diff --git "a/conv_lstm.ipynb" "b/conv_lstm.ipynb"
--- "a/conv_lstm.ipynb"
+++ "b/conv_lstm.ipynb"
@@ -1,430 +1,1255 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5iJqHKEQx66F"
+   },
+   "source": [
+    "# Next-Frame Video Prediction with Convolutional LSTMs\n",
+    "\n",
+    "**Author:** [Amogh Joshi](https://github.com/amogh7joshi)<br>\n",
+    "**Date created:** 2021/06/02<br>\n",
+    "**Last modified:** 2021/06/05<br>\n",
+    "**Description:** How to build and train a convolutional LSTM model for next-frame video prediction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9vv8zp4vx66K"
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "The\n",
+    "[Convolutional LSTM](https://papers.nips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf)\n",
+    "architectures bring together time series processing and computer vision by\n",
+    "introducing a convolutional recurrent cell in a LSTM layer. In this example, we will explore the\n",
+    "Convolutional LSTM model in an application to next-frame prediction, the process\n",
+    "of predicting what video frames come next given a series of past frames."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "daG-n305x66K"
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install imageio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "4Xx9qttUx66L"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "\n",
+    "import io\n",
+    "import imageio\n",
+    "from IPython.display import Image, display\n",
+    "from ipywidgets import widgets, Layout, HBox"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "w-uOOdg1x66M"
+   },
+   "source": [
+    "## Dataset Construction\n",
+    "\n",
+    "For this example, we will be using the\n",
+    "[Moving MNIST](http://www.cs.toronto.edu/~nitish/unsupervised_video/)\n",
+    "dataset.\n",
+    "\n",
+    "We will download the dataset and then construct and\n",
+    "preprocess training and validation sets.\n",
+    "\n",
+    "For next-frame prediction, our model will be using a previous frame,\n",
+    "which we'll call `f_n`, to predict a new frame, called `f_(n + 1)`.\n",
+    "To allow the model to create these predictions, we'll need to process\n",
+    "the data such that we have \"shifted\" inputs and outputs, where the\n",
+    "input data is frame `x_n`, being used to predict frame `y_(n + 1)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "H6_vt6q4x66N"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5iJqHKEQx66F"
-      },
-      "source": [
-        "# Next-Frame Video Prediction with Convolutional LSTMs\n",
-        "\n",
-        "**Author:** [Amogh Joshi](https://github.com/amogh7joshi)<br>\n",
-        "**Date created:** 2021/06/02<br>\n",
-        "**Last modified:** 2021/06/05<br>\n",
-        "**Description:** How to build and train a convolutional LSTM model for next-frame video prediction."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy\n",
+      "819208192/819200096 [==============================] - 8s 0us/step\n",
+      "819216384/819200096 [==============================] - 8s 0us/step\n",
+      "Training Dataset Shapes: (900, 19, 64, 64, 1), (900, 19, 64, 64, 1)\n",
+      "Validation Dataset Shapes: (100, 19, 64, 64, 1), (100, 19, 64, 64, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download and load the dataset.\n",
+    "fpath = keras.utils.get_file(\n",
+    "    \"moving_mnist.npy\",\n",
+    "    \"http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy\",\n",
+    ")\n",
+    "dataset = np.load(fpath)\n",
+    "\n",
+    "# Swap the axes representing the number of frames and number of data samples.\n",
+    "dataset = np.swapaxes(dataset, 0, 1)\n",
+    "# We'll pick out 1000 of the 10000 total examples and use those.\n",
+    "dataset = dataset[:1000, ...]\n",
+    "# Add a channel dimension since the images are grayscale.\n",
+    "dataset = np.expand_dims(dataset, axis=-1)\n",
+    "\n",
+    "# Split into train and validation sets using indexing to optimize memory.\n",
+    "indexes = np.arange(dataset.shape[0])\n",
+    "np.random.shuffle(indexes)\n",
+    "train_index = indexes[: int(0.9 * dataset.shape[0])]\n",
+    "val_index = indexes[int(0.9 * dataset.shape[0]) :]\n",
+    "train_dataset = dataset[train_index]\n",
+    "val_dataset = dataset[val_index]\n",
+    "\n",
+    "# Normalize the data to the 0-1 range.\n",
+    "train_dataset = train_dataset / 255\n",
+    "val_dataset = val_dataset / 255\n",
+    "\n",
+    "# We'll define a helper function to shift the frames, where\n",
+    "# `x` is frames 0 to n - 1, and `y` is frames 1 to n.\n",
+    "def create_shifted_frames(data):\n",
+    "    x = data[:, 0 : data.shape[1] - 1, :, :]\n",
+    "    y = data[:, 1 : data.shape[1], :, :]\n",
+    "    return x, y\n",
+    "\n",
+    "\n",
+    "# Apply the processing function to the datasets.\n",
+    "x_train, y_train = create_shifted_frames(train_dataset)\n",
+    "x_val, y_val = create_shifted_frames(val_dataset)\n",
+    "\n",
+    "# Inspect the dataset.\n",
+    "print(\"Training Dataset Shapes: \" + str(x_train.shape) + \", \" + str(y_train.shape))\n",
+    "print(\"Validation Dataset Shapes: \" + str(x_val.shape) + \", \" + str(y_val.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wJhm7oM7x66O"
+   },
+   "source": [
+    "## Data Visualization\n",
+    "\n",
+    "Our data consists of sequences of frames, each of which\n",
+    "are used to predict the upcoming frame. Let's take a look\n",
+    "at some of these sequential frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "jFE2fY1xx66O"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9vv8zp4vx66K"
-      },
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "The\n",
-        "[Convolutional LSTM](https://papers.nips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf)\n",
-        "architectures bring together time series processing and computer vision by\n",
-        "introducing a convolutional recurrent cell in a LSTM layer. In this example, we will explore the\n",
-        "Convolutional LSTM model in an application to next-frame prediction, the process\n",
-        "of predicting what video frames come next given a series of past frames."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Displaying frames for example 818.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "daG-n305x66K"
-      },
-      "source": [
-        "## Setup"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 20 Axes>"
       ]
-    },
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Construct a figure on which we will visualize the images.\n",
+    "fig, axes = plt.subplots(4, 5, figsize=(10, 8))\n",
+    "\n",
+    "# Plot each of the sequential images for one random data example.\n",
+    "data_choice = np.random.choice(range(len(train_dataset)), size=1)[0]\n",
+    "for idx, ax in enumerate(axes.flat):\n",
+    "    ax.imshow(np.squeeze(train_dataset[data_choice][idx]), cmap=\"gray\")\n",
+    "    ax.set_title(f\"Frame {idx + 1}\")\n",
+    "    ax.axis(\"off\")\n",
+    "\n",
+    "# Print information and display the figure.\n",
+    "print(f\"Displaying frames for example {data_choice}.\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jPQQIUm6x66P"
+   },
+   "source": [
+    "## Model Construction\n",
+    "\n",
+    "To build a Convolutional LSTM model, we will use the\n",
+    "`ConvLSTM2D` layer, which will accept inputs of shape\n",
+    "`(batch_size, num_frames, width, height, channels)`, and return\n",
+    "a prediction movie of the same shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "D3OvRaVpx66P"
+   },
+   "outputs": [],
+   "source": [
+    "# Construct the input layer with no definite frame size.\n",
+    "inp = layers.Input(shape=(None, *x_train.shape[2:]))\n",
+    "\n",
+    "# We will construct 3 `ConvLSTM2D` layers with batch normalization,\n",
+    "# followed by a `Conv3D` layer for the spatiotemporal outputs.\n",
+    "x = layers.ConvLSTM2D(\n",
+    "    filters=64,\n",
+    "    kernel_size=(5, 5),\n",
+    "    padding=\"same\",\n",
+    "    return_sequences=True,\n",
+    "    activation=\"relu\",\n",
+    ")(inp)\n",
+    "x = layers.BatchNormalization()(x)\n",
+    "x = layers.ConvLSTM2D(\n",
+    "    filters=64,\n",
+    "    kernel_size=(3, 3),\n",
+    "    padding=\"same\",\n",
+    "    return_sequences=True,\n",
+    "    activation=\"relu\",\n",
+    ")(x)\n",
+    "x = layers.BatchNormalization()(x)\n",
+    "x = layers.ConvLSTM2D(\n",
+    "    filters=64,\n",
+    "    kernel_size=(1, 1),\n",
+    "    padding=\"same\",\n",
+    "    return_sequences=True,\n",
+    "    activation=\"relu\",\n",
+    ")(x)\n",
+    "x = layers.Conv3D(\n",
+    "    filters=1, kernel_size=(3, 3, 3), activation=\"sigmoid\", padding=\"same\"\n",
+    ")(x)\n",
+    "\n",
+    "# Next, we will build the complete model and compile it.\n",
+    "model = keras.models.Model(inp, x)\n",
+    "model.compile(\n",
+    "    loss=keras.losses.binary_crossentropy, optimizer=keras.optimizers.Adam(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Nd0VLhrvx66Q"
+   },
+   "source": [
+    "## Model Training\n",
+    "\n",
+    "With our model and data constructed, we can now train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4Xx9qttUx66L"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "import tensorflow as tf\n",
-        "from tensorflow import keras\n",
-        "from tensorflow.keras import layers\n",
-        "\n",
-        "import io\n",
-        "import imageio\n",
-        "from IPython.display import Image, display\n",
-        "from ipywidgets import widgets, Layout, HBox"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnouamanetazi\u001b[0m (use `wandb login --relogin` to force relogin)\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "w-uOOdg1x66M"
-      },
-      "source": [
-        "## Dataset Construction\n",
-        "\n",
-        "For this example, we will be using the\n",
-        "[Moving MNIST](http://www.cs.toronto.edu/~nitish/unsupervised_video/)\n",
-        "dataset.\n",
-        "\n",
-        "We will download the dataset and then construct and\n",
-        "preprocess training and validation sets.\n",
-        "\n",
-        "For next-frame prediction, our model will be using a previous frame,\n",
-        "which we'll call `f_n`, to predict a new frame, called `f_(n + 1)`.\n",
-        "To allow the model to create these predictions, we'll need to process\n",
-        "the data such that we have \"shifted\" inputs and outputs, where the\n",
-        "input data is frame `x_n`, being used to predict frame `y_(n + 1)`."
+     "data": {
+      "text/html": [
+       "\n",
+       "                    Syncing run <strong><a href=\"https://wandb.ai/nouamanetazi/draft_conv_lstm/runs/35p3smgd\" target=\"_blank\">dry-wave-1</a></strong> to <a href=\"https://wandb.ai/nouamanetazi/draft_conv_lstm\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
+       "\n",
+       "                "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "H6_vt6q4x66N"
-      },
-      "outputs": [],
-      "source": [
-        "# Download and load the dataset.\n",
-        "fpath = keras.utils.get_file(\n",
-        "    \"moving_mnist.npy\",\n",
-        "    \"http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy\",\n",
-        ")\n",
-        "dataset = np.load(fpath)\n",
-        "\n",
-        "# Swap the axes representing the number of frames and number of data samples.\n",
-        "dataset = np.swapaxes(dataset, 0, 1)\n",
-        "# We'll pick out 1000 of the 10000 total examples and use those.\n",
-        "dataset = dataset[:1000, ...]\n",
-        "# Add a channel dimension since the images are grayscale.\n",
-        "dataset = np.expand_dims(dataset, axis=-1)\n",
-        "\n",
-        "# Split into train and validation sets using indexing to optimize memory.\n",
-        "indexes = np.arange(dataset.shape[0])\n",
-        "np.random.shuffle(indexes)\n",
-        "train_index = indexes[: int(0.9 * dataset.shape[0])]\n",
-        "val_index = indexes[int(0.9 * dataset.shape[0]) :]\n",
-        "train_dataset = dataset[train_index]\n",
-        "val_dataset = dataset[val_index]\n",
-        "\n",
-        "# Normalize the data to the 0-1 range.\n",
-        "train_dataset = train_dataset / 255\n",
-        "val_dataset = val_dataset / 255\n",
-        "\n",
-        "# We'll define a helper function to shift the frames, where\n",
-        "# `x` is frames 0 to n - 1, and `y` is frames 1 to n.\n",
-        "def create_shifted_frames(data):\n",
-        "    x = data[:, 0 : data.shape[1] - 1, :, :]\n",
-        "    y = data[:, 1 : data.shape[1], :, :]\n",
-        "    return x, y\n",
-        "\n",
-        "\n",
-        "# Apply the processing function to the datasets.\n",
-        "x_train, y_train = create_shifted_frames(train_dataset)\n",
-        "x_val, y_val = create_shifted_frames(val_dataset)\n",
-        "\n",
-        "# Inspect the dataset.\n",
-        "print(\"Training Dataset Shapes: \" + str(x_train.shape) + \", \" + str(y_train.shape))\n",
-        "print(\"Validation Dataset Shapes: \" + str(x_val.shape) + \", \" + str(y_val.shape))"
+     "data": {
+      "text/html": [
+       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/nouamanetazi/draft_conv_lstm/runs/35p3smgd?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"
+      ],
+      "text/plain": [
+       "<wandb.sdk.wandb_run.Run at 0x7f44ece3cfa0>"
       ]
-    },
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import wandb\n",
+    "from wandb.keras import WandbCallback\n",
+    "\n",
+    "wandb.init(config={\"hyper\": \"parameter\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "v9U57leux66Q",
+    "tags": []
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wJhm7oM7x66O"
-      },
-      "source": [
-        "## Data Visualization\n",
-        "\n",
-        "Our data consists of sequences of frames, each of which\n",
-        "are used to predict the upcoming frame. Let's take a look\n",
-        "at some of these sequential frames."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "180/180 [==============================] - ETA: 0s - loss: 0.1426"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "jFE2fY1xx66O"
-      },
-      "outputs": [],
-      "source": [
-        "# Construct a figure on which we will visualize the images.\n",
-        "fig, axes = plt.subplots(4, 5, figsize=(10, 8))\n",
-        "\n",
-        "# Plot each of the sequential images for one random data example.\n",
-        "data_choice = np.random.choice(range(len(train_dataset)), size=1)[0]\n",
-        "for idx, ax in enumerate(axes.flat):\n",
-        "    ax.imshow(np.squeeze(train_dataset[data_choice][idx]), cmap=\"gray\")\n",
-        "    ax.set_title(f\"Frame {idx + 1}\")\n",
-        "    ax.axis(\"off\")\n",
-        "\n",
-        "# Print information and display the figure.\n",
-        "print(f\"Displaying frames for example {data_choice}.\")\n",
-        "plt.show()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-02-13 20:41:53.410735: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:828] layout failed: INVALID_ARGUMENT: MutableGraphView::SortTopologically error: detected edge(s) creating cycle(s) {'model/conv_lstm2d_2/while/body/_97/model/conv_lstm2d_2/while/Relu_1' -> 'model/conv_lstm2d_2/while/body/_97/model/conv_lstm2d_2/while/mul_5', 'model/conv_lstm2d_2/while/body/_97/model/conv_lstm2d_2/while/mul_2' -> 'model/conv_lstm2d_2/while/body/_97/model/conv_lstm2d_2/while/add_5', 'model/conv_lstm2d_2/while/body/_97/model/conv_lstm2d_2/while/convolution_7' -> 'model/conv_lstm2d_2/while/body/_97/model/conv_lstm2d_2/while/add_6', 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/mul_2' -> 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/add_5', 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/clip_by_value' -> 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/mul_3', 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/clip_by_value_2' -> 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/mul_5', 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/convolution_6' -> 'model/conv_lstm2d_1/while/body/_49/model/conv_lstm2d_1/while/add_4'}.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jPQQIUm6x66P"
-      },
-      "source": [
-        "## Model Construction\n",
-        "\n",
-        "To build a Convolutional LSTM model, we will use the\n",
-        "`ConvLSTM2D` layer, which will accept inputs of shape\n",
-        "`(batch_size, num_frames, width, height, channels)`, and return\n",
-        "a prediction movie of the same shape."
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "180/180 [==============================] - 132s 732ms/step - loss: 0.1426 - val_loss: 0.3393 - lr: 0.0010\n",
+      "Epoch 2/20\n",
+      "180/180 [==============================] - 133s 736ms/step - loss: 0.1070 - val_loss: 0.2899 - lr: 0.0010\n",
+      "Epoch 3/20\n",
+      "180/180 [==============================] - 131s 730ms/step - loss: 0.0473 - val_loss: 0.2707 - lr: 0.0010\n",
+      "Epoch 4/20\n",
+      "180/180 [==============================] - 133s 739ms/step - loss: 0.0317 - val_loss: 0.2118 - lr: 0.0010\n",
+      "Epoch 5/20\n",
+      "180/180 [==============================] - 132s 731ms/step - loss: 0.0292 - val_loss: 0.1803 - lr: 0.0010\n",
+      "Epoch 6/20\n",
+      "180/180 [==============================] - 133s 737ms/step - loss: 0.0281 - val_loss: 0.1553 - lr: 0.0010\n",
+      "Epoch 7/20\n",
+      "180/180 [==============================] - 132s 735ms/step - loss: 0.0274 - val_loss: 0.1472 - lr: 0.0010\n",
+      "Epoch 8/20\n",
+      "180/180 [==============================] - 132s 735ms/step - loss: 0.0270 - val_loss: 0.1390 - lr: 0.0010\n",
+      "Epoch 9/20\n",
+      "180/180 [==============================] - 133s 739ms/step - loss: 0.0267 - val_loss: 0.1250 - lr: 0.0010\n",
+      "Epoch 10/20\n",
+      "180/180 [==============================] - 132s 733ms/step - loss: 0.0264 - val_loss: 0.1163 - lr: 0.0010\n",
+      "Epoch 11/20\n",
+      "180/180 [==============================] - 133s 741ms/step - loss: 0.0263 - val_loss: 0.1003 - lr: 0.0010\n",
+      "Epoch 12/20\n",
+      "180/180 [==============================] - 131s 730ms/step - loss: 0.0261 - val_loss: 0.1040 - lr: 0.0010\n",
+      "Epoch 13/20\n",
+      "180/180 [==============================] - 131s 730ms/step - loss: 0.0260 - val_loss: 0.0865 - lr: 0.0010\n",
+      "Epoch 14/20\n",
+      "180/180 [==============================] - 131s 730ms/step - loss: 0.0259 - val_loss: 0.0881 - lr: 0.0010\n",
+      "Epoch 15/20\n",
+      "180/180 [==============================] - 133s 737ms/step - loss: 0.0258 - val_loss: 0.0695 - lr: 0.0010\n",
+      "Epoch 16/20\n",
+      "180/180 [==============================] - 133s 737ms/step - loss: 0.0257 - val_loss: 0.0521 - lr: 0.0010\n",
+      "Epoch 17/20\n",
+      "180/180 [==============================] - 132s 734ms/step - loss: 0.0255 - val_loss: 0.0302 - lr: 0.0010\n",
+      "Epoch 18/20\n",
+      "180/180 [==============================] - 133s 740ms/step - loss: 0.0254 - val_loss: 0.0261 - lr: 0.0010\n",
+      "Epoch 19/20\n",
+      "180/180 [==============================] - 132s 733ms/step - loss: 0.0253 - val_loss: 0.0255 - lr: 0.0010\n",
+      "Epoch 20/20\n",
+      "180/180 [==============================] - 133s 741ms/step - loss: 0.0252 - val_loss: 0.0254 - lr: 0.0010\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "D3OvRaVpx66P"
-      },
-      "outputs": [],
-      "source": [
-        "# Construct the input layer with no definite frame size.\n",
-        "inp = layers.Input(shape=(None, *x_train.shape[2:]))\n",
-        "\n",
-        "# We will construct 3 `ConvLSTM2D` layers with batch normalization,\n",
-        "# followed by a `Conv3D` layer for the spatiotemporal outputs.\n",
-        "x = layers.ConvLSTM2D(\n",
-        "    filters=64,\n",
-        "    kernel_size=(5, 5),\n",
-        "    padding=\"same\",\n",
-        "    return_sequences=True,\n",
-        "    activation=\"relu\",\n",
-        ")(inp)\n",
-        "x = layers.BatchNormalization()(x)\n",
-        "x = layers.ConvLSTM2D(\n",
-        "    filters=64,\n",
-        "    kernel_size=(3, 3),\n",
-        "    padding=\"same\",\n",
-        "    return_sequences=True,\n",
-        "    activation=\"relu\",\n",
-        ")(x)\n",
-        "x = layers.BatchNormalization()(x)\n",
-        "x = layers.ConvLSTM2D(\n",
-        "    filters=64,\n",
-        "    kernel_size=(1, 1),\n",
-        "    padding=\"same\",\n",
-        "    return_sequences=True,\n",
-        "    activation=\"relu\",\n",
-        ")(x)\n",
-        "x = layers.Conv3D(\n",
-        "    filters=1, kernel_size=(3, 3, 3), activation=\"sigmoid\", padding=\"same\"\n",
-        ")(x)\n",
-        "\n",
-        "# Next, we will build the complete model and compile it.\n",
-        "model = keras.models.Model(inp, x)\n",
-        "model.compile(\n",
-        "    loss=keras.losses.binary_crossentropy, optimizer=keras.optimizers.Adam(),\n",
-        ")"
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f444b588be0>"
       ]
-    },
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define some callbacks to improve training.\n",
+    "early_stopping = keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10)\n",
+    "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", patience=5)\n",
+    "\n",
+    "# Define modifiable training hyperparameters.\n",
+    "epochs = 20\n",
+    "batch_size = 5\n",
+    "\n",
+    "# Fit the model to the training data.\n",
+    "model.fit(\n",
+    "    x_train,\n",
+    "    y_train,\n",
+    "    batch_size=batch_size,\n",
+    "    epochs=epochs,\n",
+    "    validation_data=(x_val, y_val),\n",
+    "    callbacks=[early_stopping, reduce_lr, WandbCallback()],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RxB7zZIxx66R"
+   },
+   "source": [
+    "## Frame Prediction Visualizations\n",
+    "\n",
+    "With our model now constructed and trained, we can generate\n",
+    "some example frame predictions based on a new video.\n",
+    "\n",
+    "We'll pick a random example from the validation set and\n",
+    "then choose the first ten frames from them. From there, we can\n",
+    "allow the model to predict 10 new frames, which we can compare\n",
+    "to the ground truth frame predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "qsujRd4Ex66R"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Nd0VLhrvx66Q"
-      },
-      "source": [
-        "## Model Training\n",
-        "\n",
-        "With our model and data constructed, we can now train the model."
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x288 with 20 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select a random example from the validation dataset.\n",
+    "example = val_dataset[np.random.choice(range(len(val_dataset)), size=1)[0]]\n",
+    "\n",
+    "# Pick the first/last ten frames from the example.\n",
+    "frames = example[:10, ...]\n",
+    "original_frames = example[10:, ...]\n",
+    "\n",
+    "# Predict a new set of 10 frames.\n",
+    "for _ in range(10):\n",
+    "    # Extract the model's prediction and post-process it.\n",
+    "    new_prediction = model.predict(np.expand_dims(frames, axis=0))\n",
+    "    new_prediction = np.squeeze(new_prediction, axis=0)\n",
+    "    predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
+    "\n",
+    "    # Extend the set of prediction frames.\n",
+    "    frames = np.concatenate((frames, predicted_frame), axis=0)\n",
+    "\n",
+    "# Construct a figure for the original and new frames.\n",
+    "fig, axes = plt.subplots(2, 10, figsize=(20, 4))\n",
+    "\n",
+    "# Plot the original frames.\n",
+    "for idx, ax in enumerate(axes[0]):\n",
+    "    ax.imshow(np.squeeze(original_frames[idx]), cmap=\"gray\")\n",
+    "    ax.set_title(f\"Frame {idx + 11}\")\n",
+    "    ax.axis(\"off\")\n",
+    "\n",
+    "# Plot the new frames.\n",
+    "new_frames = frames[10:, ...]\n",
+    "for idx, ax in enumerate(axes[1]):\n",
+    "    ax.imshow(np.squeeze(new_frames[idx]), cmap=\"gray\")\n",
+    "    ax.set_title(f\"Frame {idx + 11}\")\n",
+    "    ax.axis(\"off\")\n",
+    "\n",
+    "# Display the figure.\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "78OrJXZfx66R",
+    "tags": []
+   },
+   "source": [
+    "## Predicted Videos\n",
+    "\n",
+    "Finally, we'll pick a few examples from the validation set\n",
+    "and construct some GIFs with them to see the model's\n",
+    "predicted videos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "ncMx34rLx66R",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Truth\tPrediction\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "v9U57leux66Q"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b90a786a416d4775ae3a591728680f66",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "# Define some callbacks to improve training.\n",
-        "early_stopping = keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10)\n",
-        "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", patience=5)\n",
-        "\n",
-        "# Define modifiable training hyperparameters.\n",
-        "epochs = 20\n",
-        "batch_size = 5\n",
-        "\n",
-        "# Fit the model to the training data.\n",
-        "model.fit(\n",
-        "    x_train,\n",
-        "    y_train,\n",
-        "    batch_size=batch_size,\n",
-        "    epochs=epochs,\n",
-        "    validation_data=(x_val, y_val),\n",
-        "    callbacks=[early_stopping, reduce_lr],\n",
-        ")"
+      "text/plain": [
+       "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x87\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xfb\\…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RxB7zZIxx66R"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "703b0dae94b74b0d99d678b44b21c7ce",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Frame Prediction Visualizations\n",
-        "\n",
-        "With our model now constructed and trained, we can generate\n",
-        "some example frame predictions based on a new video.\n",
-        "\n",
-        "We'll pick a random example from the validation set and\n",
-        "then choose the first ten frames from them. From there, we can\n",
-        "allow the model to predict 10 new frames, which we can compare\n",
-        "to the ground truth frame predictions."
+      "text/plain": [
+       "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x87\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xf2\\xf2\\xf2\\xee\\…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qsujRd4Ex66R"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f9b5b502ee04203a3b6e540d8a1d303",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "# Select a random example from the validation dataset.\n",
-        "example = val_dataset[np.random.choice(range(len(val_dataset)), size=1)[0]]\n",
-        "\n",
-        "# Pick the first/last ten frames from the example.\n",
-        "frames = example[:10, ...]\n",
-        "original_frames = example[10:, ...]\n",
-        "\n",
-        "# Predict a new set of 10 frames.\n",
-        "for _ in range(10):\n",
-        "    # Extract the model's prediction and post-process it.\n",
-        "    new_prediction = model.predict(np.expand_dims(frames, axis=0))\n",
-        "    new_prediction = np.squeeze(new_prediction, axis=0)\n",
-        "    predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
-        "\n",
-        "    # Extend the set of prediction frames.\n",
-        "    frames = np.concatenate((frames, predicted_frame), axis=0)\n",
-        "\n",
-        "# Construct a figure for the original and new frames.\n",
-        "fig, axes = plt.subplots(2, 10, figsize=(20, 4))\n",
-        "\n",
-        "# Plot the original frames.\n",
-        "for idx, ax in enumerate(axes[0]):\n",
-        "    ax.imshow(np.squeeze(original_frames[idx]), cmap=\"gray\")\n",
-        "    ax.set_title(f\"Frame {idx + 11}\")\n",
-        "    ax.axis(\"off\")\n",
-        "\n",
-        "# Plot the new frames.\n",
-        "new_frames = frames[10:, ...]\n",
-        "for idx, ax in enumerate(axes[1]):\n",
-        "    ax.imshow(np.squeeze(new_frames[idx]), cmap=\"gray\")\n",
-        "    ax.set_title(f\"Frame {idx + 11}\")\n",
-        "    ax.axis(\"off\")\n",
-        "\n",
-        "# Display the figure.\n",
-        "plt.show()"
+      "text/plain": [
+       "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x87\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xf8\\…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "78OrJXZfx66R"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cdc967f2552419cbe9512f96434a6fd",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Predicted Videos\n",
-        "\n",
-        "Finally, we'll pick a few examples from the validation set\n",
-        "and construct some GIFs with them to see the model's\n",
-        "predicted videos."
+      "text/plain": [
+       "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x87\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xfb\\…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ncMx34rLx66R"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a3b4d93c8ef4d38a986796dbef26bed",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "# Select a few random examples from the dataset.\n",
-        "examples = val_dataset[np.random.choice(range(len(val_dataset)), size=5)]\n",
-        "\n",
-        "# Iterate over the examples and predict the frames.\n",
-        "predicted_videos = []\n",
-        "for example in examples:\n",
-        "    # Pick the first/last ten frames from the example.\n",
-        "    frames = example[:10, ...]\n",
-        "    original_frames = example[10:, ...]\n",
-        "    new_predictions = np.zeros(shape=(10, *frames[0].shape))\n",
-        "\n",
-        "    # Predict a new set of 10 frames.\n",
-        "    for i in range(10):\n",
-        "        # Extract the model's prediction and post-process it.\n",
-        "        frames = example[: 10 + i + 1, ...]\n",
-        "        new_prediction = model.predict(np.expand_dims(frames, axis=0))\n",
-        "        new_prediction = np.squeeze(new_prediction, axis=0)\n",
-        "        predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
-        "\n",
-        "        # Extend the set of prediction frames.\n",
-        "        new_predictions[i] = predicted_frame\n",
-        "\n",
-        "    # Create and save GIFs for each of the ground truth/prediction images.\n",
-        "    for frame_set in [original_frames, new_predictions]:\n",
-        "        # Construct a GIF from the selected video frames.\n",
-        "        current_frames = np.squeeze(frame_set)\n",
-        "        current_frames = current_frames[..., np.newaxis] * np.ones(3)\n",
-        "        current_frames = (current_frames * 255).astype(np.uint8)\n",
-        "        current_frames = list(current_frames)\n",
-        "\n",
-        "        # Construct a GIF from the frames.\n",
-        "        with io.BytesIO() as gif:\n",
-        "            imageio.mimsave(gif, current_frames, \"GIF\", fps=5)\n",
-        "            predicted_videos.append(gif.getvalue())\n",
-        "\n",
-        "# Display the videos.\n",
-        "print(\" Truth\\tPrediction\")\n",
-        "for i in range(0, len(predicted_videos), 2):\n",
-        "    # Construct and display an `HBox` with the ground truth and prediction.\n",
-        "    box = HBox(\n",
-        "        [\n",
-        "            widgets.Image(value=predicted_videos[i]),\n",
-        "            widgets.Image(value=predicted_videos[i + 1]),\n",
-        "        ]\n",
-        "    )\n",
-        "    display(box)"
+      "text/plain": [
+       "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x87\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xfb\\…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "name": "conv_lstm",
-      "provenance": [],
-      "toc_visible": true
+   ],
+   "source": [
+    "# Select a few random examples from the dataset.\n",
+    "examples = val_dataset[np.random.choice(range(len(val_dataset)), size=5)]\n",
+    "\n",
+    "# Iterate over the examples and predict the frames.\n",
+    "predicted_videos = []\n",
+    "for example in examples:\n",
+    "    # Pick the first/last ten frames from the example.\n",
+    "    frames = example[:10, ...]\n",
+    "    original_frames = example[10:, ...]\n",
+    "    new_predictions = np.zeros(shape=(10, *frames[0].shape))\n",
+    "\n",
+    "    # Predict a new set of 10 frames.\n",
+    "    for i in range(10):\n",
+    "        # Extract the model's prediction and post-process it.\n",
+    "        frames = example[: 10 + i + 1, ...]\n",
+    "        new_prediction = model.predict(np.expand_dims(frames, axis=0))\n",
+    "        new_prediction = np.squeeze(new_prediction, axis=0)\n",
+    "        predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
+    "\n",
+    "        # Extend the set of prediction frames.\n",
+    "        new_predictions[i] = predicted_frame\n",
+    "\n",
+    "    # Create and save GIFs for each of the ground truth/prediction images.\n",
+    "    for frame_set in [original_frames, new_predictions]:\n",
+    "        # Construct a GIF from the selected video frames.\n",
+    "        current_frames = np.squeeze(frame_set)\n",
+    "        current_frames = current_frames[..., np.newaxis] * np.ones(3)\n",
+    "        current_frames = (current_frames * 255).astype(np.uint8)\n",
+    "        current_frames = list(current_frames)\n",
+    "\n",
+    "        # Construct a GIF from the frames.\n",
+    "        with io.BytesIO() as gif:\n",
+    "            imageio.mimsave(gif, current_frames, \"GIF\", fps=5)\n",
+    "            predicted_videos.append(gif.getvalue())\n",
+    "\n",
+    "# Display the videos.\n",
+    "print(\" Truth\\tPrediction\")\n",
+    "for i in range(0, len(predicted_videos), 2):\n",
+    "    # Construct and display an `HBox` with the ground truth and prediction.\n",
+    "    box = HBox(\n",
+    "        [\n",
+    "            widgets.Image(value=predicted_videos[i]),\n",
+    "            widgets.Image(value=predicted_videos[i + 1]),\n",
+    "        ]\n",
+    "    )\n",
+    "    display(box)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-02-13 21:42:41.774353: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
+     ]
     },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: saved_models/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save(\"saved_models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install gradio moviepy scikit-image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "\n",
+    "import imageio, cv2\n",
+    "from moviepy.editor import *\n",
+    "from skimage.transform import resize\n",
+    "from skimage import img_as_ubyte\n",
+    "\n",
+    "import gradio as gr\n",
+    "\n",
+    "def inference(driving,\n",
+    "              output_name = 'output.mp4',\n",
+    "              audio = True,\n",
+    "              cpu = False,\n",
+    "              best_frame = None,\n",
+    "              relative = True,\n",
+    "              adapt_scale = True,\n",
+    "              ):\n",
+    "\n",
+    "    # source \n",
+    "    source_image = resize(source, (256, 256))\n",
+    "    \n",
+    "    # driving\n",
+    "    reader = imageio.get_reader(driving)\n",
+    "    fps = reader.get_meta_data()['fps']\n",
+    "    driving_video = []\n",
+    "    try:\n",
+    "        for im in reader:\n",
+    "            driving_video.append(im)\n",
+    "    except RuntimeError:\n",
+    "        pass\n",
+    "    reader.close()\n",
+    "\n",
+    "    driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]\n",
+    "    \n",
+    "\n",
+    "    # save video\n",
+    "    output_path = 'asset/output'\n",
+    "    os.makedirs(output_path, exist_ok=True)\n",
+    "    \n",
+    "    print(f'{output_path}/{output_name}')    \n",
+    "    \n",
+    "    imageio.mimsave(f'{output_path}/{output_name}', [img_as_ubyte(frame) for frame in predictions], fps=fps)\n",
+    "    \n",
+    "    return f'{output_path}/{output_name}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import os\n",
+    "samples = []\n",
+    "\n",
+    "example_driving = os.listdir('asset/driving')\n",
+    "for video in example_driving:\n",
+    "    samples.append([None, f'asset/driving/{video}'])\n",
+    "\n",
+    "iface = gr.Interface(\n",
+    "    inference, # main function\n",
+    "    inputs = [ \n",
+    "        gr.inputs.Video(label='Driving Video', type='mp4'), # driving video\n",
+    "        \n",
+    "        # gr.inputs.Checkbox(label=\"fine best frame\", default=False), \n",
+    "        # gr.inputs.Checkbox(label=\"free view\", default=False), \n",
+    "        # gr.inputs.Slider(minimum=-90, maximum=90, default=0, step=1, label=\"yaw\"),\n",
+    "        # gr.inputs.Slider(minimum=-90, maximum=90, default=0, step=1, label=\"pitch\"),\n",
+    "        # gr.inputs.Slider(minimum=-90, maximum=90, default=0, step=1, label=\"raw\"),\n",
+    "        \n",
+    "    ],\n",
+    "    outputs = [\n",
+    "        gr.outputs.Video(label='result') # generated video\n",
+    "    ], \n",
+    "    \n",
+    "    title = 'Face Vid2Vid Demo',\n",
+    "    description = \"This app is an unofficial demo web app of the face video2video. The codes are heavily based on this repo, created by zhanglonghao1992\",\n",
+    "    \n",
+    "    # examples = samples,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861/\n"
+     ]
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.0"
+    {
+     "ename": "MissingSchema",
+     "evalue": "Invalid URL 'None': No schema supplied. Perhaps you meant http://None?",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMissingSchema\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_129/3616858783.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0miface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshare\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[0;32m~/.conda/envs/default/lib/python3.9/site-packages/gradio/interface.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self, inline, inbrowser, share, debug, auth, auth_message, private_endpoint, prevent_thread_lock, show_error, server_name, server_port, show_tips, enable_queue, height, width, encrypt, cache_examples, favicon_path)\u001b[0m\n\u001b[1;32m    728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mshare\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 730\u001b[0;31m                     \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnetworking\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl_ok\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshare_url\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    731\u001b[0m                         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\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[0m\n\u001b[1;32m    732\u001b[0m                     \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshare_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\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~/.conda/envs/default/lib/python3.9/site-packages/gradio/networking.py\u001b[0m in \u001b[0;36murl_ok\u001b[0;34m(url)\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m             \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\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    190\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m401\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m302\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# 401 or 302 if auth is set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.conda/envs/default/lib/python3.9/site-packages/requests/api.py\u001b[0m in \u001b[0;36mhead\u001b[0;34m(url, **kwargs)\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\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[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'head'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.conda/envs/default/lib/python3.9/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.conda/envs/default/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mhooks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         )\n\u001b[0;32m--> 528\u001b[0;31m         \u001b[0mprep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\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    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0mproxies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproxies\u001b[0m \u001b[0;32mor\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~/.conda/envs/default/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mprepare_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPreparedRequest\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--> 456\u001b[0;31m         p.prepare(\n\u001b[0m\u001b[1;32m    457\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper\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[1;32m    458\u001b[0m             \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.conda/envs/default/lib/python3.9/site-packages/requests/models.py\u001b[0m in \u001b[0;36mprepare\u001b[0;34m(self, method, url, headers, files, data, params, auth, cookies, hooks, json)\u001b[0m\n\u001b[1;32m    314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\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    317\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_headers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_cookies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcookies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.conda/envs/default/lib/python3.9/site-packages/requests/models.py\u001b[0m in \u001b[0;36mprepare_url\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_native_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'utf8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mMissingSchema\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\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    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhost\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mMissingSchema\u001b[0m: Invalid URL 'None': No schema supplied. Perhaps you meant http://None?"
+     ]
     }
+   ],
+   "source": [
+    "iface.launch(share=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "conv_lstm",
+   "provenance": [],
+   "toc_visible": true
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
+  "kernelspec": {
+   "display_name": "default:Python",
+   "language": "python",
+   "name": "conda-env-default-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "007991c798294b8e93cdf3fb62ee1e14": {
+      "buffers": [
+       {
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+        "encoding": "base64",
+        "path": [
+         "value"
+        ]
+       }
+      ],
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ImageModel",
+      "state": {
+       "layout": "IPY_MODEL_45a52d40e2d440c8a2b9ee596712f1ee"
+      }
+     },
+     "33d22b2374124877aae12c49f857e064": {
+      "buffers": [
+       {
+        "data": 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+        "encoding": "base64",
+        "path": [
+         "value"
+        ]
+       }
+      ],
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ImageModel",
+      "state": {
+       "layout": "IPY_MODEL_c2fa1c8ae73f43a997d4e140e047efd8"
+      }
+     },
+     "c4168bd2a10a40cbab5bb60fda28f804": {
+      "buffers": [
+       {
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+        "encoding": "base64",
+        "path": [
+         "value"
+        ]
+       }
+      ],
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ImageModel",
+      "state": {
+       "layout": "IPY_MODEL_6ca6263c46cb416d95a0e5c279f2a892"
+      }
+     },
+     "ccd504cbfb9a4e7f8020a3254b06b7b0": {
+      "buffers": [
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+        "encoding": "base64",
+        "path": [
+         "value"
+        ]
+       }
+      ],
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ImageModel",
+      "state": {
+       "layout": "IPY_MODEL_1524ea5364f2465393602221858a8344"
+      }
+     },
+     "d9628e5ed42449a18fbe450b4de56e53": {
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+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "eefac49d463e46d899902775c8c92aef": {
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+      "state": {}
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}