: 72.32\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -978,7 +1829,31 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "ysZAvORejASO"
+        "id": "EP8jkJood6ru",
+        "outputId": "d3abec6b-80ab-4d42-cdf5-812eb0ac67af"
+      },
+      "source": [
+        "tf.one_hot(np.squeeze(batch_y), q_levels).shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "TensorShape([96, 28, 28, 2])"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ysZAvORejASO",
+        "outputId": "9db684ba-7321-476c-8312-a94bf584323d"
       },
       "source": [
         "# Test set performance\n",
@@ -988,24 +1863,33 @@
         "\n",
         "    # Calculate cross-entropy (= negative log-likelihood)\n",
         "    loss = compute_loss(tf.one_hot(np.squeeze(batch_y), q_levels), logits)\n",
-        "\n",
         "    test_loss.append(loss)\n",
         "print('nll : {:} nats'.format(np.array(test_loss).mean()))\n",
         "print('bits/dim : {:}'.format(np.array(test_loss).mean() / np.log(2)))"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nll : 0.08589804917573929 nats\n",
+            "bits/dim : 0.12392468956787539\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "20hQ3iOEjUNX"
+        "id": "20hQ3iOEjUNX",
+        "outputId": "c843674c-52e0-44ae-a6c3-4fddf7f4b2d8"
       },
       "source": [
         "# Test set performance\n",
         "test_loss = []\n",
         "for batch_x, batch_y in test_dataset:\n",
-        "    logits = gated_pixelcnn(np.squeeze(batch_x), training=False)\n",
+        "    logits = gated_pixelcnn(batch_x, training=False)\n",
         "\n",
         "    # Calculate cross-entropy (= negative log-likelihood)\n",
         "    loss = compute_loss(tf.one_hot(np.squeeze(batch_y), q_levels), logits)\n",
@@ -1015,12 +1899,45 @@
         "print('bits/dim : {:}'.format(np.array(test_loss).mean() / np.log(2)))"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nll : 0.08589804917573929 nats\n",
+            "bits/dim : 0.12392468956787539\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "q_ricPssd6ru",
+        "outputId": "0d85a004-c2f7-490f-8827-f0931143f41d"
+      },
+      "source": [
+        "width"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "28"
+            ]
+          },
+          "execution_count": 36,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "P2_qYR4TjXgD"
+        "id": "P2_qYR4TjXgD",
+        "outputId": "3c3b889e-e619-4aec-bfcd-8235fec7c0f3"
       },
       "source": [
         "# Generating new images\n",
@@ -1038,15 +1955,27 @@
         "    plt.xticks(np.array([]))\n",
         "    plt.yticks(np.array([]))\n",
         "plt.show()\n",
-        "plt.savefig('numbers1.png')"
+        "fig.savefig('numbers1.png')"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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42cWU5BYefNzLSq+vr9MGfVdaYpPdbyXZye1PsouTXYy+Lk52cbKL09fFyS5mS25uOAcAAFIYfAAAUNWXL1+mvfmadQYfAABAivA9HwC05dks4+12O+GVADN61gd9/G/6I5ZF5QMAAEii8gEAE3qcpTYrDWRQ+QAAAFIYfAAAACksu2IqbsilFTVvwrSdJVtpKzCu0uv7rO8/Kh8AAEAKlQ+mZxtAMtWceV47lrbMVtoKR8pqX/f+cMb2HP1cOeszROUDAABIMW3lY+socYYRdGkW0RH2DFmOqJc1pC37LMOSGbvZqh366jrc61FmT7t7vJ5nuGaz25f2fHwGR1aSVD4AAIAUBh8AAECKaZddrRmlDPqZ0pLdlp+fJbsZ7L2BTVvYbu9yK/Ly6aldazPlan4ubjlW70uyWmpjPeRVw5FL358d+4hNeVQ+AACAFNNVPnqfZTiaDLiLtoX7NVZjtsT1uo0siFi7IVqbOl/v28Af8ZpbqrRky1h98vH3j8xa5QMAAEhh8AEAAKSYYtmVpRu0ZPQlDs+Wcjyec43Sbu83tm993bP3X3vOcdYlGlvPe8vN0jO0sY/OPN8R2utR+c3eD36mt6fIq3wAAAAppqh8sN3aDDWUWKturFV/9mx12VN7XXvdI8yAtqCkPcyY+Vo+jxtH9HRt9SraB85Iezw+gyPbo8oHAACQYujKhzWC655lUDJDzX6zZFmypWeNeyF6s/e+l7tZ2hPrjt7iVDvjSCP17TWNdN2pfAAAACkMPgAAgBRDL7uinBveyFZyQ+tn7XL2m2MtkzzGjBk+fhbMmAHljv7uoB2OQeUDAABIMV3lw6h5m7UKiJkworZsclD7+C2o8VDFaGXI9bpOlfc5ucDcjqykq3wAAAAphqx8mLGhRdrlurUZ+hGz21oNKZlp2vpgR1UQnhnxOutJj/m7T5QIlQ8AACCFwQcAAJBiyGVX1FPjRtmWjHY+Iyp9X3pbQrTl9fZ2TqOa5X2oteyPunrKfuvyq7Vz8pl8vqzt2lU+AACAFCofg6q1vaaZiP0eM+xpNitD9IZFOa5zI+g6uah4tGC0dljabrTBfC20OZUPAAAghcpHZ0pHrFt+vnRrU7MRnGWktpe1thaWJf5ZwDFm/YzVDo9Rq5qRlb3KBwAAkMLgAwAASDHFsqvRS3h7nwjdws1HzGu29jfb+fZo9M+Mfzm6bc6a60ezLre6m+Ecj9Lb0qo1Kh8AAECKYSofs8wm1niQz93sN36NfG6j+thmR3r/RjqXXo3+HtTcApUys1c72KbmNdd6u1L5AAAAUgxT+ZhNjVFt6yNj5rT3HqbWue7a4H1Yd8QDams9/LYXKh48qvFZNkLbUfkAAABSGHwAAAApLLsCTrV1EwXKPOY5Qqmefriun3Mdzqn0Ohi9nah8AAAAKVQ+gKaYKY2THa35OIN7b58tbpkdvXZG3yCDOp5VAltp+2dQ+QAAAFIYfAAAACksu4IDKLsfY+Yy9RrPE4DPHdEvbzmma5A7beEXlQ8AACCFyken3LTUH+/Tc2YO41TY4Bhb+5wjqo7PjqkPZCQqHwAAQAqVj86Y6WQE2nHc1uzMlPJMdtW8pWu9xWuipXwgi8oHAACQwuADAABIMcyyq2dPUYVsbhRc59qMc2M+tWlTcVuy09/Bc+HBx71Den9/r/ZijnLUa7wft7Rzrp1dD+/Bo0h2PbW5j2q/3pmyu6t9rYya3eh9XY96z+7ov792/N6zq6n0XEbv644ku5iS3MKDj+v1uizLsry9vUUPkebbt2+HHv96vRb9jdrZHX1+RyrJrqc299FR788M2d3VznDU7Ebv63rWa3ZHt6ktx+81u5qi78OofV0G2cVsye3LLVhT/fnz5/Ljx4/l5eVl2tLi7XZbrtfr8v379+Xr1+23z8gulp3cfpFdnOxi9HVxsouTXZy+Lk52MSW5hQcfAAAAJex2BQAApDD4AAAAUhh8AAAAKQw+AACAFAYfAABACoMPAAAghcEHAACQwuADAABIYfABAACk+C/6ix4lX/Yo+Y9kF8tObr/ILk52Mfq6ONnFyS5OXxcnu5ii3G5Bl8vltiyLf8tyu1wuskvITm6yk10/uclOdrI7/5++TnYt5haufLy8vCzLsiyXy2V5fX2NHqZr7+/vy9vb2/+y2Ep2sezk9ovs4mQXo6+Lk13cmdl9+/btr//2f//3f6FjnUFfFye7mJLcwoOPe1np9fV12qDvSktssvutJDu5/Ul2cbKL0dfFyS6ulex6fB/0dXGyi9mSmxvOAQCAFOHKBwB1PZsxut1uJ7wSmNvH627WG4jhKCofAABACpUPgBOYTYV2uT7hOCofAABAimEqH1tnKayfhno+XneurX8ziwpAqSPuA2zhc1vlAwAASGHwAQAApOh22VV0GcP992ZbIlJz2cds2d1F285j9qPmN+u1dQQZHqO0Hxzpfdhy7iOdL/TsiKW6LS3/VfkAAABSdFH5OHIEOMJMT/ZodqTsPvMs2715e5DcPGaeac/mc4IeRNtpj21Q/9eWlvJV+QAAAFJ0Ufm4j9ZqbKfb0pq3PWqcx5ZR8Ch5lTqrmrQsbc1O/EsL1ZuRZqVlFzdrH7VFC1vQ99a3HaFGG+3lmt1zrmu/2/p5ZxgpA5UPAAAghcEHAACQootlV3dHlJxmKQmPfG5HKF3qV+vvsa6XJTYtLh8Yoa87Mtde2lYv1jbr6LX91bR3y/bWtP76ejBThiofAABAiq4qH5SLzjC1OHN7hpnOleOc2Y56n0377PW7Rvsx23u1d2OOHq7dHl4j7VH5AAAAUhh8AAAAKaZfdtVbGfjofduVUGlRD+2yhdc44nLJj6+79CbmFt6TM7TwfI/ePWt3WZs2tPAcpZqi12bP51xixH77MyofAABAiikqH2ujyhG2n7xT7WA0M84IRcxyDa9tgT1LBq2b5X04ouI2WnYqHnGjZ6DyAQAApBiy8lEye9DL6PKIGWAzEJQYbVauts/uTdir9JgjX7s1sl7LR1vnoyMrbqq7v812vjP3MyofAABACoMPAAAgRbfLrmYuVy3LvvLk7NnRr5bL8ls3tjhSy/kcpdY5j7a96b9kn9NIGdZY7jfTcmffNfgXlQ8AACBF05WPI0bNvc4omK2iRUe0k15ny46+4fyzvwln6fWa3aPWhgWzX8Ozn/9HM2Wh8gEAAKRorvJRcwZlplHkZ7bmKjOyjbgGem1rzuixyLf2IDlsE/svM517tK+bKaMeZN/zpvIBAACkMPgAAABSNLfsqpTS3boRl7SQq4WndffKtcWIZrl+qU+f+FvrG7YcuRRL5QMAAEjRTOXDzUp1qXjQkpL2aFYV2uGzmS2873myrslnf6fWJhwqHwAAQIpmKh9bGFmvs51u20Z6f/Y+YGvrOfaQBWM44yGRR1mrInp4L/RjpO8NH6l8AAAAKQw+AACAFM0su1orE/dWTsrm5vJ29L5cY4+9TzvuPTt9F7PRvuEYez9Pj2KrXQAAoCvNVD7uzKTUJc/63LC5zYjnxBy03b/JBM6VcQ1mrUBQ+QAAAFI0V/lgm89Gp2ap6ovOCDzezzTCe9PqetQzPduq1X0gnElbA9acdc+wygcAAJDC4AMAAEhh2VVnet+OtDc1ny5qCcRzI7bpx/f64zlaihUnO4DP7V0mfjSVDwAAIIXKR2c8jPF8cn6uVi4j5vvsnJ5VQ0Y8970+m8GTHVE1K9vQo7PatsoHAACQwuADAABIYdlVp5SBc8h5fanfRyXLXywbXH8uyGxZ1FBz4wL581Evm2LM1G5Hf9bUs8/dXtrhFiofAABAitTKx1lPUgTylM7OuOa3V5dm9NnN+kccnzk8qz62RNv8W4vv05G2ttHoioOzqHwAAAApmrvnw9Z30Kaas4Su33Uf85XV39Yy8RlCRGl7sJLjc9F72FqaoW/J3vbUUoVd5QMAAEhh8AEAAKRobtkV0L5o+Xb2ZQifaaks3ittjAza2XPP+q4j+rOjN6IYUUv5hAcf9zf+/f292ospcdbfffYaSjuhs7NrQSQ7uf3Sc3at/P2esjs7s4+vQV9XTnZxsovrsa8rsfYa977+0bP7qObrLcktPPi4Xq/LsizL29tb9BC7fPv27ZS/+8z1ei16PWdn15KS7OT2px6za+W67Sm7VjJbFn3dHrKLk11cT31dibVzqtVnjprdR0d8vmzJ7cstWDv8+fPn8uPHj+Xl5aWpUk6m2+22XK/X5fv378vXr9tvn5FdLDu5/SK7ONnF6OviZBcnuzh9XZzsYkpyCw8+AAAAStjtCgAASGHwAQAApDD4AAAAUhh8AAAAKQw+AACAFAYfAABACoMPAAAghcEHAACQwuADAABI8V/0Fz1KvuxR8h/JLpad3H6RXZzsYvR1cbKLk12cvi5OdjFFud2CLpfLbVkW/5bldrlcZJeQndxkJ7t+cpOd7GR3/j99nexazC1c+Xh5eVmWZVkul8vy+voaPUzX3t/fl7e3t/9lsZXsYtnJ7RfZxckuRl8XJ7s42cXp6+JkF1OSW3jwcS8rvb6+Thv0XWmJTXa/lWQntz/JLk52Mfq6ONnFyS5OXxcnu5gtubnhHAAASGHwAQAApDD4AAAAUhh8AAAAKQw+AACAFAYfAABAivBWu71a2wLsdrslvhIAAD7yPW18Kh8AAEAKgw8AACDFMMuuSp+AejdzCU9mAL+U9of6Qdgm+l1j67Fci/1R+QAAAFJ0W/koGUnPOCquOdOw5dijZjzTuR7pMUcZstXR12C0r/z4e9oz/O3I7yHP/o7rsB8qHwAAQIquKh+2X+MIWbMzs9iS59bMR76us/qzXmcFXZdAiV77ujWjflaqfAAAACkMPgAAgBRdLbt6prdSU5ZauYy89KF02cvaz9sAYf8yolnbWo2fn8UR1879mDKvY8SlL/Sl180gZuqDVD4AAIAU3Vc+OE9PMwp3pdt21qp2/Ov3eszwI1sRr2thJqu39+OszKL9wKhmPGfq2lJVrNE/9d5WZ9xMSeUDAABIYfABAACk6H7ZlWUfx+i9jPmoZjuJZjNSu9yb52jt61Hp+e1tGyPnOdJ105oj2s0o71fp8ljfRf7t6BxG3DRi7/eT1tueygcAAJCiq8rH1u1PZ7x5J9NMGX481y3tauTZr6xzGyWvj844p55yHPm6aY3K7brS7xSlx5olx1b0UgnYq7eqj8oHAACQoqvKxzOl2yPOMgqO6m30XGrP+/5Y3Xh2LO1q3ejt6+6sdqD98S9ZKwJ6/4zd+rr3VkN6zQdqUPkAAABSGHwAAAApul92tWbtJmClz9+2lo9nz2qWJUOPatw0Gb1Zn3UjZ9bCxiEtvIY9PmsftniOK13yDfym8gEAAKQYuvJxt7ZdaumDhFpRo1phdqaMGfoyvc8ak2frltbPfuaIttR72z369feeD4yg5+8iKh8AAECKKSofH/U+e136uvee5yizWDXPY5RMzrD1QaH82+gPLotun36kEfKteZ/W3mMzl9H7rL2efS/d26+1vqpH5QMAAEhh8AEAAKSYbtkVcIySm1AttSIiewONFpcrrKm5VIMysvubTMqUbryxVYsbRKh8AAAAKaarfDyOAHub2TqaPNjiiI0PtD322FsVGb39PXuwrpnp/WRYJvs66/W6jvZnvZyvygcAAJBi6MrHCKPDR0dsFdxrFuQ4entq7W8bM6xxs7SxLddq9HPRdqnbrb0Po2d2Zj81erYjUfkAAABSGHwAAAApulh2ZYnRMWRBic/ay4jLHFsnV56p2S5mXDq0lyWSf9Je6hjpM1blAwAASNFc5UOVY5uRz40+aZMAc1LtoYTKBwAAkKKZysdIa9kAoGdmsuuZ9TvM3vOOtsFZ8+6JygcAAJDC4AMAAEhx6rKrz0pqSmcAcD6fx89tear8yGqe96wZrhn1lgSVDwAAIEUzN5zf9TySg2dKZ3NcA3z02H60D+jLx2t49Ou39PxUO54bteJxp/IBAACkMPgAAABSNLfsaqbyJH3JKg/f/472D8CItnyebv0MHGXp1kybMKl8AAAAKU6tfIw0imNMo8yo0BftjjNod7TAd8PfRs1C5QMAAEjR3D0fMIKPsxW22qUG7YKjPeu3tLv9Rs6w5ta6I+e0ZsaKo8oHAACQwuADAABIYdkVPBEtgz4rG9//24ylVcpoI7Ri1iUwUfI6X2/vwcxL0MKDj3sw7+/v1V5Mb+7nXtpIZBfLrofcary2z44xanYZes7uzL+vr4uTXZzs4nrs6/b+3Vqvu4Xsemy7JbmFBx/X63VZlmV5e3uLHmIY1+t1+fbtW9HPL4vslqUsux5yK2kHe48xWnaZesyuRtvaS18XJ7s42cX11Nft7eNq95FnZtdCfx+1Jbcvt2Bt5+fPn8uPHz+Wl5eXaZcK3G635Xq9Lt+/f1++ft1++4zsYtnJ7RfZxckuRl8XJ7s42cXp6+JkF1OSW3jwAQAAUMJuVwAAQAqDDwAAIIXBBwAAkMLgAwAASGHwAQAApDD4AAAAUhh8AAAAKQw+AACAFAYfAABAiv+iv+hR8mWPkv9IdrHs5PaL7OJkF6Ovi5NdnOzi9HVxsospyu0WdLlcbsuy+Lcst8vlIruE7OQmO9n1k5vsZCe78//p62TXYm7hysfLy8uyLMtyuVyW19fX6GG69v7+vry9vf0vi61kF8tObr/ILk52Mfq6ONnFyS5OXxcnu5iS3MKDj3tZ6fX1ddqg70pLbLL7rSQ7uf1JdnGyi9HXxckuTnZx+ro42cVsyc0N5wAAQIpw5QOAbT7OBN1utxNfCQCcS+UDAABIofIBABTbsrZbpQ94pPIBAACkaLry8TirYgaFLPe2p81Rm7bFGZ5VKaJtcNaHqLGf/o9lUfkAAACSGHwAAAApml529ch2lRzNcgKA3/b2ib19bkfPt+a5rb2GHjJ81Fsb4HgqHwAAQIquKh9A+2re2DqKj+evukYr9rbFZ9d1r+27dNvgx5/POu+eqgi9tgWOp/IBAACkaK7yMdpaR5jF2rVre8W/HTGDqf/cbrat3O/nV2M2epSs9lwva/9/dhVklPeDHC2sTlD5AAAAUhh8AAAAKZpbdkXc1lLv3vJajZLy//3f/+0+Rq+y3qcMNdqCpQMcpaR99nQj7x7Rm8S3ZrK2vKu1a/3ZDeQ1XtsR59fLzdstLOmhvL1kL9lV+QAAAFKofHRqzyxILzMoWVqbqelhlmjLLMksM8lnqjlj3bua/dpsN6OvKT33Xj9fenyPW3rNvb7vLWitIphB5QMAAEhh8AEAAKSw7KozrZY2S8uF7+/vB72SdvX+DIbP2t7jOWzdB7+Hc29Jq33AWeRxrLUlIaXZu9bL9NC2e/9cy7bleVjLEs8uek1mtzWVDwAAIEUzlQ+j5/3ktN/MWyT+S81rs/csjrLlhsNodq1tqNCj0St1W2Y/o+1vxLyO1vP3odZf39mebe/87P8rtXfzkewKiMoHAACQopnKB9uYVajnyBF+zzNXR1Dx+G3LQ9iO1vvWjkfca7D1mL1nt2bEc6oha0vrXvrJM6qpthWPq9n/1aLyAQAApDD4AAAAUpy67Kp0605oSS8l8iPsvTZd23W2Lt1yzLVjz3IjdenPznxtz27rktnHn4teS1vbWkvXZwtLrNZ+r6Ws1jwuwd3TH/dyzncqHwAAQAo3nDOVPTfK1ZoN7WW2ecv51tz+NarlDEuNdC61eJAdGUo3CYluTerG6T8dsZX7CNuLP6vi1Nya91/HrH38f1H5AAAAUpxS+bANKaM54uFwLRvxnEbnnobtSh+653NrPEc/8K3G3+nZEd8Dsx+UV0t0+/WtfVCLeah8AAAAKQw+AACAFKnLrrKf3ltq1vIn/zbzEsEtJezRM6Af2iKl9nwn8T2jviOz6WGZ5J7t12t9v87KRuUDAABI0cxWu9Et1SBq701ts5jtfOmL9klU6SYMtn3e74jtYkd2xMNot/6dI6l8AAAAKZqpfKypubZSxYQttBOgN+5DqGdrlrKLOTK30bcVj2b37MGFZ1H5AAAAUhh8AAAAKZpedrWlXLa3fNRC+YlzHFmCHLHUCy1qaSnB2aJPeLaddjm50JuW2qzKBwAAkKKZyseRW9iNOAtty798tTL0XnCGXvvB0W8ezRbdXnb0fmv08+O3Wdp0y1Q+AACAFKmVj+h61Mff32rEdazR7LJmDFvP9dms38ds9r7+EdscbRjhwVK96L3CsvV9Xfu53jOgHdoSj1Q+AACAFAYfAABAilNuOD9iO8DSv92bvct5ssqeNZcwnWEtJ6VjjlKyrfizn927pLUXa+e59ybS0bPbq/e+nXm4ltun8gEAAKQ4davdo7ZQnGFWptVzbPV1PTPLbDHn29rGtlw/n22asOfYM8raAKUH+kKOsNZn1biOPHqgPyofAABACoMPAAAgRTNPOC9dAjND2WztxtKjzJDro2fnvHcphuULLMsxT9LVR67bemO05Va/WbbHWUo3MnDdjkHlAwAASNFM5ePO6PQYW2/uP2Kmtkeznz/tMCv9t9L+rObfG4kKLdm2bJd9xN+jLSofAABAiuYqH/x21FbE0Jte73M44oF3rZ3j2Y68z2qkrG1HSktq3mu5dkzapPIBAACkMPgAAABSWHY1MSVKWvBYat+z3LDXDRMss2rLKJlbakVPtL95hAcf90by/v5e7cX05n7upRfMnuxq5n3mexfJrvc2V+t1j55djdf4r2P0mF0L79kZfd2Zjuhne8iutfepp+xa02Nf1wrZxZTkFh58XK/XZVmW5e3tLXqIYVyv1+Xbt29FP78ssexK/k7msaJKsuu9zdXOe9TsauT02TF6yq6F6/Qus6870xGZ95BdS23tox6ya1VPfV1rZBezJbcvt2Cd6+fPn8uPHz+Wl5eXaXdiut1uy/V6Xb5//758/br99hnZxbKT2y+yi5NdjL4uTnZxsovT18XJLqYkt/DgAwAAoITdrgAAgBQGHwAAQAqDDwAAIIXBBwAAkMLgAwAASGHwAQAApDD4AAAAUhh8AAAAKQw+AACAFP9Ff9Gj5MseJf+R7GLZye0X2cXJLkZfFye7ONnF6eviZBdTlNst6HK53JZl8W9ZbpfLRXYJ2clNdrLrJzfZyU525//T18muxdzClY+Xl5dlWZblcrksr6+v0cN07f39fXl7e/tfFlvJLpad3H6RXZzsYvR1cbKLk12cvi5OdjEluYUHH/ey0uvr67RB35WW2GT3W0l2cvuT7OJkF6Ovi5NdnOzi9HVxsovZkpsbzgEAgBQGHwAAQAqDDwAAIEX4ng8AAOp4XCt/u91OeiV9WbvHoKcMn51HT6+/hMoHAACQYujKR40HvYw66gQAnts6C33kA+U+Htt3kT+N9CC/tXMZtQ2ofAAAACkMPgAAgBRDLruKluNGKmkBQIlRbtw9ykhLfXo1y3sw+vWm8gEAAKQYpvJhxoYj2LQAYNwbX/fam4XvLtvIaSwqHwAAQIphKh8cb4QHIJ2xXnSE3DLM9IClEmb8ymhHQA9muX/lGZUPAAAghcEHAACQwrIrVo2yhGHm8mZLSt+HWZesbc3Jkixq0D/G3bNzvf1t7/eHUb5/bDXyuT1S+QAAAFKofDC9o2cbHmdvZtyycsvM6rMszMjGmZGlptna0dH90ahVS302W6h8AAAAKQw+AACAFN0vuxq1dHm2GW70Gu18WhFdYrX2c7OV8j/ms+Wm+7V89JFQx9p1OeNy2kwyHYvKBwAAkKL7ygfPudn0l6wZ89lm5qNK26Nc/67+PJthnbVCBGdZu+YeP3+3Xpezf17f6cfWtZRPtM2qfAAAAClUPganAlJPS7MNLap1b4Gcn9uS4Z77QfQR86l5fxZ/k+/fot9JPHB2LCofAABACoMPAAAgRbfLrvYu8ZhhK9llUdpcc1Y5c6RMs7ZxHSmzbG5Gh+OULnXUl/2ytz8a8TtcT3303qxVPgAAgBTdVj6iRp7Z72nUfCQ5HK/mzJ73a7+1WcBZZmG1o23kdLyRrqtSpZWgmmbZYGeE81P5AAAAUkxR+RhxbeBHo59fa9ay3TLD0+v2pjVmr0qP0VM+a456z6PvySi5fmaW8zyC7KjliPtiRqjgzfwQSpUPAAAghcEHAACQYphlV1vLeqOUrT4r081y49UzH895b2m2NL8RtzWNloRHyiDqWQal/ZIcgdHs/W5S83P+bFu/N4y0YZLKBwAAkGKYyscs20mW2jLL2vuswZoW3/sWX1MNtdrRCPmUZnFkhW6EPNlv5H4eRlB6Y37P33tVPgAAgBTDVD6eaX3kF5G13emI2bWil612a86UzlRtW5a/1/CeeR8SbKVtQVui9wO2XhVR+QAAAFIYfAAAACmGXHbVQknpaDXOcfSlL5wjWiYe8bp9dk4jnueZ9GP/JhtGpW2X36C+5WeyPp9UPgAAgBRDVj5GdvSo1KwsUdoOLdAOy8mMXrQwa9+yvQ+szdoQR+UDAABI0VXlwxq/OuRINrNVHEX7AfhcaVVky0Oqo1Q+AACAFAYfAABAii6WXVkmdCzLFnjm8SndRxwbAD5j6e6xjvy8f0blAwAASNFF5WMLI9/tZHWukfLfu60fUN/Wa2+kvojxqHbk+5jr2k3oe/NX+QAAAFIYfAAAACmaXna1pXSs9EaLRm+Xo58fLMux+9yfpffXz/hmXW6V9XTxFqh8AAAAKZqufADnG30GBh49zry6BuAYNkd4rtaN3dG/+y+ecA4AAHSl28rHbKNg6ni2pjK6Jaw2+JwtdumJ9spnZr0H4Uju6f3bs21u176zRPMp7fOOeB9UPgAAgBQGHwAAQIrmll3VKIGXHGO2st6s1p7UWfOY2hP0YctSGkuy+IzlQ2Xktc2zPugxu6P7pyPfh/Dg4/6i3t/fq72YErX+7p7j3H+39A06O7sWRLLrIbeM1zZCdmf3Gz1nd4bZ+rrH1zvS50RP70Vr2UWd8Tp67uta+fs9ZldT6bmU5BYefFyv12VZluXt7S16iF2+ffvWzHGu12vRcc7OriUl2fWQW612uUXP2WXm9EzP2Z1plr7u8RxH+pw4+9qLaCW7qDMz77Gva6WN9phdTdH3YUtuX27BusrPnz+XHz9+LC8vL9OWpm+323K9Xpfv378vX79uv31GdrHs5PaL7OJkF6Ovi5NdnOzi9HVxsospyS08+AAAAChhtysAACCFwQcAAJDC4AMAAEhh8AEAAKQw+AAAAFIYfAAAACkMPgAAgBQGHwAAQAqDDwAAIMV/0V/0KPmyR8l/JLtYdnL7RXZxsovR18XJLk52cfq6ONnFFOV2C7pcLrdlWfxbltvlcpFdQnZyk53s+slNdrKT3fn/9HWyazG3cOXj5eVlWZZluVwuy+vra/QwXXt/f1/e3t7+l8VWsotlJ7dfZBcnuxh9XZzs4mQXp6+Lk11MSW7hwce9rPT6+jpt0HelJTbZ/VaSndz+JLs42cXo6+JkFye7OH1dnOxituTmhnMAACCFwQcAAJAivOwK4Fl59Xa7nfBKxiJXAEal8gEAAKRQ+QCKmZkHACJUPgAAgBQqH/yhZGs5M91zUe04zqxPxC21lpO2SA98xhK193Oipfak8gEAAKQw+AAAAFJYdjWhWks8Ph6npXIe9GDrdejaOsY9f/lytOhnrqWuczl6+W1L7UnlAwAASDF05aN0FDnijEJ0JL2WxbNjmkX800iz2i3NloxgS9uQ7281ZwPd2E+m6AYJa79nxcF4svulFtqNygcAAJBiyMrH3vWVLYwKa7mfS83ZkmfHnFGN8x+xzfG3kSphrYlmtjfrXiuCzz4L9EN5tmQ862esbYjL9JyBygcAAJDC4AMAAEgxzLKraAl87ebprcfowSjncSYlYUpZbnWM0ryOXr7S6vv32Xk//v+eIP9bjWV1tZY4/+v19OiIrf7vRmyjR5xTC9mpfAAAACm6r3y0MIJjPLZD5WhZ7cfNxHWNMgO9LH+3iZHOjXOc2Yb0db+1fi2rfAAAACkMPgAAgBTdLrt6LCnVLLMp2c3Lcqu/Pdtz/sjy9gg3vWafw5Z26+b3uN6W945wDbUoux20vnTmo55eayuOuE57eR9UPgAAgBRdVT5qzjr0NpPFccwSbvNs28ejr8lR1GhHI+fTE30CHx1RBe7pWq/5WqOPR5hVNIsW+jCVDwAAIEVXlY+PzK62pYWRdE2jnU9tz+4DuZv1Gttz3rUy027rmrUt87nHPjDr4cQtXOPZ1Y4ZPauojdQfqXwAAAApDD4AAIAU3S67KuGG4rpGKf3ZdGC/Z3kd0T5Gel8sWdjv6CUuR27lPosRnzb97FzWtiIfTel5Zb/3vbW1teXLd6N+Xqh8AAAAKbqofERHfioex5Ihz5RWQ7bM/vTOA/7iatxwGZ2d7vX9OOKaqvE53GueLWghuy3t6qjXOfLnw4xUPgAAgBRdVD62MLP4p1prlmeYbZilTZxphoy3ztDPkEWmknsLZn6P1s5phn6+1NY2MFs7Wpbjz6v1e0tqK61UZt1reSSVDwAAIIXBBwAAkKKLZVez3Sy4RWmJbdTt2kr1VpqcwYjLFnp93a1bW55QunW29yiHnLH9eh0jnbPKBwAAkKKLykepkUaHd9EbsPbOOIyYJedTgWKPrTeOz/6wwK3nu/fzYrZc2UY/f6yeN1pS+QAAAFIMU/locWS3V41R7Yi51OYhWHlmv1eL+h7bS0kl5Nnvz2zmrYjpy4yf2yWVpNYzUfkAAABSGHwAAAApul121XpJ6Siznnctny0rKHlaMnXJnBpGePovY5j986TmebuGx6LyAQAApOii8jHriHfW2ZIspQ8se/w9tpv1GqYNtbYeBzjKnqptb99LVD4AAIAUXVQ+7nob2dEHa8TP5brmaK5nStVsMzNuC0sdo7YXlQ8AACCFwQcAAJCiq2VXnk5Llmc3qGprZdy0z9m0QeiX63dcKh8AAECKLiofW7ZENQrmCEc8JElb5WwjzyiOfG5nmi27zx5I++znqMMGEeNT+QAAAFIYfAAAACm6WHZ1t7UMCpzHEoV8RzyToLf3SrurS2a/yaK+aJ/lvRiDygcAAJCiq8rHR483odsSFZiNCrCKB/RobSMhxqfyAQAApGim8uEBgtA3M9D51mYPR85cWwPol8oHAACQwuADAABI0cyyq6hnyw563SqyBZYzAK3SP8FYSh+h4PvdGMKDj/sb//7+Xu3FPNp77CNf28fjl14EGdkdoebrjWTXa26ParXrnrJr5T3rMbuos6/Xjz+fkV2r708P2bVKdnGj93UZ3z1Hze4oJbmFBx/X63VZlmV5e3uLHuJT3759O/X3t7per0V/KyO7IxyRZ0l2veb2qFaOPWWXdS1u1VN2UWdfr/efX5ac7FprY49azq51sosbta/LuN5Hze5oW3L7cgvWrn7+/Ln8+PFjeXl5mXaf5tvttlyv1+X79+/L16/bb5+RXSw7uf0iuzjZxejr4mQXJ7s4fV2c7GJKcgsPPgAAAErY7QoAAEhh8AEAAKQw+AAAAFIYfAAAACkMPgAAgBQGHwAAQAqDDwAAIIXBBwAAkMLgAwAASPFf9Bc9Sr7sUfIfyS6Wndx+kV2c7GL0dXGyi5NdnL4uTnYxRbndgi6Xy21ZFv+W5Xa5XGSXkJ3cZCe7fnKTnexkd/4/fZ3sWswtXPl4eXlZlmVZLpfL8vr6Gj1M197f35e3t7f/ZbGV7GLZye0X2cXJLkZfFye7ONnF6eviZBdTklt48HEvK72+vk4b9F1piU12v5VkJ7c/yS5OdjH6ujjZxckuTl8XJ7uYLbm54RwAAEhh8AEAAKQw+AAAAFIYfAAAACkMPgAAgBQGH0BVX758mfYhSwDAOoMPAAAghcEHAACQIvyQwZ7sWQJyu90qvpJjfTzPnl43Y9Ie66i1hG2k92BrJiOdM8AoVD4AAIAUp1Y+3JRax7Mc7//NzB+0Sf93vLWM9Y1QX2m/5jqM67l/U/kAAABSnFL5aHXGr/WRItCXGn3dWr/Ual96lGfnu6XfVh0+pq2Uts3esx7xnGqJti/3Bm5XknHruap8AAAAKQw+AACAFF1ttdti6ahVsvrlsUz5WS5bypqy5TMttSPt9XcGsy2/OnpZXunxe816tuWNJY74jGX8nFQ+AACAFM1UPnqbCZlFzdF3C+/xmbMJvc76sd8Z77l29rePmYw8s5j1EMYaNxnXei21jdw+jrD1/VurQs6uxnXbS64qHwAAQAqDDwAAIEUzy64oc1TZOlqya6lk3lrZ8fH1tL7/Nvsd8b62dBM7/dJG1rV2k/7dDO/brEuTe35SeZTKBwAAkKKZyseMI7+ImrMyZlJjWqusgOt0mx5udO5B9LNjxPyzPg9arQq09nrog8oHAACQ4pTKR+k2YSPOltRUM4sRcq15DqocnE1VmFI1tzPdWyHv5fO7l77ePYNzGP29VfkAAABSGHwAAAApmrnh/G7rE2gf/7/RS1ScY5YnItMWbe25Z8uJttyI28vSnwxry3ZqPrH8X3+DfxvhydWUq7FMsrfvxCofAABAiuYqHx+VzAKMfhPWEaPa6CwicB7X5vOKpK3D//ZZ5fbI2fTesq5VBXp2rGdqfNaO9nk9ynlElZ5/z9UwlQ8AACBF05WPNWtr5EabDTianP5WOqNQMtM1uprbfM7G2vlyWyrksiu/LmfP7Ojznz1f4kb4bFX5AAAAUhh8AAAAKbpddnVniQd7RdtOjbL5yMtCRjwn+qIN/k0mnMl3tbiRNtVQ+QAAAFJ0X/mAEjVnXbY+uMxMD8+48RcY2Ugz9WcZ9XNC5QMAAEih8tEwM+b9OWKLXuajXQCffZ602E+MOlOfbfSqkcoHAACQwuADAABIYdkVU2hpCVvPpVL2a6ktAu3psY+w3Gq/mTJU+QAAAFJ0X/nocYZgq4+j25HPM0MLD6McYbaCmJlmtIDj9NRH9PRaezBSniofAABAiu4rH8+MNDqkrs/axn2GekulRDvjMyoewJpR+ojWX18PZspQ5QMAAEhh8AEAAKTodtnVY6lypnLVsvx5/rOd+5Ees5Qte7Sw0QHQntGfYA1rVD4AAIAUXVU+zB7C3J71AT3MDvbwGoFjlH530V8wOpUPAAAghcEHAACQootlV561AAD0xHIreE7lAwAASNFF5cNsQFtbdvZ60y85Hp8SX/OYH2lzQItKPqf1Y8xI5QMAAEjRReWDNrRQdaFd2gf05axrdsTZ/q1ZjnjuUErlAwAASGHwAQAApLDsagBH3OC7hfIxcIbocqGZ+6yWlkWOtIHEllx7Pbde7G3b3p984cHH/c16f3+v9mJ6cz/30oZ7VHbZ78WevxfJTpv7pZfsjv5bkeP3kl1rWuvros54HaNkd7Rn5zlKdr20u9Zyy/J4vrKLKcktPPi4Xq/LsizL29tb9BDDuF6vy7dv34p+flnqZ1fyGlr5eyXZaXN/aj27o9vjnuO3nl2rWunrorL7yI96z+5oa9n0nl0v7a613LL8Kx/ZxWzJ7cstWG/6+fPn8uPHj+Xl5aWpcm6m2+22XK/X5fv378vXr9tvn5FdLDu5/SK7ONnF6OviZBcnuzh9XZzsYkpyCw8+AAAAStjtCgAASGHwAQAApDD4AAAAUhh8AAAAKQw+AACAFAYfAABACoMPAAAghcEHAACQwuADAABI8V/0Fz1KvuxR8h/JLpad3H6RXZzsYvR1cbKLk12cvi5OdjFFud2CLpfLbVkW/5bldrlcZJeQndxkJ7t+cpOd7GR3/j99nexazC1c+Xh5eVmWZVkul8vy+voaPUzX3t/fl7e3t/9lsZXsYtnJ7RfZxckuRl8XJ7s42cXp6+JkF1OSW3jwcS8rvb6+Thv0XWmJTXa/lWQntz/JLk52Mfq6ONnFyS5OXxcnu5gtubnhHAAASGHwAQAApDD4AAAAUhh8AAAAKQw+AACAFOHdrnp1vwv/drud/EpyZD3sZpY8oRez9XUAoyn9DtdLf6/yAQAApDD4AAAAUgy57GpLmWrEJQlZS6z4t+h7MFI73OtZhvLZRh/wp8c8tCPXV8u2XL/eq3HV6L97aUMqHwAAQIphKh/REePH32thNLiVGc627H0/1n6/p3b5L7O31zPOf4R2Qz2zX4OtmL06PvpnXcTWNrEln5KVP1uPeQSVDwAAIEX3lY+aszk93AeSte1aL+sGz2AGcd0R+fRwbT6jrXCmz9pfb9dTr2rObPektP+bvSr0TOm5Pfv5tVzP+mxV+QAAAFIYfAAAACm6XXZVUp4rLUO1yDKoc5zZTnp7P4+8JnvLogW9LlXjWNpDW7wf+/W6pXbWZ97jsZ793ezPC5UPAAAgRbeVjzVbRm73n+mtAtKCXmYVWjFLW3s8T+3kuSNyGb1t7TVbW9Qe2jdqm6zR9mptKUu5rG14VT4AAIAUBh8AAECKIZdd7V320cLTHx9fx6NRS7atyN5vfKT3s/RctPP91pb2WQYng2WZ+9zPYmnQ3/a2Q5nu9/E9OCtPlQ8AACDFkJWP3p05sp99VmHvdrFbjmMGkjO0UtHlOLP3362Ysf8/ou2VHrP3bHt//SVUPgAAgBTdVj62bF864jrfkc6lVyoe7KGN5Js111nPm7ZsbYcqd/NQ+QAAAFIYfAAAACm6XXZVordSnlJ5H7a2K+/n32ZZevTsXLYsFd16rL2vBahjlj7tX9b6uiO+g/WaaW/fR4+i8gEAAKToqvLxbKvI0pnFLXodUbNfyUYGW44Dj7a0sWfMmJVzHUK/Rrx+W3jAXwufJSofAABAii4qH89GaS2M3OBuxBmaI8y+Lvqj0vON9nmz5QqtcO2VkdexWlq1ofIBAACkMPgAAABSNLfs6szlVLOW/GwZ+7eZzpU+bNlcQ7vlo9aWJ4/YPlvL+Gx78rj/7ojt5EwttlGVDwAAIMWplY8zRmNG1JDPjebHkB0fRbdxXjsW5WRHb7LbrMoHAACQ4pTKx9EVD7MO27S4DhCAfVr4DLR+f041HqI3S9t5rFQ+e5B2VOv38qp8AAAAKQw+AACAFM1ttbvV6OU4GInrFeYwy5IZPre2AcKWrcNrLkPqTfQ66mU5vcoHAACQ4pTKR+l2gLONeFsiewDW9DLbSju2tJlZvn+sfSdeqxr1/B1a5QMAAEhx6j0fLY7GAICY2T7XZztfjrN1m+IRqkYqHwAAQAqDDwAAIEW3W+0CAOdxozkcY8tWxFt/r0UqHwAAQAqVDwCGVGtmvpfZxLPIh2ei15/29NxIuah8AAAAKQw+AACAFJZdAdAVNzpDm/ZcmyMtK2KdygcAAJBC5YOnzEAArcnaalJlZZ18qMV3jTmpfAAAAClUPiZmxuFv9xk92XC0tdlj7W87WZ1H9mwRfWAe41L5AAAAUhh8AAAAKSy7gieySsKWLcxnS9vyZGCgR6V90Me+Tv81j/Dg495I3t/fq72Y3tzPvfSCkV0suxFzi5yL7OJGz+6o19hyX5fxvuz5Gy1nt9fRr23k7I7WY1/XyvvVY3YtKMktPPi4Xq/LsizL29tb9BDDuF6vy7dv34p+fllktyxl2Y2YW0m7eTR7dnuMmt2e9rRFi33d0edc62+0mN1eGdkvy5jZZempr8tqT1v1lF1LtuT25Rasc/38+XP58ePH8vLyMu2uBbfbbbler8v379+Xr1+33z4ju1h2cvtFdnGyi9HXxckuTnZx+ro42cWU5BYefAAAAJSw2xUAAJDC4AMAAEhh8AEAAKQw+AAAAFIYfAAAACkMPgAAgBQGHwAAQAqDDwAAIIXBBwAAkMLgAwAASGHwAQAApDD4AAAAUvw/kxWrIEbXkzMAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1000x1000 with 100 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "tL8cNhDFjbOY"
+        "id": "tL8cNhDFjbOY",
+        "outputId": "a6aba131-83e6-4805-8fd5-156b648a659f"
       },
       "source": [
         "# Filling occluded images\n",
@@ -1078,10 +2007,31 @@
         "    plt.xticks(np.array([]))\n",
         "    plt.yticks(np.array([]))\n",
         "plt.show()\n",
-        "plt.savefig('numbers2.png')"
+        "fig.savefig('numbers2.png')"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1000x1000 with 10 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 1000x1000 with 10 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ]
     },
     {
       "cell_type": "markdown",