diff --git a/WIP/6-gated_pixelcnn_cropped/cropped_gated_pixelcnn.ipynb b/WIP/6-gated_pixelcnn_cropped/cropped_gated_pixelcnn.ipynb
index 2b30544..5deefb7 100644
--- a/WIP/6-gated_pixelcnn_cropped/cropped_gated_pixelcnn.ipynb
+++ b/WIP/6-gated_pixelcnn_cropped/cropped_gated_pixelcnn.ipynb
@@ -1,624 +1,623 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "name": "cropped gated_pixelcnn.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
-    }
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "k1uZnxh4Xz9Z"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
+    "for device in gpu_devices: tf.config.experimental.set_memory_growth(device, True)\n",
+    "\n",
+    "import random as rn\n",
+    "import time\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.utils import Progbar\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import initializers\n",
+    "from tensorflow import nn"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "k1uZnxh4Xz9Z",
-        "colab": {}
-      },
-      "source": [
-        "import tensorflow as tf\n",
-        "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
-        "for device in gpu_devices: tf.config.experimental.set_memory_growth(device, True)\n",
-        "\n",
-        "import random as rn\n",
-        "import time\n",
-        "\n",
-        "import matplotlib\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import tensorflow as tf\n",
-        "from tensorflow.keras.utils import Progbar\n",
-        "from tensorflow import keras\n",
-        "from tensorflow.keras import initializers\n",
-        "from tensorflow import nn"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "NN6vJl7eVnZ4",
-        "colab": {}
-      },
-      "source": [
-        "# Defining random seeds\n",
-        "random_seed = 42\n",
-        "tf.random.set_seed(random_seed)\n",
-        "np.random.seed(random_seed)\n",
-        "rn.seed(random_seed)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "8BnkhgCjVpJu",
-        "colab": {}
-      },
-      "source": [
-        "# Loading data\n",
-        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
-        "\n",
-        "height = 28\n",
-        "width = 28\n",
-        "n_channel = 1\n",
-        "\n",
-        "x_train = x_train.astype('float32') / 255.\n",
-        "x_test = x_test.astype('float32') / 255.\n",
-        "\n",
-        "x_train = x_train.reshape(x_train.shape[0], height, width, n_channel)\n",
-        "x_test = x_test.reshape(x_test.shape[0], height, width, n_channel)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "3ne-qY7JVZaB",
-        "colab": {}
-      },
-      "source": [
-        "def quantise(images, q_levels):\n",
-        "    \"\"\"Quantise image into q levels\"\"\"\n",
-        "    return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "3QVhnMymVrzc",
-        "colab": {}
-      },
-      "source": [
-        "# Quantise the input data in q levels\n",
-        "q_levels = 2\n",
-        "x_train_quantised = quantise(x_train, q_levels)\n",
-        "x_test_quantised = quantise(x_test, q_levels)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "ZObIXqzNGwmo",
-        "colab": {}
-      },
-      "source": [
-        "# Creating input stream using tf.data API\n",
-        "batch_size = 192\n",
-        "train_buf = 10000\n",
-        "\n",
-        "train_dataset = tf.data.Dataset.from_tensor_slices((x_train_quantised / (q_levels - 1),\n",
-        "                                                    x_train_quantised.astype('int32')))\n",
-        "train_dataset = train_dataset.shuffle(buffer_size=train_buf)\n",
-        "train_dataset = train_dataset.batch(batch_size)\n",
-        "\n",
-        "test_dataset = tf.data.Dataset.from_tensor_slices((x_test_quantised / (q_levels - 1),\n",
-        "                                                   x_test_quantised.astype('int32')))\n",
-        "test_dataset = test_dataset.batch(batch_size)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "75VTDkK8VZLA",
-        "colab": {}
-      },
-      "source": [
-        "class VerticalConv2D(keras.layers.Conv2D):\n",
-        "    \"\"\"https://github.com/JesseFarebro/PixelCNNPP/blob/master/layers/VerticalConv2D.py\"\"\"\n",
-        "    def __init__(self,\n",
-        "                 filters,\n",
-        "                 kernel_size,\n",
-        "                 **kwargs):\n",
-        "        if not isinstance(kernel_size, tuple):\n",
-        "            kernel_size = (kernel_size // 2 + 1, kernel_size)\n",
-        "\n",
-        "        super(VerticalConv2D, self).__init__(filters, kernel_size, **kwargs)\n",
-        "\n",
-        "        self.pad = tf.keras.layers.ZeroPadding2D(\n",
-        "            (\n",
-        "                (kernel_size[0] - 1, 0),  # Top, Bottom\n",
-        "                (kernel_size[1] // 2, kernel_size[1] // 2),  # Left, Right\n",
-        "            )\n",
-        "        )\n",
-        "\n",
-        "    def call(self, inputs):\n",
-        "        inputs = self.pad(inputs)\n",
-        "        output = super(VerticalConv2D, self).call(inputs)\n",
-        "\n",
-        "        return output\n",
-        "\n",
-        "\n",
-        "class HorizontalConv2D(keras.layers.Conv2D):\n",
-        "    def __init__(self,\n",
-        "                 filters,\n",
-        "                 kernel_size,\n",
-        "                 **kwargs):\n",
-        "\n",
-        "        if not isinstance(kernel_size, tuple):\n",
-        "            kernel_size = (kernel_size // 2 + 1,) * 2\n",
-        "\n",
-        "        super(HorizontalConv2D, self).__init__(filters, kernel_size, **kwargs)\n",
-        "        self.pad = tf.keras.layers.ZeroPadding2D(\n",
-        "            (\n",
-        "                (kernel_size[0] - 1, 0),  # (Top, Bottom)\n",
-        "                (kernel_size[1] - 1, 0),  # (Left, Right)\n",
-        "            )\n",
-        "        )\n",
-        "\n",
-        "    def call(self, inputs):\n",
-        "        inputs = self.pad(inputs)\n",
-        "        outputs = super(HorizontalConv2D, self).call(inputs)\n",
-        "\n",
-        "        return outputs"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "g625fSNYR9I6",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "filters = 1\n",
-        "kernel_size = 3\n",
-        "vertical_conv = VerticalConv2D(filters=2 * filters,\n",
-        "                                            kernel_size=kernel_size)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "NN6vJl7eVnZ4"
+   },
+   "outputs": [],
+   "source": [
+    "# Defining random seeds\n",
+    "random_seed = 42\n",
+    "tf.random.set_seed(random_seed)\n",
+    "np.random.seed(random_seed)\n",
+    "rn.seed(random_seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "8BnkhgCjVpJu"
+   },
+   "outputs": [],
+   "source": [
+    "# Loading data\n",
+    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
+    "\n",
+    "height = 28\n",
+    "width = 28\n",
+    "n_channel = 1\n",
+    "\n",
+    "x_train = x_train.astype('float32') / 255.\n",
+    "x_test = x_test.astype('float32') / 255.\n",
+    "\n",
+    "x_train = x_train.reshape(x_train.shape[0], height, width, n_channel)\n",
+    "x_test = x_test.reshape(x_test.shape[0], height, width, n_channel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3ne-qY7JVZaB"
+   },
+   "outputs": [],
+   "source": [
+    "def quantise(images, q_levels):\n",
+    "    \"\"\"Quantise image into q levels\"\"\"\n",
+    "    return (np.digitize(images, np.arange(q_levels) / q_levels) - 1).astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3QVhnMymVrzc"
+   },
+   "outputs": [],
+   "source": [
+    "# Quantise the input data in q levels\n",
+    "q_levels = 2\n",
+    "x_train_quantised = quantise(x_train, q_levels)\n",
+    "x_test_quantised = quantise(x_test, q_levels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZObIXqzNGwmo"
+   },
+   "outputs": [],
+   "source": [
+    "# Creating input stream using tf.data API\n",
+    "batch_size = 192\n",
+    "train_buf = 10000\n",
+    "\n",
+    "train_dataset = tf.data.Dataset.from_tensor_slices((x_train_quantised / (q_levels - 1),\n",
+    "                                                    x_train_quantised.astype('int32')))\n",
+    "train_dataset = train_dataset.shuffle(buffer_size=train_buf)\n",
+    "train_dataset = train_dataset.batch(batch_size)\n",
+    "\n",
+    "test_dataset = tf.data.Dataset.from_tensor_slices((x_test_quantised / (q_levels - 1),\n",
+    "                                                   x_test_quantised.astype('int32')))\n",
+    "test_dataset = test_dataset.batch(batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "75VTDkK8VZLA"
+   },
+   "outputs": [],
+   "source": [
+    "class VerticalConv2D(keras.layers.Conv2D):\n",
+    "    \"\"\"https://github.com/JesseFarebro/PixelCNNPP/blob/master/layers/VerticalConv2D.py\"\"\"\n",
+    "    def __init__(self,\n",
+    "                 filters,\n",
+    "                 kernel_size,\n",
+    "                 **kwargs):\n",
+    "        if not isinstance(kernel_size, tuple):\n",
+    "            kernel_size = (kernel_size // 2 + 1, kernel_size)\n",
+    "\n",
+    "        super(VerticalConv2D, self).__init__(filters, kernel_size, **kwargs)\n",
+    "\n",
+    "        self.pad = tf.keras.layers.ZeroPadding2D(\n",
+    "            (\n",
+    "                (kernel_size[0] - 1, 0),  # Top, Bottom\n",
+    "                (kernel_size[1] // 2, kernel_size[1] // 2),  # Left, Right\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        inputs = self.pad(inputs)\n",
+    "        output = super(VerticalConv2D, self).call(inputs)\n",
+    "\n",
+    "        return output\n",
+    "\n",
+    "\n",
+    "class HorizontalConv2D(keras.layers.Conv2D):\n",
+    "    def __init__(self,\n",
+    "                 filters,\n",
+    "                 kernel_size,\n",
+    "                 **kwargs):\n",
+    "\n",
+    "        if not isinstance(kernel_size, tuple):\n",
+    "            kernel_size = (kernel_size // 2 + 1,) * 2\n",
+    "\n",
+    "        super(HorizontalConv2D, self).__init__(filters, kernel_size, **kwargs)\n",
+    "        self.pad = tf.keras.layers.ZeroPadding2D(\n",
+    "            (\n",
+    "                (kernel_size[0] - 1, 0),  # (Top, Bottom)\n",
+    "                (kernel_size[1] - 1, 0),  # (Left, Right)\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        inputs = self.pad(inputs)\n",
+    "        outputs = super(HorizontalConv2D, self).call(inputs)\n",
+    "\n",
+    "        return outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "g625fSNYR9I6"
+   },
+   "outputs": [],
+   "source": [
+    "filters = 1\n",
+    "kernel_size = 3\n",
+    "vertical_conv = VerticalConv2D(filters=2 * filters,\n",
+    "                                            kernel_size=kernel_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
     },
+    "colab_type": "code",
+    "id": "DqsfC9JnS8C8",
+    "outputId": "ef53f88a-c437-4aa4-b40f-0184e140b1b3"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DqsfC9JnS8C8",
-        "colab_type": "code",
-        "outputId": "ef53f88a-c437-4aa4-b40f-0184e140b1b3",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "vertical_conv.kernel_size\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(2, 3)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 9
-        }
+     "data": {
+      "text/plain": [
+       "(2, 3)"
       ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vertical_conv.kernel_size\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "PTUN4s52Nu3w"
+   },
+   "outputs": [],
+   "source": [
+    "class GatedBlock(tf.keras.Model):\n",
+    "    \"\"\" Gated block of the Gated PixelCNN.\"\"\"\n",
+    "\n",
+    "    def __init__(self,\n",
+    "                 mask_type,\n",
+    "                 filters,\n",
+    "                 kernel_size):\n",
+    "        super(GatedBlock, self).__init__(name='')\n",
+    "\n",
+    "        self.mask_type = mask_type\n",
+    "        self.vertical_conv = VerticalConv2D(filters=2 * filters,\n",
+    "                                            kernel_size=kernel_size)\n",
+    "        \n",
+    "\n",
+    "        if mask_type =='A':\n",
+    "            self.horizontal_conv = keras.layers.Conv2D(filters=2 * filters, \n",
+    "                                          kernel_size=1)\n",
+    "\n",
+    "        else:         \n",
+    "            self.horizontal_conv = HorizontalConv2D(filters=2 * filters,\n",
+    "                                                kernel_size=kernel_size)\n",
+    "\n",
+    "        self.padding_A = keras.layers.ZeroPadding2D(padding=(0, (1,0)))\n",
+    "        self.cropping_A = keras.layers.Cropping2D(cropping=(0, (0, 1)))\n",
+    "\n",
+    "        self.padding = keras.layers.ZeroPadding2D(padding=((1,0),0))\n",
+    "        self.cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
+    "\n",
+    "        self.v_to_h_conv = keras.layers.Conv2D(filters=2 * filters, kernel_size=1)\n",
+    "\n",
+    "        self.horizontal_output = keras.layers.Conv2D(filters=filters, kernel_size=1)\n",
+    "\n",
+    "    def _gate(self, x):\n",
+    "        tanh_preactivation, sigmoid_preactivation = tf.split(x, 2, axis=-1)\n",
+    "        return tf.nn.tanh(tanh_preactivation) * tf.nn.sigmoid(sigmoid_preactivation)\n",
+    "\n",
+    "    def call(self, input_tensor):\n",
+    "        v = input_tensor[0]\n",
+    "        h = input_tensor[1]\n",
+    "\n",
+    "        vertical_preactivation = self.vertical_conv(v)  # NxN\n",
+    "\n",
+    "        # Shifting feature map down to ensure causality\n",
+    "        v_to_h = self.padding(vertical_preactivation)\n",
+    "        v_to_h = self.cropping(v_to_h)\n",
+    "        v_to_h = self.v_to_h_conv(v_to_h)  # 1x1\n",
+    "\n",
+    "        horizontal_preactivation = self.horizontal_conv(h)  # 1xN\n",
+    "        if self.mask_type == 'A':\n",
+    "            horizontal_preactivation = self.padding_A(horizontal_preactivation)\n",
+    "            horizontal_preactivation = self.cropping_A(horizontal_preactivation)\n",
+    "                \n",
+    "        \n",
+    "        v_out = self._gate(vertical_preactivation)\n",
+    "\n",
+    "        horizontal_preactivation = horizontal_preactivation + v_to_h\n",
+    "        h_activated = self._gate(horizontal_preactivation)\n",
+    "        h_activated = self.horizontal_output(h_activated)\n",
+    "\n",
+    "        if self.mask_type == 'A':\n",
+    "            h_out = h_activated\n",
+    "        elif self.mask_type == 'B':\n",
+    "            h_out = h + h_activated\n",
+    "\n",
+    "        return v_out, h_out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WB57YufrVxn2"
+   },
+   "outputs": [],
+   "source": [
+    "# Create Gated PixelCNN model\n",
+    "inputs = keras.layers.Input(shape=(height, width, n_channel))\n",
+    "v, h = GatedBlock(mask_type='A', filters=64, kernel_size=3)([inputs, inputs])\n",
+    "\n",
+    "for i in range(7):\n",
+    "    v, h = GatedBlock(mask_type='B', filters=64, kernel_size=3)([v, h])\n",
+    "\n",
+    "x = keras.layers.Activation(activation='relu')(h)\n",
+    "x = keras.layers.Conv2D(filters=128, kernel_size=1, strides=1)(x)\n",
+    "\n",
+    "x = keras.layers.Activation(activation='relu')(x)\n",
+    "x = keras.layers.Conv2D(filters=q_levels, kernel_size=1, strides=1)(x)\n",
+    "\n",
+    "gated_pixelcnn = tf.keras.Model(inputs=inputs, outputs=x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "_LnzHUaqV77d"
+   },
+   "outputs": [],
+   "source": [
+    "# Prepare optimizer and loss function\n",
+    "lr_decay = 0.999995\n",
+    "learning_rate = 1e-3\n",
+    "optimizer = keras.optimizers.Adam(lr=learning_rate)\n",
+    "\n",
+    "compute_loss = keras.losses.CategoricalCrossentropy(from_logits=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "CsAgEKVzLCJD"
+   },
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def train_step(batch_x, batch_y):\n",
+    "    with tf.GradientTape() as ae_tape:\n",
+    "        logits = gated_pixelcnn(batch_x, training=True)\n",
+    "\n",
+    "        loss = compute_loss(tf.squeeze(tf.one_hot(batch_y, q_levels)), logits)\n",
+    "\n",
+    "    gradients = ae_tape.gradient(loss, gated_pixelcnn.trainable_variables)\n",
+    "    gradients, _ = tf.clip_by_global_norm(gradients, 1.0)\n",
+    "    optimizer.apply_gradients(zip(gradients, gated_pixelcnn.trainable_variables))\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 476
     },
+    "colab_type": "code",
+    "id": "NoEPrfwQNM-s",
+    "outputId": "4a9422c4-8ce1-4310-8783-882be0c7c924"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "PTUN4s52Nu3w",
-        "colab": {}
-      },
-      "source": [
-        "class GatedBlock(tf.keras.Model):\n",
-        "    \"\"\" Gated block of the Gated PixelCNN.\"\"\"\n",
-        "\n",
-        "    def __init__(self,\n",
-        "                 mask_type,\n",
-        "                 filters,\n",
-        "                 kernel_size):\n",
-        "        super(GatedBlock, self).__init__(name='')\n",
-        "\n",
-        "        self.mask_type = mask_type\n",
-        "        self.vertical_conv = VerticalConv2D(filters=2 * filters,\n",
-        "                                            kernel_size=kernel_size)\n",
-        "        \n",
-        "\n",
-        "        if mask_type =='A':\n",
-        "            self.horizontal_conv = keras.layers.Conv2D(filters=2 * filters, \n",
-        "                                          kernel_size=1)\n",
-        "\n",
-        "        else:         \n",
-        "            self.horizontal_conv = HorizontalConv2D(filters=2 * filters,\n",
-        "                                                kernel_size=kernel_size)\n",
-        "\n",
-        "        self.padding_A = keras.layers.ZeroPadding2D(padding=(0, (1,0)))\n",
-        "        self.cropping_A = keras.layers.Cropping2D(cropping=(0, (0, 1)))\n",
-        "\n",
-        "        self.padding = keras.layers.ZeroPadding2D(padding=((1,0),0))\n",
-        "        self.cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
-        "\n",
-        "        self.v_to_h_conv = keras.layers.Conv2D(filters=2 * filters, kernel_size=1)\n",
-        "\n",
-        "        self.horizontal_output = keras.layers.Conv2D(filters=filters, kernel_size=1)\n",
-        "\n",
-        "    def _gate(self, x):\n",
-        "        tanh_preactivation, sigmoid_preactivation = tf.split(x, 2, axis=-1)\n",
-        "        return tf.nn.tanh(tanh_preactivation) * tf.nn.sigmoid(sigmoid_preactivation)\n",
-        "\n",
-        "    def call(self, input_tensor):\n",
-        "        v = input_tensor[0]\n",
-        "        h = input_tensor[1]\n",
-        "\n",
-        "        vertical_preactivation = self.vertical_conv(v)  # NxN\n",
-        "\n",
-        "        # Shifting feature map down to ensure causality\n",
-        "        v_to_h = self.padding(vertical_preactivation)\n",
-        "        v_to_h = self.cropping(v_to_h)\n",
-        "        v_to_h = self.v_to_h_conv(v_to_h)  # 1x1\n",
-        "\n",
-        "        horizontal_preactivation = self.horizontal_conv(h)  # 1xN\n",
-        "        if self.mask_type == 'A':\n",
-        "            horizontal_preactivation = self.padding_A(horizontal_preactivation)\n",
-        "            horizontal_preactivation = self.cropping_A(horizontal_preactivation)\n",
-        "                \n",
-        "        \n",
-        "        v_out = self._gate(vertical_preactivation)\n",
-        "\n",
-        "        horizontal_preactivation = horizontal_preactivation + v_to_h\n",
-        "        h_activated = self._gate(horizontal_preactivation)\n",
-        "        h_activated = self.horizontal_output(h_activated)\n",
-        "\n",
-        "        if self.mask_type == 'A':\n",
-        "            h_out = h_activated\n",
-        "        elif self.mask_type == 'B':\n",
-        "            h_out = h + h_activated\n",
-        "\n",
-        "        return v_out, h_out"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "WB57YufrVxn2",
-        "colab": {}
-      },
-      "source": [
-        "# Create Gated PixelCNN model\n",
-        "inputs = keras.layers.Input(shape=(height, width, n_channel))\n",
-        "v, h = GatedBlock(mask_type='A', filters=64, kernel_size=3)([inputs, inputs])\n",
-        "\n",
-        "for i in range(7):\n",
-        "    v, h = GatedBlock(mask_type='B', filters=64, kernel_size=3)([v, h])\n",
-        "\n",
-        "x = keras.layers.Activation(activation='relu')(h)\n",
-        "x = keras.layers.Conv2D(filters=128, kernel_size=1, strides=1)(x)\n",
-        "\n",
-        "x = keras.layers.Activation(activation='relu')(x)\n",
-        "x = keras.layers.Conv2D(filters=q_levels, kernel_size=1, strides=1)(x)\n",
-        "\n",
-        "gated_pixelcnn = tf.keras.Model(inputs=inputs, outputs=x)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "_LnzHUaqV77d",
-        "colab": {}
-      },
-      "source": [
-        "# Prepare optimizer and loss function\n",
-        "lr_decay = 0.999995\n",
-        "learning_rate = 1e-3\n",
-        "optimizer = keras.optimizers.Adam(lr=learning_rate)\n",
-        "\n",
-        "compute_loss = keras.losses.CategoricalCrossentropy(from_logits=True)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "313/313 [==============================] - 167s 533ms/step - loss: 0.1132\n",
+      "Epoch 2/50\n",
+      "313/313 [==============================] - 160s 512ms/step - loss: 0.0888\n",
+      "Epoch 3/50\n",
+      "188/313 [=================>............]"
+     ]
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "CsAgEKVzLCJD",
-        "colab": {}
-      },
-      "source": [
-        "@tf.function\n",
-        "def train_step(batch_x, batch_y):\n",
-        "    with tf.GradientTape() as ae_tape:\n",
-        "        logits = gated_pixelcnn(batch_x, training=True)\n",
-        "\n",
-        "        loss = compute_loss(tf.squeeze(tf.one_hot(batch_y, q_levels)), logits)\n",
-        "\n",
-        "    gradients = ae_tape.gradient(loss, gated_pixelcnn.trainable_variables)\n",
-        "    gradients, _ = tf.clip_by_global_norm(gradients, 1.0)\n",
-        "    optimizer.apply_gradients(zip(gradients, gated_pixelcnn.trainable_variables))\n",
-        "\n",
-        "    return loss"
-      ],
-      "execution_count": 0,
-      "outputs": []
+     "ename": "KeyboardInterrupt",
+     "evalue": "ignored",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-936f4c3f4059>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, n, values)\u001b[0m\n\u001b[1;32m    676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_seen_so_far\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, current, values, finalize)\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0minfo\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m' - %s:'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 640\u001b[0;31m           \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    641\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1e-3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m             \u001b[0minfo\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m' %.4f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mmean\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m   3333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3334\u001b[0m     return _methods._mean(a, axis=axis, dtype=dtype,\n\u001b[0;32m-> 3335\u001b[0;31m                           out=out, **kwargs)\n\u001b[0m\u001b[1;32m   3336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m     \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0mis_float16_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \"\"\"\n\u001b[0;32m--> 138\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# Training loop\n",
+    "n_epochs = 50\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",
+    "    for i_iter, (batch_x, batch_y) in enumerate(train_dataset):\n",
+    "        optimizer.lr = optimizer.lr * lr_decay\n",
+    "        loss = train_step(batch_x, batch_y)\n",
+    "\n",
+    "        progbar.add(1, values=[('loss', loss)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
     },
+    "colab_type": "code",
+    "id": "ue0vZbitSNmz",
+    "outputId": "42de5250-7404-4fb2-daff-e5283be9f53e"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "NoEPrfwQNM-s",
-        "outputId": "4a9422c4-8ce1-4310-8783-882be0c7c924",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 476
-        }
-      },
-      "source": [
-        "# Training loop\n",
-        "n_epochs = 50\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",
-        "    for i_iter, (batch_x, batch_y) in enumerate(train_dataset):\n",
-        "        optimizer.lr = optimizer.lr * lr_decay\n",
-        "        loss = train_step(batch_x, batch_y)\n",
-        "\n",
-        "        progbar.add(1, values=[('loss', loss)])"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/50\n",
-            "313/313 [==============================] - 167s 533ms/step - loss: 0.1132\n",
-            "Epoch 2/50\n",
-            "313/313 [==============================] - 160s 512ms/step - loss: 0.0888\n",
-            "Epoch 3/50\n",
-            "188/313 [=================>............]"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-14-936f4c3f4059>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, n, values)\u001b[0m\n\u001b[1;32m    676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_seen_so_far\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, current, values, finalize)\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0minfo\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m' - %s:'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 640\u001b[0;31m           \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    641\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1e-3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m             \u001b[0minfo\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m' %.4f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mmean\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m   3333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3334\u001b[0m     return _methods._mean(a, axis=axis, dtype=dtype,\n\u001b[0;32m-> 3335\u001b[0;31m                           out=out, **kwargs)\n\u001b[0m\u001b[1;32m   3336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m     \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0mis_float16_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \"\"\"\n\u001b[0;32m--> 138\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nll : 0.08501224964857101 nats\n",
+      "bits/dim : 0.12264675098280793\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test set performance\n",
+    "test_loss = []\n",
+    "for batch_x, batch_y in test_dataset:\n",
+    "    logits = gated_pixelcnn(batch_x, training=False)\n",
+    "\n",
+    "    # Calculate cross-entropy (= negative log-likelihood)\n",
+    "    loss = compute_loss(tf.squeeze(tf.one_hot(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)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 581
     },
+    "colab_type": "code",
+    "id": "-Ia9VXYySkuW",
+    "outputId": "ad1075b1-70cc-4012-851e-925cae32ac2c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "ue0vZbitSNmz",
-        "outputId": "42de5250-7404-4fb2-daff-e5283be9f53e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "# Test set performance\n",
-        "test_loss = []\n",
-        "for batch_x, batch_y in test_dataset:\n",
-        "    logits = gated_pixelcnn(batch_x, training=False)\n",
-        "\n",
-        "    # Calculate cross-entropy (= negative log-likelihood)\n",
-        "    loss = compute_loss(tf.squeeze(tf.one_hot(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": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "nll : 0.08501224964857101 nats\n",
-            "bits/dim : 0.12264675098280793\n"
-          ],
-          "name": "stdout"
-        }
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
       ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generating new images\n",
+    "samples = np.zeros((100, height, width, n_channel), dtype='float32')\n",
+    "for i in range(height):\n",
+    "    for j in range(width):\n",
+    "        logits = gated_pixelcnn(samples)\n",
+    "        next_sample = tf.random.categorical(logits[:, i, j, :], 1)\n",
+    "        samples[:, i, j, 0] = (next_sample.numpy() / (q_levels - 1))[:, 0]\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "for i in range(100):\n",
+    "    ax = fig.add_subplot(10, 10, i + 1)\n",
+    "    ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)\n",
+    "    plt.xticks(np.array([]))\n",
+    "    plt.yticks(np.array([]))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 153
     },
+    "colab_type": "code",
+    "id": "KBuWx-FtSouR",
+    "outputId": "3498bba6-068c-413a-b1a9-74e98192b543"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "-Ia9VXYySkuW",
-        "outputId": "ad1075b1-70cc-4012-851e-925cae32ac2c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 581
-        }
-      },
-      "source": [
-        "# Generating new images\n",
-        "samples = np.zeros((100, height, width, n_channel), dtype='float32')\n",
-        "for i in range(height):\n",
-        "    for j in range(width):\n",
-        "        logits = gated_pixelcnn(samples)\n",
-        "        next_sample = tf.random.categorical(logits[:, i, j, :], 1)\n",
-        "        samples[:, i, j, 0] = (next_sample.numpy() / (q_levels - 1))[:, 0]\n",
-        "\n",
-        "fig = plt.figure(figsize=(10, 10))\n",
-        "for i in range(100):\n",
-        "    ax = fig.add_subplot(10, 10, i + 1)\n",
-        "    ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)\n",
-        "    plt.xticks(np.array([]))\n",
-        "    plt.yticks(np.array([]))\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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oyWIcatWdPc0Sn4hJgOfYWiDhAZCjxd4+lq8nltt+x1G/9z4eTHUBAIAwbld87jyeHH3BmleeHjONwtL0CPZ5uJ5ePQrNmLSr9S7yd1DxAQAAYdyu+Nx50zN8sVzpsdz2O6j02He2PkK9CtRzd17oUOu/ZoubWwWmdoCQR/0Ce+XsNQZ7/+ZhnFrvM9jqQ0ttHY3CwVhMdQEAgDBk9/GBttq7t+fPqd/dXE0XjH78sgXuuP0onaK0dN6dnW/q7b/D4jVlT+67umai4gMAAMKg4gNcyNlcTX2OvuQOLMLdtVWWF6PntjNnN2crMd+hfk0poRYLiQ+6OZsuUjsRclgo4ZbYO/7b+CJ9yaRkM6m4at9rEmHpvMt9nYWVeM4cXSutnoN7facUC1NdAAAgjKUk+1qW5Z+U0t/9mtPNX+u6/rr6kOH4UvIfY1Z8KRGjOMbpH8QojRj/8BhfUeIDAABgGVNdAAAgDBIfAAAQBokPAAAIg8QHAACEQeIDAADCIPEBAABhkPgAAIAwSHwAAEAYRe/qent7Wx+PR6em9PP19ZW+v78vX7JkNb6UUvr8/PzO2YXTaoy5fZgSMSrzfi7Shz8Ro67I52JR4vN4PNLHx0ebVg30/v6e9Tmr8aWU0rIsWVuKW40xtw9TIkZl3s9F+vCn0hhVXj5KP/6Px/h4OzsQ2Oubr3mFDWZQenM3/GONDwAACIOKDxDMa5UHmIWxiBmo+AAAgDDkKj5ndwDM/6I3r2sNuLMGMNqdxeo9r8USiU/uRdnal1Ltl42V+LaWZTltt7WE9ioeD9Z1DZ0Q5cSuNgasnUeIaW+c7v3b0ZjtfV1iqgsAAIQxpeJzN5tTuhvvkZkqxXdlG3/tsbBWybPk7C4rQrXH6rUmQt9s1U6DjPhbPUSaDTgzq4JJxQcAAIQxpeKTc8e5l+2p3QWVtOc1HrVYSllv/6voG/m1iFetcmdxjN5p853jP6PvSmJt2ZezxmmLGErWyeDY1MXNpR22TZjULrJ7ztq2/W8WL9Atp0uU+9CTXsdZ5Vxs8aUw+1yccV3Ym66e3Zc5ats4uo97/z1LfXZlVAxMdQEAgDAkHmf3piRrnX2HOYvS3YnHPhg1dady7DxOAZRO97eIV/GYterbGWN15N+09FDM06z2UvEBAABhUPGpcOeuS+UOeSRLdyGW2lqr9SJLhWO2txljxHPNk15rthTG65OVdUqlah9gGsVU4qPc2bW7Fpf+LjWW94Sx2OYcXuO64u3JyVxWvzxLdu0l4fltdp+VOvt+mDk1x1QXAAAIw1TFZ0ttN9XtI4WeKzwlLMXVsq2zHy/tOe1jab+j3EfDLewZFgXVnn3Wx+PR9+KsayUVHwAAEIZ8xcdapmutva1sM3qlu6tRlPr9zo7irX6vmtw4rcXo5Vzzuqvx3b/f61yeQWk9qGzio7gavLTjckrt6oO1hKdYStVMcfZyNe68TR3Avu219fU6W3NeeRifKtcTj5jqAgAAYchWfPZYyeJzHqu1EgvOqfdt67YoxRZRy5fJtv69LWzbcadNlqfdIz4ckzMmWz7+TsUHAACEYaLiYy2jVVyfhHOW7xB7sfToOrB1th5TYRyzfufa2ePvWzX9ScUHAACEIVvxUcjKW/IWD/yi0uNHTmXBc//uPeE4u7rbutozO57ZauKXTXyUKe1HgHp7j9DmUi2h17IUj9oLUmfjOpRn7zF5xk9MTHUBAIAwqPjcoLRpHepFvuuzPq0VtfrT4roT6XhtzY77zvdG7vvnImFxMwAAwImmFZ+IGy+x3icWT68esVotiVppbfHqhojHTVHO98bVOWl1zVKL78y7cTZLfGqCsHrh3UMC5J+XvvV03kXVYjE+5ot8/tVM27FzMwAAQKFmFZ+j8nNuqdXzbsfW2x+Rxztk6wuZr3jfz8RrXMBoVHwAAEAYTRc359yR1Mzreb9T9eCqL9X77G6FR31hIet6YlKuXHp6UGAG5b7tpVVVd/g+Pi06i4u4PcpTmVH2RLHQRvhUco5xo3vN0k7rLbG4GQAAoJDszs1Wd6iMuseIN7njz8pd1nZcWmkz6qmM2c/PT/fTyCPx3dIGFR8AABDGsIrPncWv1nYfjX53cha/pb5TuWtuxWKbW1Efd7W87ZZPxfynnGOh3qettIyzS8VnWZZ//e9MlI7zbF3X//7Psqsx6yFG70qvP2e89PWscfuf//znx7Xh6n+R1Xxvejlmo2NhqgsAAISxlGRZy7L8k1L6u19zuvlrXddfVx8yHF9K/mPMii8lYhTHOP2DGKUR4x8e4ytKfAAAACxjqgsAAIRB4gMAAMIg8QEAAGGQ+AAAgDBIfAAAQBgkPgAAIAwSHwAAEEbRu7re3t7Wx+PRqSn9fH19pe/v78t9663Gl1JKn5+f3zmbUVmNMbcPUyJGZd7PRfrwJ2LUFflcLEp8Ho9H+vj4aNOqgd7f37M+ZzW+lFJaliVrZ02rMeb2YUrEqMz7uUgf/kSMuiKfi0x1AQCAMEh8AABAGCQ+AAAgDBIfAAAQRtHi5tmW5d8LtNXeLr/XxpT02gkA0Mb3SR9yic9RR79S6/irdj//u1q7AfzP2XnMuYvWcr/vzn6OcVmOqS4AABDGlIpPSZarns2WZuzeKj8R7jwsTLHiPqq2GKW20nP1uxibeaj4AACAMIZXfDwt1iqJ5fWzFrP0lncpqnJjtLwWpGUFa1kW+XhbiRIn0Eqv74y75+KwxMfiF32No9jOYlb/Es35orScFLVuu+JYz53GiWxd11DHgak7f2b06Yxz5u41lqkuAAAQhtzj7BEpV09yM2uLd401x/koTqU+2+rdLov9fuYZj2p/5rDc9ihqx1nOMoqeWvytFteMbTtqqlxUfAAAQBhUfBrz8uiz4jqVVlrcZe19Zu/3zlpHwV3/PVbG/J1+VozxarG8t+vSNobaa4VilXJk39T8LRKfG/YG7dF/v/O7R3qNw8PFZevq4uAt3j0RYvSqR9KuYG/qouRnUrIT65GW7Z9xwzXib7VK7pjqAgAAYQyr+FjPxq+0XrCFsbyNz73yt7cYo2m9B5rS4+wtFu0qxRNRy+Pf+52dVHwAAEAYrPGZjCrPfBGqddwF+xKhP7dVS+UtPyLpvZB61BpMEp/B1E/Y14Ed6ZUEV9T7bo/XvsvpCy+xe52uzL22eIrZixlPkrUcB0x1AQCAMKj4dHQnG569UG+b0c9uyywqu5Tit9rFriWU+6tX26jqznNVzfP2yP6R0e+rpOIDAADCoOLTSMsdVFXXkvR6XFHxLqZ0Q0rVPvOkZpy0qBKpbCaK8ZWBu3IX624r63d+3+vvVTdrjFPxAQAAYZiq+KhVCbytIejp7FiNvMvee6dW643hWv087pv9NmsLZo7T3CqytT6ree1GiyelLF1zzr7P7759/YpM4uN9l86cdqqf3D2TkZFyLjC5bVLvM/wbU5a/zb52ej7HSpOY7bG487MKjhbLl9zgHt2gtoqVqS4AABDGlIqPxQy+RGlWqrSgsgfV+O60wdoiS1zzOE4jUDo+LTbWVFvSUaPXBqOtZnqo+AAAgDCGVXxqqzx7c30qrGbjOVSP+WxUevxRHevRx5Nqv5zpVUVWM2IBds/XYnRPfCK9U6cVtePR4ukmSyf1EY9Jz17pOMo5qzq1Bajy8sQZU10AACCMKYubWzzarZQ91lCtgPRul/V+e2UpntK9lGp/V0rax6XXvk0AflM/l6j4AACAMExuYKieTe6Jsm7iiOXYVKtzCpTWb6m0A2NZ28zWsr0FxxaPu0Ti4/WCZXXnTfzmdVzWUhynLfpIMa7IOO/0jThneo4DproAAEAY3Ss+OS8fs+zO/kRWRCwl5y7etfRIdI92zTqHvTxWC2D87AgVHwAAEMbwNT4tMraWb2kdzWq7o8i988h5NNxzX8/czNHyo/S4djZLkPN5q/aqyJ6uJXerwy2PwdTFzbmB7B0wTwNCXfRjvBd/zklsOUHfM3tq2uPO2aNYfwrnzOu48BafZaoPHzDVBQAAwlhKsqllWf5JKf3drznd/LWu66+rDxmOLyX/MWbFlxIximOc/kGM0ojxD4/xFSU+AAAAljHVBQAAwiDxAQAAYZD4AACAMEh8AABAGCQ+AAAgDBIfAAAQBokPAAAIo+iVFW9vb+vj8ejUlH6+vr7S9/f35d7ZVuNLKaXPz8/vnM2orMaY24cpEaMy7+ciffgTMeqKfC4WJT6PxyN9fHy0adVA7+/vWZ+zGl9KKS3LkrWzptUYc/swJWJU5v1cpA9/IkZdkc/FqS8pBTCG55dUAkAJ1vgAAIAwqPgAQVDpwbbyt8XYQCRUfAAAQBjDKz6sNfDh2Y/0ITDOUcUGti3LwrV0oKlTXXx52sTFF/Bl9rU495pS276936/wvVN73FXjsYKpLgAAEMawig8Zqn3RKj2v8Vocr63v5D0ck8i2/aVwPp+14dnW7Wdatnnm9JLCsY+Mig8AAAhjyhof7hJtiVit24uZBYj/5uGYeH7g4iyes4rKiOOQe13Z+zcvFZMWa3tQrnviE6U0nlOyrfnZKzOOZ69pkx5/o1RpX8xeFIoyV/3roT9btH1EQnvn99/52dl9TPLyb6O/C5jqAgAAYbBz8w25mbvlDH9025XuwnovrvRAvTJS218Wp7+YNtF2NH3+dNZ/itXxV70qaT3ORSo+AAAgjGEVH5WstIUId0q9FzQrLZi+asvZI8CexrU33s7TFvF4OyYl1GNXb9+eqypWr79397rbNfGx2JEtePsy7J3wzKKUfFlwp1Q/ktIYG6HHcVfpy1YUx+ldM+NofY7VTvPVYqoLAACE0aXi4zG73lLb/bSnkv47OhbrupqprnjvT89q+079fG6xsLX3u7AsmB2b+jgbZXY/pETFBwAABNK04hM5i42qxcaNCncAuSy1tQfV+FtUNKxUJVNqW+lRjbEF1WvM3TUts+PZbvXRqy0984mixOfz8/M00Nd/X5bF/VMwe3u9eHAnLit9fLUVvpU49vQYj5aPh8W23+lDC1+eI3iJUTWO0UkP+/gAAAAUKkp8/vOf/xRlXOu6/vd/T94qI1Ft+/b1f6W/Q0lNHNCV05dHlWnFMdCiXaqxRWdpHLb2jH3vGDy1PA5UfAAAQBjDd27eZnQeMlnvFaxefeSh72Gb8rnba30P5x1UlI7xlgupqfgAAIAwhr+dfbuZnafKD44p31l7xTl1zGpFJHd7iJqfxVylWylcXVMV+7vlu+ak39WVq+deANBCP2Okkout0tjc2+187zpp8Qswujt9Zm27jV43vXcTIKa6AABAGBIVH6ss7fQ6A1NcsMDSORv1nVt3ryWzjkdNu61uPnmnj3Liafl9QsUHAACEQcUHgHl7a2Ksq31tjGI1oEaLCsL2QRql4+J5rVbuuVga095GyLX9SuJTwdsFthev7zHzzvKC4JT8jTelY9xbq6k8lTEQ6Um8bXtHtr1moTNTXQAAIIxhFR9Pme0eDzGMwN5NOq7eibP33qC936HWl3sl8b2Y1NodWcupHgsPnXj/PuylVSWPig8AAAija8XH04ItwLKatRM556f6OfzaPvX24n9K+8pCpSclO+1U0/Kt7V0SH+9lPA8xjJS7C60F1hb+cpGFBTVjUmUBcy5r7VXR47gx1QUAAMLoUvE5W1yIa1arIaWsjI3aO44o/QiMlntOKixm9z4DUqtFJYd3dQEAAFzo/jh75Iy2FHPAWugPAD1E/l7s/U6vHOzcDDSiuHOwYpugRWE6qKW9pRZKPBzjGVoeN6a6AABAGEvhjpj/pJT+7tecbv5a1/XX1YcMx5eS/xiz4kuJGMUxTv8gRmnE+IfH+IoSHwAAAMuY6gIAAGGQ+AAAgDBIfAAAQBgkPgAAIAwSHwAAEAaJDwAACIPEBwAAhFH0yoq3t7f18Xh0ako/X19f6fv7+3LvcqvxpZTS5+fnd85mVFZjzO3DlIhRmfdzkT78iRh1RT4XixKfx+ORPj4+2rRqoPf396zPWY0vpZSWZcnaWdNqjLl9mBIxKvN+LtKHPxGjrsjnIi8pFeftBYIAAMzEGh8AABAGFR9R20pPSlR7AKBUhIr5M0av8fVAxQcAAIQhX/F5rXycIeMFEHkOW9MAABS+SURBVJ3HKkfJ98DV7+CYQC7xKe1ML4MYAO7wlhz0+mJXirGlZVncxdQLU10AACAMmYrPWXYfLYu1VKq+c1emHhti2BvDXsZmbnVDfdqktD/U47nLe3y9UfEBAABhTK34UOX5SbXSw1z7NdW+K+W1+lE6hi31593zU6160KI9zz7zNp7V+sqqKYmPt8F4l+KePa1PsLMLkaUEKOe4ePvStBTPK74oyljrX6sUr/mRMNUFAADCGF7x8V7tKX2kUOluuubueHabR8qdmrVSZcg9F7efs3CnauX4j3DWP0rXnq2z6rAVltseARUfAAAQxrCKz1kGrHrnUcvSmpUcXuIoVbv4fnvHqjgWSs+3s2qWtU3TctvKHTtq9H4QBG3I7OPzpPhFkaM2eVvX9b8/Ozv2bVt6/o2Ujhc5q/e7evvOWJimQn8Wv0RLr41KMeZMNx5d+zy9smlUn+QcB6a6AABAGN0rPjULKLf/v3oW21KkWNXdWYSvdLf55G06Gb9djbWcsag6Hvaqw2fnpcJ5d+dYenpP5cy+yJk5oOIDAADCkFnjo5S1j6acuacUa21Ir40bZ2lZ6fG+FUVKMca6t5gsf2d4/N6zEEvXxCfihdKy3Fg8Tpu0eH2K0pdmy3Fp7TyuXUBv6Vz2NC3y6m4/WNxTa8SDJb3ltr/XWLyaEt1iqgsAAIQhM9XlhaU7qxY8VH+O7g6s7sB9V5SXB3uO02r7W+8eb+k45O6ybdGIfiiZNqTiAwAAwuhS8SldE2BtDcGT9Sz81dUxL310Vn2uvfW6HvUx23LthAVH6yYsV3pK1+Gpx5PLSxy1lONXOZ9er8Xv7++Hn6XiAwAAwmha8Sm9o7Ra6YmqtIJj+ckTy22/w0scTyXz/t5it47+sEu977ovbs49AOoHao+HRxBr3X2vzNXv6qlln6mOW9V2zcLx0Bb1Oor7arYRYaoLAACEwePsjVl4w7gC1WOk2i4gCs5B5KqtFFLxAQAAYUyp+Cht7Y/+LPSvhTYCXnH++TLjwaWS3z888fG2iM3jS+Za4WLmbz8V2MB4g5qW+/3c/b5lqgsAAIQxrOJDRQQqRlXpGPOYgWoPRmpxPb3zszXjnYoPAAAIo3vFR+U9Hpawo/V4rMWBdYxdzNRyU9s7fzNH08SHhb51cl//wIWtrb3x2uKlo4ovLmX8ABgtNyd4vTYdfb7VNYypLgAAEEaXqa6rLM/TnWdtdSv35zwdK1VHL18tmf5SnZ5kCg+wxePykKP3WuZWevY+ewcVHwAAEEbXxc1Ws9MrpVkpa57suFr3U/I7FCi1BX3Qxz7krvVMyV+fj65yyb6ktHQx1Ai5nRM10XlNGiyfpDUJrMU4vfM2bWCxzb1Yvr68ivCdUXpT2bNfmeoCAABhLCVZ1bIs/6SU/u7XnG7+Wtf119WHDMeXkv8Ys+JLiRjFMU7/IEZpxPiHx/iKEh8AAADLmOoCAABhkPgAAIAwSHwAAEAYJD4AACAMEh8AABAGiQ8AAAiDxAcAAIRB4gMAAMIoelfX29vb+ng8OjWln6+vr/T9/X35MhSr8aWU0ufn53fOLpxWY8ztw5SIUZn3c5E+/IkYdUU+F4sSn8fjkT4+Ptq0aqD39/esz1mNL6WUlmXJ2lLcaoy5fZgSMSrzfi7Shz8Ro67I56Ls29mBGbZvC470OpeocQOIhzU+AAAgDCo+QPpZ8UiJqgcAeEXFBwAAhEHFZ5LXCsMrKg7jRF3fcjUGrdmL56w/qfL5wjVVV+m1pndfkfgMUPMF8/wZTtYxIh3ns/Ho/Th4S/YiWJalKIHN/Zz3sT5Li3Os980oU10AACAM+YqPxfJlSca71/7tzytWfnLj27ZZ8W4r2t2/53ivYotc5bKqtk+vrqnbf6Pv22l9fenZN1R8AABAGLIVn9J529mZe8s7ynVd//X7Zt6d1GbyZz83c0GxYvWpt5K7Yy+uYrMcu8p1T8nZsdi7pqqzMtuhtnA5h1zik/ulpJIY9PoSff6e2Sdr6ZMyR7/j9WdmJnIt/m5tv8xO8rYULkA9qE+x3nH3eqD+ZXp3mUDO52dfU/fceQDmaXbf7an5rmj1u84w1QUAAMKQqfiUZK8qGbvn/V9aVHrOWD1eKmPvjpbHXnmBqIe+quUpdtXxVatX3yiei1dTsjnHgsfZAQAAbpCo+LSonIzMdEfeTW3npUcsaOxd6bHM0130XRaOhcUFraVaLCxVP0atKhmz45z992e4M3PT83tnauLjYarIarv3kPT4GJNbrWOwsKByS719vViLO3fPntq4ZiYd3hOevUXj6ktVmOoCAABhSEx15WbxCpniLNuyfespr97H1drd56vSBXheK2feqmGWRTr+udc+xf1kIn1nWRqTVHwAAEAYUys+LSo9Mxc1W8pwz5xt7HVUvfC6c+xV5ebu5y2yVunxOjbV3+HXyl5Mr9co9XNtxqJz9bFQGk/P7/0piU9OAqE4sGcOrBHHg5f7/bS32/TRf8v9HZZYSvQjPMGlfPxbOkvqWuy+PPqp2Nw2WNkNvoTqOclUFwAACENicfOWaoaYy9q0wJWjOxPPpfYa1sftlpdYolQmPah975aSO9WeGorH4En9GkLFBwAAhDGs4rOtELRYqKaQ7fbedOvobyjE7lVpxU5l4X0rVseb+h2mIm/VaYs8jFuLMVDxAQAAYcit8ckx6+4k9/HmFhmwehYd/Q6RSo8+6+2HLblbW6hf2xX03iZkeOJjtdOP2j3y5XmzL+RW+65WhHitJzwR+gj2tHjsvufvUzXq2sNUFwAACMPEVJfCHWhJG2rfTBthF2B1uXdWHu7ArFd7RohwjLzFo6D1dUH5OlN7LZw57qj4AACAMIZVfEqzQk93IdYXwlp6dUErEWL0qrTvlO+mYUuLsWS1mmzpmjl8qsvzCy5ftZzWUn6SLSLLxyXC9M2rll9IwJGW0z4R3j83C1NdAAAgjCmLmz3fOZU+9p67iFaBUluAI63vkr2Ne6oIOlq+pR35qPgAAIAwTDzOrqomM4+4uHsPb84er/edpKe1Q1bbDQ17a32uxlTJ+RlhfPa8XpH4VDi7wB91lvIC5j2UW2MoGXOKY6LXDrmwQ/namvt3uSHO0yp+proAAEAYVHwqnGWdNYvV1LN49fapUD5ONdNQd6ooo49FyaO/yv00gsX4a/dCe/6c9al1y22v0bu6TMUHAACE0b3i42nBYy3eweWHtf5rsQDfyvmas97HSiyRKa/ZGS1KnE+jzt2miY+1LwWgFbUL1N2pgavPKbPabuy705/bZFjxjQF8Z/42+maFqS4AABDGUvg46z8ppb/7Naebv9Z1/XX1IcPxpeQ/xqz4UiJGcYzTP4hRGjH+4TG+osQHAADAMqa6AABAGCQ+AAAgDBIfAAAQBokPAAAIg8QHAACEQeIDAADCIPEBAABhFL2y4u3tbX08Hp2a0s/X11f6/v6+3BvcanwppfT5+fmdsxmV1Rhz+zAlYlTm/VykD38iRl2Rz8WixOfxeKSPj482rRro/f0963NW40sppWVZsnbWtBpjbh+mRIzKvJ+L9OFPxKgr8rnIVBcAAAiDxAcAAIRB4gMAAMIg8QEAAGEULW5GH8vy74Xn67pOaAm2nv1CXwCAHyYSn73E4EnlS6n0S/IsJmiJlADVjEu142LhegFgHqa6AABAGLIVn9w7z9l346/tXJblX22hugMlkccj08qY7er88z4eFc5BKj4AACAMuYpP6d2oYnbc4o56diULPrQ4n6xUiF7beXTu7FVpzz6v5G5fXPWv0jFQbVet0lmMLevxX8U++hyUSXxKTmjrgyCl8xhmftHkfnl4Z+XL/kjLBb7Pz1s/Jk9H8exNUytoedzVvoDO2uBFbUJe8ztUqBcwmOoCAABhSFR8vD5+yqPtx7yVsZV4LJX34KGSta5rUZVm77xTil+pLa1d9U/ONLPl81ip7VR8AABAGBIVn+1dizW1WaxivIptGq22WnJ07Gbf5cz++7hvrzKz7deSPt5ea5Uqgy0fCLli5Zywej1WGldHJBKfPWoH6i6rg7g11eNQ2y7FeFqdOzmxWT5PrU0j3Gmf5b1jahYDnxm9gLv071hIHHKptpupLgAAEIZcxUc1QxyN4zBf1D6wWOlRrLzNZGV7kNJ+a/1ov8o4Vp0qL2HpgRUqPgAAIAy5io9XJY+OqmfL3tTMqXvrR+sVk5IHJKyt7cmlvmlcrTtjU3Uncq9buFhB4iOEAa+v5KKpVErf8vpKldebi6sXBiu1vVRtH1qO+cheH5fuodbzuHh72uyIpfYz1QUAAMKg4jOYpawY9vUu66tWtV4pTG+MYqE/Umo/hXX132aNASv9EQkVHwAAEIZcxUdx7QDa2VvkPavPe98BWnpMeI/qwtASnjaDu0JcQB4qPgAAIAy5is8TlR89Xp6IqWWl2tFz8zoL/X62dcRZ+y291kGpLb20jNHDBoFoRybxKdnnBhgpZ88NS+M2ysW+9JHm3M9FOX4esF8O9jDVBQAAwpCp+GC+vd1vIy0OLaF4DLi7vWapMuddyW7brf8u7Gm51IKKDwAACEOu4mNxzYQnHP/fchdDRj9O6lr2D5WCse6sqeK89GOvL++ut5NLfKDvzm691qfOvL2c1LM770jii9MeS0/l4drey4RbnZdMdQEAgDCo+GBXyykv7p7niXiXy2PnduRcZ64qzNGrsFEeamBxMwAAQAW5ig/VAS1H86qvd9V3+m3WHXrtDr/Q1Ks6yVjob3uMSzeUzP29lvG9+Fur/pRJfKx3bGn7o52Qyk+LeemLPVGmfVqUwS0mPT3bvLe4dJbSGy31fssRIcYjvb8nmOoCAABhyFR8XlnJZmsfmbV0J15arTmLSbny49mdLQhyfveWhTGdUpxFoTVUz89I01tbHmPaM2rcUfEBAABhyFZ8rCrNzHveibe2rdZYaXMkIxdrK6+HOaumelk30evOeHa/3onLSt9dOVuv5rVKObrCKJf4WO68lNruNaDKa1xe9Oyf2V+MR3JfsHv1O9SNTHhGuhpXXr/wr+S8Ouf5GYs3pLPGHVNdAAAgjKUkQ1yW5Z+U0t/9mtPNX+u6/rr6kOH4UvIfY1Z8KRGjOMbpH8QojRj/8BhfUeIDAABgGVNdAAAgDBIfAAAQBokPAAAIg8QHAACEQeIDAADCIPEBAABhkPgAAIAwil5Z8fb2tj4ej05N6efr6yt9f39f7o1tNb6UUvr8/PzO2YzKaoy5fZgSMSrzfi7Shz8Ro67I52JR4vN4PNLHx0ebVg30/v6e9Tmr8aWU0rIsWTtrWo0xtw9TIkZl3s9F+vAnYtQV+VxkqgsAAIRB4gMAAMIg8QEAAGGQ+AAAgDCKFjfPtiy/F2irv1H+2c49Z23f+zn1WBGTlXMRwDy134W9ySY+ZwdsWRa5C+5Ze2s+50luzGp9Wuo1TqvxlIxRxXPxzDY2S+1+5SWO3lS/eD2r+S4c3RdMdQEAgDDkKj6l2eLMrL1n9Ub5bmQvU29xLNTvYi1X63q23Uuly4K9fqSq8ZvlyrKHZQ6l43D7+dHf51R8AABAGDIVn6NscZsBnt3tjMyOr+4sStpioYpwtd7Kq7uxKax/mdE/CnF7cqcPPVQSrliseOX0qeUK1lNp20Z9n0skPrknZ8tplRoWT7C7PCc2Z0qT26jHae/GxOu5oOTOtTBCP3mO7dXsJQK1yfW6rtOum0x1AQCAMCQqPqVmZoqtqFePWk7nbX/f7KrdHWcxH03Jeru7zj0Gyqy0c0/Ltnsbm0/q8ZRc+yxfL3Mcxdd7upyKDwAACEO24lN6N9I7Q7zKuK/am5Oxq9+pPN1pZ+tKEupdPThw9nnLLFc6ctZTlFYHWIyujzWFbUkkPmfP+M8+KXOnpLbtLWXlolPbTotPJ+xNp85eRFgjt51W4sFPZw+B7OELc57SY8852Q9TXQAAIAyJis+VnIWxI7NjxUfte6pdyNzr949S2qce+94j1fF2pGV7PTwY4p218XlHzv59PVDxAQAAYchVfEp3Ce6VGd7ZlKn291sWYW3T2WPqs+5cAPw0e13okdyHCc6+eyx8b9z5DhzVb8MTn1YdN3pgK55ICkoXLlt+ouZqwTNsiPQEGzT1mEafNWZbxDK67Ux1AQCAMOSmus5wFzZHbVWj9FFbbyLFasVe1S5SP1GhRC+WziMqPgAAIIxhFR9Lm9j1vCvydsdVuvtvJKqLLKOL2Cecm35ZGc8Ka3ueqPgAAIAwJNb4KGesym3roeY9P6+8HrOau2bLT7HBPuWngdDmMXUL1xi174lhiY+1fQhe373VqpPUj0PO7ti5L2x9/TkAY5Dw2MKSgbGY6gIAAGEMn+qy+q6YngtVFUuVZ20pvTvxssj36lFoi+Ma/lyNQw/nomelswKK/XnWdoX2UvEBAABhSCxutlIRaLlAS32tz5XaRdAW+vlMTiXMap8+WXzv2LbNyu3soWS8RTs2Flmu9JxRau+UxOfspY9nnx+Jhbz/Y/2LXIF64mepj0vOSdXj3YrFJBX7PCewau1lqgsAAIQxdarLQlWlZiHv2e/I+Vml7Lh1JUAxxlYsVU2eLLW5dNHusiwhKrKvIsTozV6V0tK5uZVbcW0dX8m4p+IDAADCkFjcvJepKWa7e3eUORRjuVJzd11yfKwsaM9l8RHiq+qk8rjNOZ57sURa/wNdyudWbwqxSyQ+e84uwCpfmla+JEbY64+rkq2HaS+PCY+qO220Pn3wqkUfWh0HVuVeK9Qe9Gml17lXc0yY6gIAAGHIVnysaTFdZyWbb/3eMossvgvJS7UD9RgDujxWelT3JKLiAwAAwpCv+Fieh859lM9DPF60uCNWPE5sdOfD1QMDJej7sWoe7bbYR7nv45wZm1ziQynWDyulW6/JTkpt+0DloYIaFm80WooWs7UHJ6y0M5f6wwRMdQEAgDCWkkxzWZZ/Ukp/92tON3+t6/rr6kOG40vJf4xZ8aVEjOIYp38QozRi/MNjfEWJDwAAgGVMdQEAgDBIfAAAQBgkPgAAIAwSHwAAEAaJDwAACIPEBwAAhEHiAwAAwiDxAQAAYZD4AACAMP4P6a4zeY5hWWgAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 720x720 with 100 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 10 Axes>"
       ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "KBuWx-FtSouR",
-        "outputId": "3498bba6-068c-413a-b1a9-74e98192b543",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 153
-        }
-      },
-      "source": [
-        "# Filling occluded images\n",
-        "occlude_start_row = 14\n",
-        "num_generated_images = 10\n",
-        "samples = np.copy(x_test_quantised[0:num_generated_images, :, :, :])\n",
-        "samples = samples / (q_levels - 1)\n",
-        "samples[:, occlude_start_row:, :, :] = 0\n",
-        "\n",
-        "fig = plt.figure(figsize=(10, 10))\n",
-        "\n",
-        "for i in range(10):\n",
-        "    ax = fig.add_subplot(1, 10, i + 1)\n",
-        "    ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)\n",
-        "    plt.xticks(np.array([]))\n",
-        "    plt.yticks(np.array([]))\n",
-        "\n",
-        "for i in range(occlude_start_row, height):\n",
-        "    for j in range(width):\n",
-        "        logits = gated_pixelcnn(samples)\n",
-        "        next_sample = tf.random.categorical(logits[:, i, j, :], 1)\n",
-        "        samples[:, i, j, 0] = (next_sample.numpy() / (q_levels - 1))[:, 0]\n",
-        "\n",
-        "fig = plt.figure(figsize=(10, 10))\n",
-        "\n",
-        "for i in range(10):\n",
-        "    ax = fig.add_subplot(1, 10, i + 1)\n",
-        "    ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)\n",
-        "    plt.xticks(np.array([]))\n",
-        "    plt.yticks(np.array([]))\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAABECAYAAABu1lQcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAADy0lEQVR4nO3cQVLkOBAF0PTEHIFeTx2C+58A7kCvhztoFkwHBOFy2WW7UCrfW3Y4OpSWbL5Shqm1FgAAVfz10wMAAHgk4QcAKEX4AQBKEX4AgFKEHwCgFOEHAChF+AEAShF+AIBShB8AoJS/t1z89PTULpfLSUM519vbW7y/v09L12SuLyLi9fX1vbX2a+mazDWumcMINfbOs/ghc43W6afRa8xcX8T1Z3FT+LlcLvHy8nLcqB7o+fn55jWZ64uImKbp961rMte4Zg4j1Ng7z+KHzDVap59GrzFzfRHXn0XHXgBAKcIPAFDKpmMvtpum+ePU1tqDRwIAROj8AADF6Pwc5FqH59b1FTpAo9Q6N8fZawLGNE2T99MCnR8AoBSdnwNs7fpAT0bpzO2V8T58f/dkGjvb3XvC8If18Un4OcnSIvu6IDO+cNcaKRSOVMtRvt6TzOv3+9xmeCbPWI9Z5jPLOPfyzjmXYy8AoJRTOz8VPxDdWt/o9yO7W7uvDF2Ca/buLFtrdqc/4Oj3apajEWttbI/OCzo/AEApp3R+lhL6kem9lx3K2nFU3bn0Mk9b+LCwhuzd6UxjvdeanydZ78O9489a7x9r369L1+29B6eEn7Uf++6VfeGPLGvQO2rcPf+NjaOCWtY5zuqMD317/nh4y/rq+Xm75lZ92epZ0uO7wrEXAFDKw3/VfW+a7Xmnck321jrzvs5hjzubORmfnzNkma+IXGP9SVlOAqrM59o65+br+1zO/V97u306PwBAKf7I4cmqpPwR7NmpzO1QsuxEtxp1TY82T3Myd6GXOgRz/9ZbXZnv/dHu+cD76PeOzg8AUEqazk+2bxXO/BW9nmWbp7XW1tJrB+iseRlpjjPZ8ht72Tt1o66xUes6osO19K3PUboPP9kf3K9GXewjM2efRnoWM1nzg+Deuel1ffe2edjLs/Ohp7/z59gLACil+85P1rSfddx7Za575OOgI8bTW017ZKwl45jvtfWYuXdZxnmEpVp7ug86PwBAKcIPAFCK8AMAlCL8AAClCD8AQCnCDwBQivADAJQi/AAApQg/AEApwg8AUIrwAwCUIvwAAKUIPwBAKcIPAFCK8AMAlCL8AAClCD8AQCnCDwBQivADAJQi/AAApQg/AEApwg8AUIrwAwCUMrXW1l88Tf9GxO/zhnOqf1prv5YuSF5fxPg13qwvQo0JjL5OI8av0Tr93+g1Jq8v4kqNm8IPAEB2jr0AgFKEHwCgFOEHAChF+AEAShF+AIBShB8AoBThBwAoRfgBAEoRfgCAUv4Dzge+SeXbn58AAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 720x720 with 10 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 720x720 with 10 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 10 Axes>"
       ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "W7bzeLMjPNcI",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": 0,
-      "outputs": []
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "# Filling occluded images\n",
+    "occlude_start_row = 14\n",
+    "num_generated_images = 10\n",
+    "samples = np.copy(x_test_quantised[0:num_generated_images, :, :, :])\n",
+    "samples = samples / (q_levels - 1)\n",
+    "samples[:, occlude_start_row:, :, :] = 0\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    ax = fig.add_subplot(1, 10, i + 1)\n",
+    "    ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)\n",
+    "    plt.xticks(np.array([]))\n",
+    "    plt.yticks(np.array([]))\n",
+    "\n",
+    "for i in range(occlude_start_row, height):\n",
+    "    for j in range(width):\n",
+    "        logits = gated_pixelcnn(samples)\n",
+    "        next_sample = tf.random.categorical(logits[:, i, j, :], 1)\n",
+    "        samples[:, i, j, 0] = (next_sample.numpy() / (q_levels - 1))[:, 0]\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    ax = fig.add_subplot(1, 10, i + 1)\n",
+    "    ax.matshow(samples[i, :, :, 0], cmap=matplotlib.cm.binary)\n",
+    "    plt.xticks(np.array([]))\n",
+    "    plt.yticks(np.array([]))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "W7bzeLMjPNcI"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "cropped gated_pixelcnn.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/WIP/6-gated_pixelcnn_cropped/gated_pixelcnn_vs_cropped.ipynb b/WIP/6-gated_pixelcnn_cropped/gated_pixelcnn_vs_cropped.ipynb
index aede3e3..c5d69fa 100644
--- a/WIP/6-gated_pixelcnn_cropped/gated_pixelcnn_vs_cropped.ipynb
+++ b/WIP/6-gated_pixelcnn_cropped/gated_pixelcnn_vs_cropped.ipynb
@@ -1,1135 +1,1255 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "gated_pixelcnn_vs_cropped.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "HgFGN07idT26"
+   },
+   "source": [
+    "# Masked vs cropped implementation for Gated PixelCNN"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HgFGN07idT26",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Converting masked-based implementation to cropping-based implementation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ObS7YqtCbC33",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import tensorflow as tf\n",
-        "import tensorflow.keras as keras\n",
-        "import numpy as np\n",
-        "import math"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "iCMR2mKLbt_l",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "test_ones_2d = np.ones([1, 5, 5, 1], dtype='float32')\n",
-        "test_ones_3d = np.ones([1, 5, 5, 5, 1], dtype='float32')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "NycU0IQZb1X1",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def print_3d(matrix_3d):\n",
-        "    for i in range(matrix_3d.shape[0]):\n",
-        "        print(f'Depth {i}')\n",
-        "        print(matrix_3d[i,...])"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "AeH21Zkzcrt5",
-        "colab_type": "code",
-        "outputId": "d47da908-4659-45aa-f540-e01d3e510bdc",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 527
-        }
-      },
-      "source": [
-        "print_3d(test_ones_3d.squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Depth 0\n",
-            "[[1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]]\n",
-            "Depth 1\n",
-            "[[1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]]\n",
-            "Depth 2\n",
-            "[[1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]]\n",
-            "Depth 3\n",
-            "[[1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]]\n",
-            "Depth 4\n",
-            "[[1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "xqrqiDnbfoqW",
-        "colab_type": "code",
-        "outputId": "12aef41a-4992-464c-8d38-8fd732286c57",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "print(test_ones_2d[0,:,:,0].squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1. 1.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1UcUkEj0d7wh",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Creating 2D masked solution to check results with cropped solution later"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "83mZFyondaAT",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "class MaskedConv2D(tf.keras.layers.Layer):\n",
-        "    def __init__(self,\n",
-        "                 mask_type,\n",
-        "                 filters,\n",
-        "                 kernel_size,\n",
-        "                 strides=1,\n",
-        "                 padding='same',\n",
-        "                 kernel_initializer='glorot_uniform',\n",
-        "                 bias_initializer='zeros'):\n",
-        "        super(MaskedConv2D, self).__init__()\n",
-        "\n",
-        "        assert mask_type in {'A', 'B', 'V'}\n",
-        "        self.mask_type = mask_type\n",
-        "\n",
-        "        self.filters = filters\n",
-        "\n",
-        "        if isinstance(kernel_size, int):\n",
-        "            kernel_size = (kernel_size, kernel_size)\n",
-        "        self.kernel_size = kernel_size\n",
-        "\n",
-        "        self.strides = strides\n",
-        "        self.padding = padding.upper()\n",
-        "        self.kernel_initializer = tf.keras.initializers.get(kernel_initializer)\n",
-        "        self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n",
-        "\n",
-        "    def build(self, input_shape):\n",
-        "        kernel_h, kernel_w = self.kernel_size\n",
-        "\n",
-        "        self.kernel = self.add_weight(\"kernel\",\n",
-        "                                      shape=(kernel_h,\n",
-        "                                             kernel_w,\n",
-        "                                             int(input_shape[-1]),\n",
-        "                                             self.filters),\n",
-        "                                      initializer=self.kernel_initializer,\n",
-        "                                      trainable=True)\n",
-        "\n",
-        "        self.bias = self.add_weight(\"bias\",\n",
-        "                                    shape=(self.filters,),\n",
-        "                                    initializer=self.bias_initializer,\n",
-        "                                    trainable=True)\n",
-        "\n",
-        "        mask = np.ones(self.kernel.shape, dtype=np.float32)\n",
-        "\n",
-        "        if kernel_h % 2 != 0: \n",
-        "            center_h = kernel_h // 2\n",
-        "        else:\n",
-        "            center_h = (kernel_h - 1) // 2\n",
-        "\n",
-        "        if kernel_w % 2 != 0: \n",
-        "            center_w = kernel_w // 2\n",
-        "        else:\n",
-        "            center_w = (kernel_w - 1) // 2\n",
-        "\n",
-        "        if self.mask_type == 'V':\n",
-        "            mask[center_h + 1:, :, :, :] = 0.\n",
-        "        else:\n",
-        "            mask[:center_h, :, :] = 0.\n",
-        "            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.float32, name='mask')\n",
-        "\n",
-        "    def call(self, input):\n",
-        "        masked_kernel = tf.math.multiply(self.mask, self.kernel)\n",
-        "        x = tf.nn.conv2d(input, masked_kernel, strides=[1, self.strides, self.strides, 1], padding=self.padding)\n",
-        "        x = tf.nn.bias_add(x, self.bias)\n",
-        "        return x"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nMXqIeSUkdcV",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Tests with kernel_size 3"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "M62GZQe8ixvy",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Vertical stack"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "kjUrpEtIg7p9",
-        "colab_type": "code",
-        "outputId": "8df4b5ee-1d0e-4f7d-9009-beaf8c8133ee",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "mask_type = 'V'\n",
-        "kernel_size=(3, 3)\n",
-        "\n",
-        "conv = MaskedConv2D(mask_type=mask_type,\n",
-        "                filters=1,\n",
-        "                kernel_size=kernel_size, \n",
-        "                padding='same',\n",
-        "                kernel_initializer='ones', \n",
-        "                bias_initializer='zeros')\n",
-        "\n",
-        "result_v = conv(test_ones_2d)\n",
-        "\n",
-        "print('MASK')\n",
-        "print(conv.mask.numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result_v.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "MASK\n",
-            "[[1. 1. 1.]\n",
-            " [1. 1. 1.]\n",
-            " [0. 0. 0.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[2. 3. 3. 3. 2.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PFqvGa439Z2o",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Feeding horizontal"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "oq_JTwdE9lr6",
-        "colab_type": "code",
-        "outputId": "a5eecd56-e068-4d9f-ff4b-01bd048f0bbb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 119
-        }
-      },
-      "source": [
-        "padding = keras.layers.ZeroPadding2D(padding=((1,0),0))\n",
-        "cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
-        "\n",
-        "x = padding(result_v)\n",
-        "result = cropping(x)\n",
-        "\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "OUTPUT\n",
-            "[[0. 0. 0. 0. 0.]\n",
-            " [2. 3. 3. 3. 2.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iHY6UE2_p5oc",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Horizontal stack A"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "4_q_IunZkFmj",
-        "colab_type": "code",
-        "outputId": "1bfa1a88-7ae3-4a7e-d43e-46e7e683cce4",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "mask_type = 'A'\n",
-        "kernel_size=(3, 3)\n",
-        "\n",
-        "conv = MaskedConv2D(mask_type=mask_type,\n",
-        "                filters=1,\n",
-        "                kernel_size=kernel_size, \n",
-        "                padding='same',\n",
-        "                kernel_initializer='ones', \n",
-        "                bias_initializer='zeros')\n",
-        "\n",
-        "result = conv(test_ones_2d)\n",
-        "\n",
-        "print('MASK')\n",
-        "print(conv.mask.numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "MASK\n",
-            "[[0. 0. 0.]\n",
-            " [1. 0. 0.]\n",
-            " [0. 0. 0.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jMuS-vgWqAWK",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Horizontal stack B"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5yeB5h2tkSs_",
-        "colab_type": "code",
-        "outputId": "9e7346b9-d360-42dd-85b4-a9a22ba2bacb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "mask_type = 'B'\n",
-        "kernel_size=(3, 3)\n",
-        "\n",
-        "conv = MaskedConv2D(mask_type=mask_type,\n",
-        "                filters=1,\n",
-        "                kernel_size=kernel_size, \n",
-        "                padding='same',\n",
-        "                kernel_initializer='ones', \n",
-        "                bias_initializer='zeros')\n",
-        "\n",
-        "result = conv(test_ones_2d)\n",
-        "\n",
-        "print('MASK')\n",
-        "print(conv.mask.numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "MASK\n",
-            "[[0. 0. 0.]\n",
-            " [1. 1. 0.]\n",
-            " [0. 0. 0.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1NxkQ3U1knbE",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Tests with kernel_size 4"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WNykK-WpqMlu",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Vertical stack"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "p3DTiFYCk57Y",
-        "colab_type": "code",
-        "outputId": "01d4c588-acde-43ba-8b52-7b5b2fddc52a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 221
-        }
-      },
-      "source": [
-        "mask_type = 'V'\n",
-        "kernel_size=(4, 4)\n",
-        "\n",
-        "padding = keras.layers.ZeroPadding2D(padding=((1,0),0))\n",
-        "\n",
-        "conv = MaskedConv2D(mask_type=mask_type,\n",
-        "                filters=1,\n",
-        "                kernel_size=kernel_size, \n",
-        "                padding='same',\n",
-        "                kernel_initializer='ones', \n",
-        "                bias_initializer='zeros')\n",
-        "\n",
-        "cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
-        "\n",
-        "x = padding(test_ones_2d)\n",
-        "x = conv(x)\n",
-        "result = cropping(x)\n",
-        "\n",
-        "print('MASK')\n",
-        "print(conv.mask.numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "MASK\n",
-            "[[1. 1. 1. 1.]\n",
-            " [1. 1. 1. 1.]\n",
-            " [0. 0. 0. 0.]\n",
-            " [0. 0. 0. 0.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[0. 0. 0. 0. 0.]\n",
-            " [3. 4. 4. 3. 2.]\n",
-            " [6. 8. 8. 6. 4.]\n",
-            " [6. 8. 8. 6. 4.]\n",
-            " [6. 8. 8. 6. 4.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E5jUGK3_qbT8",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Horizontal stack A"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "J5HSUo7Rk5xO",
-        "colab_type": "code",
-        "outputId": "d2eabc6f-1b49-43f9-f7d8-1cf13021c47b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 221
-        }
-      },
-      "source": [
-        "mask_type = 'A'\n",
-        "kernel_size=(4, 4)\n",
-        "\n",
-        "conv = MaskedConv2D(mask_type=mask_type,\n",
-        "                filters=1,\n",
-        "                kernel_size=kernel_size, \n",
-        "                padding='same',\n",
-        "                kernel_initializer='ones', \n",
-        "                bias_initializer='zeros')\n",
-        "\n",
-        "result = conv(test_ones_2d)\n",
-        "\n",
-        "print('MASK')\n",
-        "print(conv.mask.numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "MASK\n",
-            "[[0. 0. 0. 0.]\n",
-            " [1. 0. 0. 0.]\n",
-            " [0. 0. 0. 0.]\n",
-            " [0. 0. 0. 0.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KqORK7mLqvPP",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Horizontal stack B"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "_2V51aerk5l1",
-        "colab_type": "code",
-        "outputId": "b54f7147-0080-48a2-a84c-d3ca557c31d9",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 221
-        }
-      },
-      "source": [
-        "mask_type = 'B'\n",
-        "kernel_size=(4, 4)\n",
-        "\n",
-        "conv = MaskedConv2D(mask_type=mask_type,\n",
-        "                filters=1,\n",
-        "                kernel_size=kernel_size, \n",
-        "                padding='same',\n",
-        "                kernel_initializer='ones', \n",
-        "                bias_initializer='zeros')\n",
-        "\n",
-        "result = conv(test_ones_2d)\n",
-        "\n",
-        "print('MASK')\n",
-        "print(conv.mask.numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "MASK\n",
-            "[[0. 0. 0. 0.]\n",
-            " [1. 1. 0. 0.]\n",
-            " [0. 0. 0. 0.]\n",
-            " [0. 0. 0. 0.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kZmui789Br2B",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Creating 2D cropped solution"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "PxBNsvzhB1ec",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "class VerticalCroppedConv2d(tf.keras.Model):\n",
-        "    def __init__(self,\n",
-        "                 filters,\n",
-        "                 kernel_size,\n",
-        "                 kernel_initializer, \n",
-        "                 bias_initializer):\n",
-        "        super(VerticalCroppedConv2d, self).__init__(name='')\n",
-        "\n",
-        "        if isinstance(kernel_size, int):\n",
-        "            kernel_size = (kernel_size, kernel_size)\n",
-        "\n",
-        "        kernel_h, kernel_w = kernel_size\n",
-        "\n",
-        "        self.padding = keras.layers.ZeroPadding2D(padding=((kernel_h-1, 0),(int((kernel_w-1)/2),int((kernel_w-1)/2))))\n",
-        "\n",
-        "        self.conv = keras.layers.Conv2D(filters=filters,\n",
-        "                                        kernel_size=kernel_size,\n",
-        "                                        strides=1,\n",
-        "                                        padding='valid',\n",
-        "                                        kernel_initializer=kernel_initializer, \n",
-        "                                        bias_initializer=bias_initializer)\n",
-        "\n",
-        "    def call(self, input_value):\n",
-        "\n",
-        "        x = self.padding(input_value)\n",
-        "        x = self.conv(x)\n",
-        "        out = self.cropping(x)\n",
-        "\n",
-        "        return out\n",
-        "\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RfFuwKlrP2JU",
-        "colab_type": "text"
-      },
-      "source": [
-        "Example step by step"
-      ]
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hi all, in this notebook we will compare the masked implemntation of the convolutions from the Gated PixelCNN versus the alternative sugexted in the paper, the use of convolutions operaritions with appropriate croppings and padding to achieve the same result.\n",
+    "Let's check out!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we willcheck if both implementation create the same result. For this we will create a 5x5 matrix filled with ones as our input example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ObS7YqtCbC33"
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow import nn\n",
+    "from tensorflow.keras import initializers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iCMR2mKLbt_l"
+   },
+   "outputs": [],
+   "source": [
+    "test_ones_2d = np.ones([1, 5, 5, 1], dtype='float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 102
     },
+    "colab_type": "code",
+    "id": "xqrqiDnbfoqW",
+    "outputId": "12aef41a-4992-464c-8d38-8fd732286c57"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fH3I0lfoPdcH",
-        "colab_type": "code",
-        "outputId": "f142569c-f99d-4244-ab5d-1887243d0d29",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "kernel_h = 2\n",
-        "kernel_w = 3\n",
-        "\n",
-        "kernel_size = (kernel_h, kernel_w)\n",
-        "\n",
-        "padding = keras.layers.ZeroPadding2D(padding=((kernel_h-1, 0),(int((kernel_w-1)/2),int((kernel_w-1)/2))))\n",
-        "\n",
-        "res = padding(test_ones_2d)\n",
-        "print(res.numpy().squeeze())\n",
-        "\n",
-        "conv = keras.layers.Conv2D(filters=1,\n",
-        "                                        kernel_size=kernel_size,\n",
-        "                                        strides=1,\n",
-        "                                        padding='valid',\n",
-        "                                        kernel_initializer='ones', \n",
-        "                                        bias_initializer='zeros')\n",
-        "\n",
-        "res2 = conv(res)\n",
-        "print(res2.numpy().squeeze())\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[0. 0. 0. 0. 0. 0. 0.]\n",
-            " [0. 1. 1. 1. 1. 1. 0.]\n",
-            " [0. 1. 1. 1. 1. 1. 0.]\n",
-            " [0. 1. 1. 1. 1. 1. 0.]\n",
-            " [0. 1. 1. 1. 1. 1. 0.]\n",
-            " [0. 1. 1. 1. 1. 1. 0.]]\n",
-            "[[2. 3. 3. 3. 2.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(test_ones_2d[0,:,:,0].squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "1UcUkEj0d7wh"
+   },
+   "source": [
+    "Now, let's copy themasked implementation that we have been using for our Gated PixelCNN models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Masked convolutions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "83mZFyondaAT"
+   },
+   "outputs": [],
+   "source": [
+    "class MaskedConv2D(keras.layers.Layer):\n",
+    "    \"\"\"Convolutional layers with masks extended to work with Gated PixelCNN.\n",
+    "\n",
+    "    Convolutional layers with simple implementation of masks type A and B for\n",
+    "    autoregressive models. Extended version to work with the verticala and horizontal\n",
+    "    stacks from the Gated PixelCNN model.\n",
+    "\n",
+    "    Arguments:\n",
+    "    mask_type: one of `\"V\"`, `\"A\"` or `\"B\".`\n",
+    "    filters: Integer, the dimensionality of the output space (i.e. the number of output\n",
+    "        filters in the convolution).\n",
+    "    kernel_size: An integer or tuple/list of 2 integers, specifying the height and width\n",
+    "        of the 2D convolution window.\n",
+    "        Can be a single integer to specify the same value for all spatial dimensions.\n",
+    "    strides: An integer or tuple/list of 2 integers, specifying the strides of the\n",
+    "        convolution along the height and width.\n",
+    "        Can be a single integer to specify the same value for all spatial dimensions.\n",
+    "        Specifying any stride value != 1 is incompatible with specifying any\n",
+    "        `dilation_rate` value != 1.\n",
+    "    padding: one of `\"valid\"` or `\"same\"` (case-insensitive).\n",
+    "    kernel_initializer: Initializer for the `kernel` weights matrix.\n",
+    "    bias_initializer: Initializer for the bias vector.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self,\n",
+    "                 mask_type,\n",
+    "                 filters,\n",
+    "                 kernel_size,\n",
+    "                 strides=1,\n",
+    "                 padding='same',\n",
+    "                 kernel_initializer='glorot_uniform',\n",
+    "                 bias_initializer='zeros'):\n",
+    "        super(MaskedConv2D, self).__init__()\n",
+    "\n",
+    "        assert mask_type in {'A', 'B', 'V'}\n",
+    "        self.mask_type = mask_type\n",
+    "\n",
+    "        self.filters = filters\n",
+    "\n",
+    "        if isinstance(kernel_size, int):\n",
+    "            kernel_size = (kernel_size, kernel_size)\n",
+    "        self.kernel_size = kernel_size\n",
+    "\n",
+    "        self.strides = strides\n",
+    "        self.padding = padding.upper()\n",
+    "        self.kernel_initializer = initializers.get(kernel_initializer)\n",
+    "        self.bias_initializer = initializers.get(bias_initializer)\n",
+    "\n",
+    "    def build(self, input_shape):\n",
+    "        kernel_h, kernel_w = self.kernel_size\n",
+    "\n",
+    "        self.kernel = self.add_weight('kernel',\n",
+    "                                      shape=(kernel_h,\n",
+    "                                             kernel_w,\n",
+    "                                             int(input_shape[-1]),\n",
+    "                                             self.filters),\n",
+    "                                      initializer=self.kernel_initializer,\n",
+    "                                      trainable=True)\n",
+    "\n",
+    "        self.bias = self.add_weight('bias',\n",
+    "                                    shape=(self.filters,),\n",
+    "                                    initializer=self.bias_initializer,\n",
+    "                                    trainable=True)\n",
+    "\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",
+    "            center_h = kernel_h // 2\n",
+    "        else:\n",
+    "            center_h = (kernel_h - 1) // 2\n",
+    "\n",
+    "        if kernel_w % 2 != 0:\n",
+    "            center_w = kernel_w // 2\n",
+    "        else:\n",
+    "            center_w = (kernel_w - 1) // 2\n",
+    "\n",
+    "        if self.mask_type == 'V':\n",
+    "            mask[center_h + 1:, :, :, :] = 0.\n",
+    "        else:\n",
+    "            mask[:center_h, :, :] = 0.\n",
+    "            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.float32, name='mask')\n",
+    "\n",
+    "    def call(self, input):\n",
+    "        masked_kernel = tf.math.multiply(self.mask, self.kernel)\n",
+    "        x = nn.conv2d(input,\n",
+    "                      masked_kernel,\n",
+    "                      strides=[1, self.strides, self.strides, 1],\n",
+    "                      padding=self.padding)\n",
+    "        x = nn.bias_add(x, self.bias)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this implementation, we will recreate all convolutional operation that occur inside of the Gated Block. These operations are:\n",
+    "\n",
+    "- Vertical stack\n",
+    "- Vertical to horizontal stack\n",
+    "- Horizontal stack - convolution layer with mask type \"A\"\n",
+    "- Horizontal stack - convolution layer with mask type \"B\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "                                              IMAGE GATED BLOCK\n",
+    "                                              \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "M62GZQe8ixvy"
+   },
+   "source": [
+    "## Vertical stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
     },
+    "colab_type": "code",
+    "id": "kjUrpEtIg7p9",
+    "outputId": "8df4b5ee-1d0e-4f7d-9009-beaf8c8133ee"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TFexYspQWEpo",
-        "colab_type": "code",
-        "outputId": "d0533bb8-38e8-482c-8e65-4f6522519087",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "conv.weights[0].numpy().squeeze()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[1., 1., 1.],\n",
-              "       [1., 1., 1.]], dtype=float32)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
-        }
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MASK\n",
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [0. 0. 0.]]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask_type = 'V'\n",
+    "kernel_size = (3, 3)\n",
+    "\n",
+    "conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                    filters=1,\n",
+    "                    kernel_size=kernel_size,\n",
+    "                    padding='same',\n",
+    "                    kernel_initializer='ones',\n",
+    "                    bias_initializer='zeros')\n",
+    "\n",
+    "result_v = conv(test_ones_2d)\n",
+    "\n",
+    "print('MASK')\n",
+    "print(conv.mask.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result_v.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "PFqvGa439Z2o"
+   },
+   "source": [
+    "## Vertical to horizontal stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
     },
+    "colab_type": "code",
+    "id": "oq_JTwdE9lr6",
+    "outputId": "a5eecd56-e068-4d9f-ff4b-01bd048f0bbb"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "AfyRyUmTNYZ8",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def build_test_croppedv_stack_2d(input_shape=(5, 5, 1), kernel_size=3):\n",
-        "  inputs = tf.keras.layers.Input(shape=input_shape)\n",
-        "  \n",
-        "  x = VerticalCroppedConv2d(\n",
-        "                   filters=1,\n",
-        "                   kernel_size=kernel_size, \n",
-        "                   kernel_initializer='ones', \n",
-        "                   bias_initializer='zeros')(inputs)\n",
-        "\n",
-        "  stack = tf.keras.Model(inputs=inputs, outputs=x)\n",
-        "  stack.compile(optimizer='adam', loss='mse')\n",
-        "  return stack"
-      ],
-      "execution_count": 0,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[0. 0. 0. 0. 0.]\n",
+      " [2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "padding = keras.layers.ZeroPadding2D(padding=((1, 0), 0))\n",
+    "cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
+    "\n",
+    "x = padding(result_v)\n",
+    "result = cropping(x)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(result_v.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "iHY6UE2_p5oc"
+   },
+   "source": [
+    "## Horizontal stack - convolution layer with mask type \"A\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
     },
+    "colab_type": "code",
+    "id": "4_q_IunZkFmj",
+    "outputId": "1bfa1a88-7ae3-4a7e-d43e-46e7e683cce4"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Nskk-zJwN3Em",
-        "colab_type": "text"
-      },
-      "source": [
-        "###Tests with kernel_size 3"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MASK\n",
+      "[1. 0. 0.]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask_type = 'A'\n",
+    "kernel_size = (1, 3)\n",
+    "\n",
+    "conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                    filters=1,\n",
+    "                    kernel_size=kernel_size,\n",
+    "                    padding='same',\n",
+    "                    kernel_initializer='ones',\n",
+    "                    bias_initializer='zeros')\n",
+    "\n",
+    "result = conv(test_ones_2d)\n",
+    "\n",
+    "print('MASK')\n",
+    "print(conv.mask.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "jMuS-vgWqAWK"
+   },
+   "source": [
+    "## Horizontal stack - convolution layer with mask type \"B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
     },
+    "colab_type": "code",
+    "id": "5yeB5h2tkSs_",
+    "outputId": "9e7346b9-d360-42dd-85b4-a9a22ba2bacb"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DdcDFcMbxwpZ",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Vertical stack"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MASK\n",
+      "[1. 1. 0.]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask_type = 'B'\n",
+    "kernel_size = (1, 3)\n",
+    "\n",
+    "conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                    filters=1,\n",
+    "                    kernel_size=kernel_size,\n",
+    "                    padding='same',\n",
+    "                    kernel_initializer='ones',\n",
+    "                    bias_initializer='zeros')\n",
+    "\n",
+    "result = conv(test_ones_2d)\n",
+    "\n",
+    "print('MASK')\n",
+    "print(conv.mask.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the results of the masked approach as reference, let's check the cropped method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kZmui789Br2B"
+   },
+   "source": [
+    "# Cropped and padded convolutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vertical stack\n",
+    "\n",
+    "First, let's checkout this operation that some strategic padding and applying the convolution in \"valid\" mode to achieve the same result from the masked version. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
     },
+    "colab_type": "code",
+    "id": "fH3I0lfoPdcH",
+    "outputId": "f142569c-f99d-4244-ab5d-1887243d0d29"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "jc06sHDoNzx8",
-        "colab_type": "code",
-        "outputId": "750bf826-6cf4-400b-f693-51ad6350fd15",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 187
-        }
-      },
-      "source": [
-        "kernel_size=(2, 3)\n",
-        "kernel_h, kernel_w = kernel_size\n",
-        "\n",
-        "padding2 = keras.layers.ZeroPadding2D(padding=((kernel_h-1, 0),(int((kernel_w-1)/2),int((kernel_w-1)/2))))\n",
-        "conv = keras.layers.Conv2D(filters=1,\n",
-        "                           kernel_size=kernel_size,\n",
-        "                           strides=1,\n",
-        "                           padding='valid',\n",
-        "                           kernel_initializer='ones', \n",
-        "                           bias_initializer='zeros')\n",
-        "\n",
-        "x = padding2(test_ones_2d)\n",
-        "result_v = conv(x)\n",
-        "\n",
-        "print('KERNEL')\n",
-        "print(conv.weights[0].numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result_v.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "KERNEL\n",
-            "[[1. 1. 1.]\n",
-            " [1. 1. 1.]]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[2. 3. 3. 3. 2.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "PADDED INPUT\n",
+      "[[0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 1. 1. 1. 1. 1. 0.]\n",
+      " [0. 1. 1. 1. 1. 1. 0.]\n",
+      " [0. 1. 1. 1. 1. 1. 0.]\n",
+      " [0. 1. 1. 1. 1. 1. 0.]\n",
+      " [0. 1. 1. 1. 1. 1. 0.]]\n",
+      "\n",
+      "CONV FILTER\n",
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_h = 2\n",
+    "kernel_w = 3\n",
+    "\n",
+    "kernel_size = (kernel_h, kernel_w)\n",
+    "\n",
+    "padding = keras.layers.ZeroPadding2D(padding=((kernel_h - 1, 0), (int((kernel_w - 1) / 2), int((kernel_w - 1) / 2))))\n",
+    "\n",
+    "res = padding(test_ones_2d)\n",
+    "\n",
+    "conv = keras.layers.Conv2D(filters=1,\n",
+    "                           kernel_size=kernel_size,\n",
+    "                           strides=1,\n",
+    "                           padding='valid',\n",
+    "                           kernel_initializer='ones',\n",
+    "                           bias_initializer='zeros')\n",
+    "\n",
+    "result_v = conv(res)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(test_ones_2d.squeeze())\n",
+    "print('')\n",
+    "print('PADDED INPUT')\n",
+    "print(res.numpy().squeeze())\n",
+    "print('')\n",
+    "print('CONV FILTER')\n",
+    "print(conv.weights[0].numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result_v.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "RfFuwKlrP2JU"
+   },
+   "source": [
+    "Now, let's implement a layer that we will include all the previous operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "PxBNsvzhB1ec"
+   },
+   "outputs": [],
+   "source": [
+    "class VerticalConv2D(keras.layers.Conv2D):\n",
+    "    \"\"\"https://github.com/JesseFarebro/PixelCNNPP/blob/master/layers/VerticalConv2D.py\"\"\"\n",
+    "\n",
+    "    def __init__(self,\n",
+    "                 filters,\n",
+    "                 kernel_size,\n",
+    "                 **kwargs):\n",
+    "        if not isinstance(kernel_size, tuple):\n",
+    "            kernel_size = (kernel_size // 2 + 1, kernel_size)\n",
+    "\n",
+    "        super(VerticalConv2D, self).__init__(filters, kernel_size, **kwargs)\n",
+    "\n",
+    "        self.pad = tf.keras.layers.ZeroPadding2D(\n",
+    "            (\n",
+    "                (kernel_size[0] - 1, 0),  # Top, Bottom\n",
+    "                (kernel_size[1] // 2, kernel_size[1] // 2),  # Left, Right\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        inputs = self.pad(inputs)\n",
+    "        output = super(VerticalConv2D, self).call(inputs)\n",
+    "\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "AfyRyUmTNYZ8"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "SvWpzQFGEGGm",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Feeding horizontal"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "CONV FILTER\n",
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_h = 2\n",
+    "kernel_w = 3\n",
+    "\n",
+    "kernel_size = (kernel_h, kernel_w)\n",
+    "\n",
+    "conv = VerticalConv2D(filters=1,\n",
+    "                      kernel_size=kernel_size,\n",
+    "                      strides=1,\n",
+    "                      padding='valid',\n",
+    "                      kernel_initializer='ones',\n",
+    "                      bias_initializer='zeros')\n",
+    "\n",
+    "result_v = conv(test_ones_2d)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(test_ones_2d.squeeze())\n",
+    "print('')\n",
+    "print('CONV FILTER')\n",
+    "print(conv.weights[0].numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result_v.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "SvWpzQFGEGGm"
+   },
+   "source": [
+    "## Vertical to horizontal stack\n",
+    "In this operation, the implementation continue the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
     },
+    "colab_type": "code",
+    "id": "5jLEZhYtOZgi",
+    "outputId": "d293ab6f-7fa6-4aec-cc68-9ab3ae7185dd"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5jLEZhYtOZgi",
-        "colab_type": "code",
-        "outputId": "d293ab6f-7fa6-4aec-cc68-9ab3ae7185dd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 119
-        }
-      },
-      "source": [
-        "padding = keras.layers.ZeroPadding2D(padding=((1,0),0))\n",
-        "cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
-        "\n",
-        "x = padding(result_v)\n",
-        "result = cropping(x)\n",
-        "\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "OUTPUT\n",
-            "[[0. 0. 0. 0. 0.]\n",
-            " [2. 3. 3. 3. 2.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]\n",
-            " [4. 6. 6. 6. 4.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n",
+      "\n",
+      "OUTPUT\n",
+      "[[0. 0. 0. 0. 0.]\n",
+      " [2. 3. 3. 3. 2.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]\n",
+      " [4. 6. 6. 6. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "padding = keras.layers.ZeroPadding2D(padding=((1, 0), 0))\n",
+    "cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
+    "\n",
+    "x = padding(result_v)\n",
+    "result = cropping(x)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(result_v.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "MQLekDEaEUUT"
+   },
+   "source": [
+    "## Horizontal stack - convolution layer with mask type \"A\"\n",
+    "Again, let's check each operation step by step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 170
     },
+    "colab_type": "code",
+    "id": "bHiwKZniEk5A",
+    "outputId": "ebd659c5-d899-4d6f-9c81-5c821cf4ea61"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MQLekDEaEUUT",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Horizontal stack A"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "CONV FILTER\n",
+      "1.0\n",
+      "\n",
+      "CONVOLUTION RESULT\n",
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "PADDED RESULT\n",
+      "[[0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "CROPPED RESULT\n",
+      "[[0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_size = (1, 1)\n",
+    "conv = keras.layers.Conv2D(filters=1,\n",
+    "                           kernel_size=kernel_size,\n",
+    "                           strides=1,\n",
+    "                           kernel_initializer='ones',\n",
+    "                           bias_initializer='zeros')\n",
+    "\n",
+    "padding = keras.layers.ZeroPadding2D(padding=(0, (1, 0)))\n",
+    "cropping = keras.layers.Cropping2D(cropping=(0, (0, 1)))\n",
+    "\n",
+    "res = conv(test_ones_2d)\n",
+    "res_2 = padding(res)\n",
+    "res_3 = cropping(res_2)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(test_ones_2d.squeeze())\n",
+    "print('')\n",
+    "print('CONV FILTER')\n",
+    "print(conv.weights[0].numpy().squeeze())\n",
+    "print('')\n",
+    "print('CONVOLUTION RESULT')\n",
+    "print(res.numpy().squeeze())\n",
+    "print('')\n",
+    "print('PADDED RESULT')\n",
+    "print(res_2.numpy().squeeze())\n",
+    "print('')\n",
+    "print('CROPPED RESULT')\n",
+    "print(res_3.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Since our input test just have one channel, the convolution 1x1 looks like did not perform any change."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "IvmGrDziEadf"
+   },
+   "source": [
+    "## Horizontal stack - convolution layer with mask type \"B\"\n",
+    "The step by step of the mask type \"B\" convolution layer is a little different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 170
     },
+    "colab_type": "code",
+    "id": "pRKJFE4TFx4I",
+    "outputId": "75d34ade-0983-49a5-f157-b98975c22560"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "bHiwKZniEk5A",
-        "colab_type": "code",
-        "outputId": "ebd659c5-d899-4d6f-9c81-5c821cf4ea61",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 170
-        }
-      },
-      "source": [
-        "kernel_size=(1, 1)\n",
-        "kernel_h, kernel_w = kernel_size\n",
-        "\n",
-        "conv = keras.layers.Conv2D(filters=1,\n",
-        "                           kernel_size=kernel_size,\n",
-        "                           strides=1,\n",
-        "                           kernel_initializer='ones', \n",
-        "                           bias_initializer='zeros')\n",
-        "\n",
-        "padding = keras.layers.ZeroPadding2D(padding=(0,(1,0)))\n",
-        "cropping = keras.layers.Cropping2D(cropping=(0, (0, 1)))\n",
-        "\n",
-        "x = conv(test_ones_2d)\n",
-        "x = padding(x)\n",
-        "result = cropping(x)\n",
-        "\n",
-        "print('KERNEL')\n",
-        "print(conv.weights[0].numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "KERNEL\n",
-            "1.0\n",
-            "\n",
-            "OUTPUT\n",
-            "[[0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]\n",
-            " [0. 1. 1. 1. 1.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "PADDED INPUT\n",
+      "[[0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]\n",
+      " [0. 1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "CONV FILTER\n",
+      "[1. 1.]\n",
+      "\n",
+      "RESULT\n",
+      "[[1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_size = (1, 2)\n",
+    "kernel_h, kernel_w = kernel_size\n",
+    "\n",
+    "padding = keras.layers.ZeroPadding2D(padding=((int((kernel_h - 1) / 2), int((kernel_h - 1) / 2)), (kernel_w - 1, 0)))\n",
+    "conv = keras.layers.Conv2D(filters=1,\n",
+    "                           kernel_size=kernel_size,\n",
+    "                           strides=1,\n",
+    "                           padding='valid',\n",
+    "                           kernel_initializer='ones',\n",
+    "                           bias_initializer='zeros')\n",
+    "\n",
+    "res = padding(test_ones_2d)\n",
+    "result = conv(res)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(test_ones_2d.squeeze())\n",
+    "print('')\n",
+    "print('PADDED INPUT')\n",
+    "print(res.numpy().squeeze())\n",
+    "print('')\n",
+    "print('CONV FILTER')\n",
+    "print(conv.weights[0].numpy().squeeze())\n",
+    "print('')\n",
+    "print('RESULT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, we also implemented a layer version encapsulation these operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class HorizontalConv2D(keras.layers.Conv2D):\n",
+    "    def __init__(self,\n",
+    "                 filters,\n",
+    "                 kernel_size,\n",
+    "                 **kwargs):\n",
+    "        if not isinstance(kernel_size, tuple):\n",
+    "            kernel_size = (kernel_size // 2 + 1,) * 2\n",
+    "\n",
+    "        super(HorizontalConv2D, self).__init__(filters, kernel_size, **kwargs)\n",
+    "        self.pad = tf.keras.layers.ZeroPadding2D(\n",
+    "            (\n",
+    "                (kernel_size[0] - 1, 0),  # (Top, Bottom)\n",
+    "                (kernel_size[1] - 1, 0),  # (Left, Right)\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    def call(self, inputs):\n",
+    "        inputs = self.pad(inputs)\n",
+    "        outputs = super(HorizontalConv2D, self).call(inputs)\n",
+    "\n",
+    "        return outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IvmGrDziEadf",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Horizontal stack B"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INPUT\n",
+      "[[1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      "CONV FILTER\n",
+      "[1. 1.]\n",
+      "\n",
+      "RESULT\n",
+      "[[1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]\n",
+      " [1. 2. 2. 2. 2.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kernel_size = (1, 2)\n",
+    "conv = HorizontalConv2D(filters=1,\n",
+    "                        kernel_size=kernel_size,\n",
+    "                        strides=1,\n",
+    "                        kernel_initializer='ones',\n",
+    "                        bias_initializer='zeros')\n",
+    "\n",
+    "result = conv(test_ones_2d)\n",
+    "\n",
+    "print('INPUT')\n",
+    "print(test_ones_2d.squeeze())\n",
+    "print('')\n",
+    "print('CONV FILTER')\n",
+    "print(conv.weights[0].numpy().squeeze())\n",
+    "print('')\n",
+    "print('RESULT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Execution time\n",
+    "Now we will compare the time that takes to perform each convolutional operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "def measure_time(conv_fn):\n",
+    "    exec_time = []\n",
+    "    n_iter = 100\n",
+    "    for _ in range(n_iter):\n",
+    "        test_input = np.random.rand(128, 256, 256, 1).astype('float32') \n",
+    "        start = time.time()\n",
+    "        conv_fn(test_input)\n",
+    "        exec_time.append(time.time() - start)\n",
+    "    exec_time = np.array(exec_time, dtype='float32')\n",
+    "    return exec_time.mean(), exec_time.std()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vertical stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "pRKJFE4TFx4I",
-        "colab_type": "code",
-        "outputId": "75d34ade-0983-49a5-f157-b98975c22560",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 170
-        }
-      },
-      "source": [
-        "kernel_size=(1, 2)\n",
-        "kernel_h, kernel_w = kernel_size\n",
-        "\n",
-        "padding2 = keras.layers.ZeroPadding2D(padding=((int((kernel_h-1)/2),int((kernel_h-1)/2)), (kernel_w-1, 0)))\n",
-        "conv = keras.layers.Conv2D(filters=1,\n",
-        "                           kernel_size=kernel_size,\n",
-        "                           strides=1,\n",
-        "                           padding='valid',\n",
-        "                           kernel_initializer='ones', \n",
-        "                           bias_initializer='zeros')\n",
-        "\n",
-        "\n",
-        "x = padding2(test_ones_2d)\n",
-        "result = conv(x)\n",
-        "\n",
-        "print('KERNEL')\n",
-        "print(conv.weights[0].numpy().squeeze())\n",
-        "print('')\n",
-        "print('OUTPUT')\n",
-        "print(result.numpy().squeeze())"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "KERNEL\n",
-            "[1. 1.]\n",
-            "\n",
-            "OUTPUT\n",
-            "[[1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]\n",
-            " [1. 2. 2. 2. 2.]]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vertical stack\n",
+      "Masked convolution:         0.01410292 +- 0.00891058 seconds\n",
+      "Cropped padded convolution: 0.01386628 +- 0.00675169 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask_type = 'V'\n",
+    "kernel_size = (3, 3)\n",
+    "masked_conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                    filters=32,\n",
+    "                    kernel_size=kernel_size,\n",
+    "                    padding='same',\n",
+    "                    kernel_initializer='ones',\n",
+    "                    bias_initializer='zeros')\n",
+    "\n",
+    "@tf.function\n",
+    "def test_masked_fn(x):\n",
+    "    _ = masked_conv(x)\n",
+    "    \n",
+    "\n",
+    "masked_time = measure_time(test_masked_fn)\n",
+    "# ----------------------------------------------------------------\n",
+    "\n",
+    "kernel_size = (2, 3)\n",
+    "cropped_conv = VerticalConv2D(filters=32,\n",
+    "                      kernel_size=kernel_size,\n",
+    "                      strides=1,\n",
+    "                      padding='valid',\n",
+    "                      kernel_initializer='ones',\n",
+    "                      bias_initializer='zeros')\n",
+    "\n",
+    "@tf.function\n",
+    "def test_cropped_fn(x):\n",
+    "    _ = cropped_conv(x)\n",
+    "\n",
+    "cropped_time = measure_time(test_cropped_fn)\n",
+    "# ----------------------------------------------------------------\n",
+    "\n",
+    "print(\"Vertical stack\")\n",
+    "print(f\"Masked convolution:         {masked_time[0]:.8f} +- {masked_time[1]:.8f} seconds\")\n",
+    "print(f\"Cropped padded convolution: {cropped_time[0]:.8f} +- {cropped_time[1]:.8f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Horizontal stack - convolution layer with mask type \"A\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "swYJ4XMofUWv",
-        "colab_type": "text"
-      },
-      "source": [
-        "REFERENCES\n",
-        "\n",
-        "https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Conditional_Image_Generation_with_PixelCNN_Decoders#Gated_PixelCNN\n",
-        "\n",
-        "https://www.slideshare.net/suga93/conditional-image-generation-with-pixelcnn-decoders\n",
-        "\n",
-        "https://www.youtube.com/watch?v=1BURwCCYNEI"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Horizontal stack - convolution layer with mask type 'A'\n",
+      "Masked convolution:         0.01360846 +- 0.00381987 seconds\n",
+      "Cropped padded convolution: 0.01365352 +- 0.00476047 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask_type = 'A'\n",
+    "kernel_size = (1, 3)\n",
+    "masked_conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                    filters=1,\n",
+    "                    kernel_size=kernel_size,\n",
+    "                    padding='same',\n",
+    "                    kernel_initializer='ones',\n",
+    "                    bias_initializer='zeros')\n",
+    "\n",
+    "@tf.function\n",
+    "def test_masked_fn(x):\n",
+    "    _ = masked_conv(x)\n",
+    "    \n",
+    "masked_time = measure_time(test_masked_fn)\n",
+    "# ----------------------------------------------------------------\n",
+    "\n",
+    "kernel_size = (1, 1)\n",
+    "conv = keras.layers.Conv2D(filters=1,\n",
+    "                           kernel_size=kernel_size,\n",
+    "                           strides=1,\n",
+    "                           kernel_initializer='ones',\n",
+    "                           bias_initializer='zeros')\n",
+    "\n",
+    "padding = keras.layers.ZeroPadding2D(padding=(0, (1, 0)))\n",
+    "cropping = keras.layers.Cropping2D(cropping=(0, (0, 1)))\n",
+    "\n",
+    "@tf.function\n",
+    "def test_cropped_fn(x):\n",
+    "    x = conv(x)\n",
+    "    x = padding(x)\n",
+    "    x = cropping(x)\n",
+    "\n",
+    "cropped_time = measure_time(test_cropped_fn)\n",
+    "# ----------------------------------------------------------------\n",
+    "\n",
+    "print(\"Horizontal stack - convolution layer with mask type 'A'\")\n",
+    "print(f\"Masked convolution:         {masked_time[0]:.8f} +- {masked_time[1]:.8f} seconds\")\n",
+    "print(f\"Cropped padded convolution: {cropped_time[0]:.8f} +- {cropped_time[1]:.8f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Horizontal stack - convolution layer with mask type \"B\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "jTI9ts7i7Wch",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": 0,
-      "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Horizontal stack - convolution layer with mask type 'B'\n",
+      "Masked convolution:         0.01353339 +- 0.00374499 seconds\n",
+      "Cropped padded convolution: 0.01384839 +- 0.00734248 seconds\n"
+     ]
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "mask_type = 'B'\n",
+    "kernel_size = (1, 3)\n",
+    "masked_conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                    filters=1,\n",
+    "                    kernel_size=kernel_size,\n",
+    "                    padding='same',\n",
+    "                    kernel_initializer='ones',\n",
+    "                    bias_initializer='zeros')\n",
+    "\n",
+    "@tf.function\n",
+    "def test_masked_fn(x):\n",
+    "    _ = masked_conv(x)\n",
+    "    \n",
+    "masked_time = measure_time(test_masked_fn)\n",
+    "# ----------------------------------------------------------------\n",
+    "\n",
+    "kernel_size = (1, 2)\n",
+    "cropped_conv = HorizontalConv2D(filters=1,\n",
+    "                        kernel_size=kernel_size,\n",
+    "                        strides=1,\n",
+    "                        kernel_initializer='ones',\n",
+    "                        bias_initializer='zeros')\n",
+    "\n",
+    "@tf.function\n",
+    "def test_cropped_fn(x):\n",
+    "    _ = cropped_conv(x)\n",
+    "\n",
+    "cropped_time = measure_time(test_cropped_fn)\n",
+    "# ----------------------------------------------------------------\n",
+    "\n",
+    "print(\"Horizontal stack - convolution layer with mask type 'B'\")\n",
+    "print(f\"Masked convolution:         {masked_time[0]:.8f} +- {masked_time[1]:.8f} seconds\")\n",
+    "print(f\"Cropped padded convolution: {cropped_time[0]:.8f} +- {cropped_time[1]:.8f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Altough its looks like cropped is better in the vertical convolution, the difference does not to look very significant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "swYJ4XMofUWv"
+   },
+   "source": [
+    "# REFERENCES\n",
+    "\n",
+    "https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Conditional_Image_Generation_with_PixelCNN_Decoders#Gated_PixelCNN\n",
+    "\n",
+    "https://www.slideshare.net/suga93/conditional-image-generation-with-pixelcnn-decoders\n",
+    "\n",
+    "https://www.youtube.com/watch?v=1BURwCCYNEI"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "gated_pixelcnn_vs_cropped.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/WIP/6-gated_pixelcnn_cropped/masked_vs_cropped_even_filter_size.ipynb b/WIP/6-gated_pixelcnn_cropped/masked_vs_cropped_even_filter_size.ipynb
new file mode 100644
index 0000000..42c3205
--- /dev/null
+++ b/WIP/6-gated_pixelcnn_cropped/masked_vs_cropped_even_filter_size.ipynb
@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tests with kernel_size 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask_type = 'V'\n",
+    "kernel_size=(4, 4)\n",
+    "\n",
+    "padding = keras.layers.ZeroPadding2D(padding=((1,0),0))\n",
+    "\n",
+    "conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                filters=1,\n",
+    "                kernel_size=kernel_size, \n",
+    "                padding='same',\n",
+    "                kernel_initializer='ones', \n",
+    "                bias_initializer='zeros')\n",
+    "\n",
+    "cropping = keras.layers.Cropping2D(cropping=((0, 1), 0))\n",
+    "\n",
+    "x = padding(test_ones_2d)\n",
+    "x = conv(x)\n",
+    "result = cropping(x)\n",
+    "\n",
+    "print('MASK')\n",
+    "print(conv.mask.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask_type = 'A'\n",
+    "kernel_size=(1, 4)\n",
+    "\n",
+    "conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                filters=1,\n",
+    "                kernel_size=kernel_size, \n",
+    "                padding='same',\n",
+    "                kernel_initializer='ones', \n",
+    "                bias_initializer='zeros')\n",
+    "\n",
+    "result = conv(test_ones_2d)\n",
+    "\n",
+    "print('MASK')\n",
+    "print(conv.mask.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask_type = 'B'\n",
+    "kernel_size=(1, 4)\n",
+    "\n",
+    "conv = MaskedConv2D(mask_type=mask_type,\n",
+    "                filters=1,\n",
+    "                kernel_size=kernel_size, \n",
+    "                padding='same',\n",
+    "                kernel_initializer='ones', \n",
+    "                bias_initializer='zeros')\n",
+    "\n",
+    "result = conv(test_ones_2d)\n",
+    "\n",
+    "print('MASK')\n",
+    "print(conv.mask.numpy().squeeze())\n",
+    "print('')\n",
+    "print('OUTPUT')\n",
+    "print(result.numpy().squeeze())"
+   ]
+  }
+ ],
+ "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}