conv_lstm_heatmap_pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R-yvp8WLxTbL"
   },
   "outputs": [],
   "source": [
    "# Import libraries for ML.\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input, Reshape, MaxPooling2D, TimeDistributed, LeakyReLU, concatenate, Permute\n",
    "from keras.layers.convolutional import Conv2D, Conv3D\n",
    "from keras.layers.convolutional_recurrent import ConvLSTM2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.optimizers import SGD\n",
    "from keras.losses import mean_squared_error, logcosh\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import random\n",
    "from tensorflow.python.util import deprecation\n",
    "deprecation._PRINT_DEPRECATION_WARNINGS = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1Cv0h1qKMG3b",
    "outputId": "9055168a-17be-4c63-d736-239c98bed7f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "# Mount Google Drive to import dataset.\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive', force_remount=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nopQB30XCRqv"
   },
   "outputs": [],
   "source": [
    "# Set data constants for the tensor of dimension (N,T,S,S,K).\n",
    "N = 42      # Samples (Region, City, County, etc.) in Dataset\n",
    "T = 15      # Time Series Duration/Slices of Heatmaps\n",
    "S = 121     # Dimension/Size of Spatial Window of Heatmap\n",
    "K = 31      # Number of Features in the Dataset (i.e. Price, Size, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "colab_type": "code",
    "id": "BJt9JTexmyXG",
    "outputId": "050bd9cf-ef3c-4cd7-fc85-c920851fa226"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Input Shape:  (38, 15, 121, 121, 31)\n",
      "Training Output Shape:  (38, 121, 121)\n",
      "Validation Input Shape:  (3, 15, 121, 121, 31)\n",
      "Validation Output Shape:  (3, 121, 121)\n",
      "Test Input Shape:  (1, 15, 121, 121, 31)\n",
      "Test Output Shape:  (1, 121, 121)\n"
     ]
    }
   ],
   "source": [
    "# PART I - Import and construct machine learning dataset.\n",
    "path = \"/content/drive/My Drive/colab_data_proc\"\n",
    "# Import and collect machine learning data to NumPy arrays.\n",
    "data_input = np.zeros((N,T,S,S,K), dtype=np.single)\n",
    "data_output = np.zeros((N,S,S), dtype=np.single)\n",
    "i, j = 0, 0\n",
    "for file in sorted(os.listdir(path)):\n",
    "  print(file)\n",
    "  # Extract input and output data. Reduce data precision to save memory.\n",
    "  if file.endswith(\"_input.npy\"):\n",
    "    data_input[i] = np.load(path + '/' + file).astype(np.single)\n",
    "    i += 1\n",
    "  elif file.endswith(\"_output.npy\"):\n",
    "    # Dispose of remainder data.\n",
    "    data_output[j] = np.load(path + '/' + file).astype(np.single)[0]\n",
    "    j += 1\n",
    "\n",
    "# Separate training and test data via index mask to save memory via Python referencing.\n",
    "# Randomly choose 6 sample regions for validation/testing. Other samples are for training.\n",
    "rand_idx = random.sample(range(N), 6)\n",
    "train_idx = sorted(list(set(range(N)) - set(rand_idx)))\n",
    "val_idx = sorted(rand_idx[:4])\n",
    "test_idx = sorted(rand_idx[4:])\n",
    "\n",
    "# # Alternative: Fix Los Angeles County as test set for time-series display in publication.\n",
    "# rand_idx = random.sample(set(range(N)) - set([3]), 3)\n",
    "# train_idx = sorted(list(set(range(N)) - set(rand_idx + [3])))\n",
    "# val_idx = sorted(rand_idx)\n",
    "# test_idx = [3]\n",
    "# # Export test data time-series as .csv. Isolate property value for heatmap. Los Angeles has index 3 in dataset.\n",
    "# la = \"/content/drive/My Drive/pred_results/pred_heatmap_LA/\"\n",
    "# for ts in range(T):\n",
    "#   np.savetxt(la + \"la_heatmap_ts_{}.csv\".format(ts+1), data_input[3, ts, :, :, 0], delimiter=\",\")\n",
    "\n",
    "# Print training, validation, and test indices. (Compare with dataset for regions.)\n",
    "print(train_idx)\n",
    "print(val_idx)\n",
    "print(test_idx)\n",
    "\n",
    "# Extract data as .npy file to avoid import process and rename data. Print data dimensions.\n",
    "np.save(\"/content/drive/My Drive/colab_train_data/x_train_ts_heatmap\", data_input[train_idx])\n",
    "print(\"Training Input Shape: \", data_input[train_idx].shape)\n",
    "np.save(\"/content/drive/My Drive/colab_train_data/y_train_ts_heatmap\", data_output[train_idx])\n",
    "print(\"Training Output Shape: \", data_output[train_idx].shape)\n",
    "np.save(\"/content/drive/My Drive/colab_train_data/x_val_ts_heatmap\", data_input[val_idx])\n",
    "print(\"Validation Input Shape: \", data_input[val_idx].shape)\n",
    "np.save(\"/content/drive/My Drive/colab_train_data/y_val_ts_heatmap\", data_output[val_idx])\n",
    "print(\"Validation Output Shape: \", data_output[val_idx].shape)\n",
    "np.save(\"/content/drive/My Drive/colab_train_data/x_test_ts_heatmap\", data_input[test_idx])\n",
    "print(\"Test Input Shape: \", data_input[test_idx].shape)\n",
    "np.save(\"/content/drive/My Drive/colab_train_data/y_test_ts_heatmap\", data_output[test_idx])\n",
    "print(\"Test Output Shape: \", data_output[test_idx].shape)\n",
    "\n",
    "# ------------------------------------------------------ #\n",
    "\n",
    "# PART II - Import previously saved data. \n",
    "# x_ has input dimension (:,T,S,S,K) and y_ has output dimension (:,S,S).\n",
    "path = \"/content/drive/My Drive/colab_train_data\"\n",
    "x_train = np.load(path + '/' + \"x_train_ts_heatmap.npy\")\n",
    "y_train = np.load(path + '/' + \"y_train_ts_heatmap.npy\")\n",
    "x_val = np.load(path + '/' + \"x_val_ts_heatmap.npy\")\n",
    "y_val = np.load(path + '/' + \"y_val_ts_heatmap.npy\")\n",
    "x_test = np.load(path + '/' + \"x_test_ts_heatmap.npy\")\n",
    "y_test = np.load(path + '/' + \"y_test_ts_heatmap.npy\")\n",
    "\n",
    "# Logarithmic and shift normalization due to incredibly diverse variance of (non-negative) data.\n",
    "eps = 1e-3\n",
    "x_train[:, :, :, :, [0,4,5,6,7,8,9]] = np.log(x_train[:, :, :, :, [0,4,5,6,7,8,9]] + eps + 1)\n",
    "x_train[:, :, :, :, 2] -= 2000\n",
    "y_train = np.log(y_train + eps + 1)\n",
    "x_val[:, :, :, :, [0,4,5,6,7,8,9]] = np.log(x_val[:, :, :, :, [0,4,5,6,7,8,9]] + eps + 1)\n",
    "x_val[:, :, :, :, 2] -= 2000\n",
    "y_val = np.log(y_val + eps + 1)\n",
    "x_test[:, :, :, :, [0,4,5,6,7,8,9]] = np.log(x_test[:, :, :, :, [0,4,5,6,7,8,9]] + eps + 1)\n",
    "x_test[:, :, :, :, 2] -= 2000\n",
    "\n",
    "# [DEBUG]\n",
    "# print(np.unravel_index(np.argmax(x_train, axis=None), x_train.shape))\n",
    "# print(np.max(x_train))\n",
    "# print(x_train[0, 0, 7, 0, :])\n",
    "\n",
    "# Print training and test data dimensions.\n",
    "print(\"Training Input Shape: \", x_train.shape)\n",
    "print(\"Training Output Shape: \", y_train.shape)\n",
    "print(\"Validation Input Shape: \", x_val.shape)\n",
    "print(\"Validation Output Shape: \", y_val.shape)\n",
    "print(\"Test Input Shape: \", x_test.shape)\n",
    "print(\"Test Output Shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "barzCaSomrzN"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "[Elementary Convolutional Inception Module]\n",
    "Apply supporting convolutional inception filters to learn spatial features \n",
    "from the weighted heatmap prior to application of LSTM.\n",
    "Utilized to construct an inception-convolutional LSTM.\n",
    "\"\"\"\n",
    "# Initialize and reshape input. Assume feature maps are time-invariant. \n",
    "input = Input(shape=(S, S, K))\n",
    "\n",
    "# Lightweight Convolutional Inception Module (5-Path Filter) for Feature Mixing\n",
    "t1 = Conv2D(32, (1, 1), padding='same', data_format='channels_last')(input)\n",
    "t2 = Conv2D(16, (3, 3), padding='same', data_format='channels_last')(input)\n",
    "t3 = Conv2D(8, (7, 7), padding='same', data_format='channels_last')(input)\n",
    "t4 = Conv2D(4, (11, 11), padding='same', data_format='channels_last')(input)\n",
    "t5 = MaxPooling2D(pool_size=(3,3), strides=(1,1), \n",
    "                  padding='same', data_format='channels_last')(input)\n",
    "\n",
    "# Compute output.\n",
    "output = concatenate([t1, t2, t3, t4, t5])\n",
    "\n",
    "# Construct layer/model.\n",
    "conv_incptn = Model(inputs=input, outputs=output)\n",
    "\"\"\"\n",
    "[Multi-Layer Inception-Convolutional LSTM Architecture for Housing Index Forecasting]\n",
    "Apply space-time convolutional LSTM layers via ConvLSTM2D.\n",
    "Input (N,T,S,S,K) -> [Incptn-Conv -> Conv-LSTM -> Batch Normalization] x 4\n",
    "                  -> Conv2D -> LeakyReLU -> Output (S,S) is a 2-D heatmap.\n",
    "(Derivation Source: https://arxiv.org/pdf/1909.05622.pdf)\n",
    "\"\"\"\n",
    "# Intialize a sequential model.\n",
    "incptn_conv_lstm = Sequential()\n",
    "\n",
    "# Insert a series of Conv-LSTM and Batch Normalization Layers.\n",
    "incptn_conv_lstm.add(TimeDistributed(conv_incptn, input_shape=(T, S, S, K)))\n",
    "incptn_conv_lstm.add(ConvLSTM2D(filters=K, kernel_size=(3, 3),\n",
    "                          padding='same', data_format='channels_last', \n",
    "                          return_sequences=True))\n",
    "incptn_conv_lstm.add(TimeDistributed(BatchNormalization()))\n",
    "incptn_conv_lstm.add(TimeDistributed(conv_incptn))\n",
    "incptn_conv_lstm.add(ConvLSTM2D(filters=K, kernel_size=(3, 3),\n",
    "                          padding='same', data_format='channels_last', \n",
    "                          return_sequences=True))\n",
    "incptn_conv_lstm.add(TimeDistributed(BatchNormalization()))\n",
    "incptn_conv_lstm.add(TimeDistributed(conv_incptn))\n",
    "incptn_conv_lstm.add(ConvLSTM2D(filters=K, kernel_size=(3, 3),\n",
    "                          padding='same', data_format='channels_last', \n",
    "                          return_sequences=True))\n",
    "incptn_conv_lstm.add(TimeDistributed(BatchNormalization()))\n",
    "incptn_conv_lstm.add(TimeDistributed(conv_incptn))\n",
    "incptn_conv_lstm.add(ConvLSTM2D(filters=K, kernel_size=(3, 3),\n",
    "                          padding='same', data_format='channels_last', \n",
    "                          return_sequences=False))\n",
    "incptn_conv_lstm.add(TimeDistributed(BatchNormalization()))\n",
    "\n",
    "# Conv2D layer to collapse filters.\n",
    "incptn_conv_lstm.add(Conv2D(filters=1, kernel_size=(3, 3), padding='same', data_format='channels_last'))\n",
    "\n",
    "# Reshape to heatmap.\n",
    "incptn_conv_lstm.add(Reshape((S, S)))\n",
    "\n",
    "# Weak LeakyReLU to avoid neuron death and approximate ReLU.\n",
    "incptn_conv_lstm.add(LeakyReLU(alpha=0.1))\n",
    "\n",
    "# Compile the model. Nesterov SGD with thresholded gradient and MSE Loss.\n",
    "incptn_conv_lstm.compile(optimizer=SGD(lr=2e-3, momentum=0.2, nesterov=True, clipnorm=0.25), loss=mean_squared_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "colab_type": "code",
    "id": "2ZQOxg2ipg-g",
    "outputId": "cea5d174-3fad-4abd-e796-3ca8d6b97ce9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "time_distributed_9 (TimeDist (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_5 (ConvLSTM2D)  (None, 15, 121, 121, 31)  136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_10 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_11 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_6 (ConvLSTM2D)  (None, 15, 121, 121, 31)  136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_12 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_13 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_7 (ConvLSTM2D)  (None, 15, 121, 121, 31)  136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_14 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_15 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_8 (ConvLSTM2D)  (None, 121, 121, 31)      136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_16 (TimeDis (None, 121, 121, 31)      124       \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 121, 121, 1)       280       \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 121, 121)          0         \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 121, 121)          0         \n",
      "=================================================================\n",
      "Total params: 578,552\n",
      "Trainable params: 578,304\n",
      "Non-trainable params: 248\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "incptn_conv_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "STuHLIre9bOA"
   },
   "outputs": [],
   "source": [
    "# Set model checkpoint to save the trained model.\n",
    "cp_path = \"/content/drive/My Drive/trained_models/incptn_conv_lstm\"\n",
    "cp_callback = ModelCheckpoint(filepath=cp_path, monitor='val_loss', save_weights_only=False, save_best_only=True, mode='min', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iFs4QTI2AEZM"
   },
   "outputs": [],
   "source": [
    "# Load pre-trained model. (Overwrites un-trained model!)\n",
    "incptn_conv_lstm = load_model(cp_path)\n",
    "incptn_conv_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "dXYxHHx3yXMu",
    "outputId": "9ab90801-3050-414d-9f27-afaca28f483e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 38 samples, validate on 3 samples\n",
      "Epoch 1/30\n",
      "38/38 [==============================] - 305s 8s/step - loss: 7.8075 - val_loss: 8.9248\n",
      "Epoch 2/30\n",
      "38/38 [==============================] - 259s 7s/step - loss: 7.7507 - val_loss: 5.1664\n",
      "Epoch 3/30\n",
      "38/38 [==============================] - 251s 7s/step - loss: 7.6286 - val_loss: 5.1781\n",
      "Epoch 4/30\n",
      "38/38 [==============================] - 250s 7s/step - loss: 7.5886 - val_loss: 5.1990\n",
      "Epoch 5/30\n",
      "38/38 [==============================] - 273s 7s/step - loss: 7.5531 - val_loss: 5.6312\n",
      "Epoch 6/30\n",
      "38/38 [==============================] - 275s 7s/step - loss: 7.5280 - val_loss: 5.2536\n",
      "Epoch 7/30\n",
      "38/38 [==============================] - 262s 7s/step - loss: 7.6215 - val_loss: 5.1172\n",
      "Epoch 8/30\n",
      "38/38 [==============================] - 280s 7s/step - loss: 7.4558 - val_loss: 5.0562\n",
      "Epoch 9/30\n",
      "38/38 [==============================] - 297s 8s/step - loss: 7.4393 - val_loss: 5.0163\n",
      "Epoch 10/30\n",
      "38/38 [==============================] - 263s 7s/step - loss: 7.4035 - val_loss: 5.0017\n",
      "Epoch 11/30\n",
      "38/38 [==============================] - 254s 7s/step - loss: 7.3588 - val_loss: 4.9172\n",
      "Epoch 12/30\n",
      "38/38 [==============================] - 249s 7s/step - loss: 7.3627 - val_loss: 4.9602\n",
      "Epoch 13/30\n",
      "38/38 [==============================] - 272s 7s/step - loss: 7.3349 - val_loss: 4.8496\n",
      "Epoch 14/30\n",
      "38/38 [==============================] - 274s 7s/step - loss: 7.2891 - val_loss: 4.8124\n",
      "Epoch 15/30\n",
      "38/38 [==============================] - 260s 7s/step - loss: 7.2593 - val_loss: 4.7946\n",
      "Epoch 16/30\n",
      "38/38 [==============================] - 258s 7s/step - loss: 7.2379 - val_loss: 4.8263\n",
      "Epoch 17/30\n",
      "38/38 [==============================] - 283s 7s/step - loss: 7.2643 - val_loss: 4.8322\n",
      "Epoch 18/30\n",
      "38/38 [==============================] - 257s 7s/step - loss: 7.2543 - val_loss: 4.8214\n",
      "Epoch 19/30\n",
      "38/38 [==============================] - 247s 7s/step - loss: 7.1876 - val_loss: 4.7636\n",
      "Epoch 20/30\n",
      "38/38 [==============================] - 251s 7s/step - loss: 7.2625 - val_loss: 4.7533\n",
      "Epoch 21/30\n",
      "38/38 [==============================] - 269s 7s/step - loss: 7.1688 - val_loss: 4.7348\n",
      "Epoch 22/30\n",
      "38/38 [==============================] - 288s 8s/step - loss: 7.1863 - val_loss: 4.6953\n",
      "Epoch 23/30\n",
      "38/38 [==============================] - 276s 7s/step - loss: 7.1376 - val_loss: 4.7109\n",
      "Epoch 24/30\n",
      "38/38 [==============================] - 277s 7s/step - loss: 7.1422 - val_loss: 4.6939\n",
      "Epoch 25/30\n",
      "38/38 [==============================] - 298s 8s/step - loss: 7.1578 - val_loss: 4.7537\n",
      "Epoch 26/30\n",
      "38/38 [==============================] - 273s 7s/step - loss: 7.1098 - val_loss: 5.3594\n",
      "Epoch 27/30\n",
      "38/38 [==============================] - 253s 7s/step - loss: 7.3998 - val_loss: 4.6568\n",
      "Epoch 28/30\n",
      "38/38 [==============================] - 256s 7s/step - loss: 7.1243 - val_loss: 4.8703\n",
      "Epoch 29/30\n",
      "38/38 [==============================] - 272s 7s/step - loss: 7.1579 - val_loss: 4.6919\n",
      "Epoch 30/30\n",
      "38/38 [==============================] - 277s 7s/step - loss: 7.0510 - val_loss: 4.6464\n"
     ]
    }
   ],
   "source": [
    "# Train the Hybrid Inceptive-Conv-LSTM Model on the Time-Series Heatmap Data.\n",
    "num_epoch = 30\n",
    "model_stats = incptn_conv_lstm.fit(x=x_train, y=y_train, batch_size=int(N/4), epochs=num_epoch, validation_data=(x_val, y_val), callbacks=[cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IHvZKYMTKHU0"
   },
   "outputs": [],
   "source": [
    "# Part I - Cache loss data. (Initialize loss cache to track model training.)\n",
    "train_loss = model_stats.history['loss']\n",
    "val_loss = model_stats.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NGvk6lArEn-c"
   },
   "outputs": [],
   "source": [
    "# Part II - Append loss data to cache. (To collect loss data through multiple training sessions.)\n",
    "train_loss = train_loss + model_stats.history['loss']\n",
    "val_loss = val_loss + model_stats.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MeSI75LQGBB-"
   },
   "outputs": [],
   "source": [
    "# Part III - Export loss data for analysis or preservation.\n",
    "np.save(\"/content/drive/My Drive/pred_results/train_loss\", train_loss)\n",
    "np.save(\"/content/drive/My Drive/pred_results/val_loss\", val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BAi-QtZCBm5m"
   },
   "outputs": [],
   "source": [
    "# Part IV - Load loss statistics.\n",
    "train_loss = np.load(\"/content/drive/My Drive/pred_results/train_loss\")\n",
    "val_loss = np.load(\"/content/drive/My Drive/pred_results/val_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 747
    },
    "colab_type": "code",
    "id": "3f_D3Qx73SOr",
    "outputId": "e915a8aa-55f4-4eeb-c3bb-7d19996fdb6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean-Squared Error Validation Loss')"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27djDLyKmJ4rGMQO7ADBpTkGsQhMREalX0SRhc4DoiySkyTAzTj6gGxOvOZwD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\nKg40CFNKKaWUigMNwpRSSiml4uD/AJwvWIKcRCS6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Output loss statistics. Plot training and validation loss over epochs.\n",
    "plt.rcParams['figure.figsize'] = (10.0, 12.0) # set default size of plots\n",
    "plt.subplots_adjust(hspace = 0.3)\n",
    "# Training Loss\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(range(len(train_loss)), train_loss)\n",
    "plt.title('Inception-Convolutional LSTM w/ Thresholded Nesterov SGD')\n",
    "plt.xlabel('Training Epochs')\n",
    "plt.ylabel('Mean-Squared Error Training Loss')\n",
    "# Validation Loss\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(range(len(val_loss)), val_loss)\n",
    "plt.title('Inception-Convolutional LSTM w/ Thresholded Nesterov SGD')\n",
    "plt.xlabel('Training Epochs')\n",
    "plt.ylabel('Mean-Squared Error Validation Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sanyAHtJ_tJP"
   },
   "outputs": [],
   "source": [
    "# Save model manually. (Deletes the optimal model! Sometimes desirable if the model is close to prior optimal, but needs more training without overwriting descent progress.)\n",
    "incptn_conv_lstm.save(cp_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "colab_type": "code",
    "id": "o7pvzzmy4t-B",
    "outputId": "f1169a72-022f-4bcb-e347-9183e6af8c6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "time_distributed_33 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_17 (ConvLSTM2D) (None, 15, 121, 121, 31)  136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_34 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_35 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_18 (ConvLSTM2D) (None, 15, 121, 121, 31)  136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_36 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_37 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_19 (ConvLSTM2D) (None, 15, 121, 121, 31)  136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_38 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_39 (TimeDis (None, 15, 121, 121, 91)  32672     \n",
      "_________________________________________________________________\n",
      "conv_lst_m2d_20 (ConvLSTM2D) (None, 121, 121, 31)      136276    \n",
      "_________________________________________________________________\n",
      "time_distributed_40 (TimeDis (None, 121, 121, 31)      124       \n",
      "_________________________________________________________________\n",
      "conv2d_25 (Conv2D)           (None, 121, 121, 1)       280       \n",
      "_________________________________________________________________\n",
      "reshape_5 (Reshape)          (None, 121, 121)          0         \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 121, 121)          0         \n",
      "=================================================================\n",
      "Total params: 676,568\n",
      "Trainable params: 676,320\n",
      "Non-trainable params: 248\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Load optimal pre-trained model.\n",
    "incptn_conv_lstm_opt = load_model(cp_path)\n",
    "incptn_conv_lstm_opt.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "IeW1HPSHr2W5",
    "outputId": "a0697808-0cd6-4e20-a106-618fe5f19e32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - 3s 3s/step\n",
      "Average Test Loss:  9.647385597229004\n"
     ]
    }
   ],
   "source": [
    "# Test the trained model. Print test loss for all test sets.\n",
    "pred = incptn_conv_lstm_opt.predict(x_test)\n",
    "print(\"Average Test Loss: \", incptn_conv_lstm_opt.evaluate(x_test, np.log(y_test + eps + 1)))\n",
    "\n",
    "# Rectify and un-normalize the output data.\n",
    "pred = np.exp(pred) - eps - 1\n",
    "pred[pred < eps] = 0\n",
    "\n",
    "# Export the heatmaps.\n",
    "path = \"/content/drive/My Drive/pred_results\"\n",
    "for ts in range(len(y_test)):\n",
    "  np.savetxt(path + '/' + \"pred_heatmap_{}.csv\".format(ts), pred[ts], delimiter=\",\")\n",
    "  np.savetxt(path + '/' + \"test_heatmap_{}.csv\".format(ts), y_test[ts], delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zbaZ6TSzYk_9"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "[Deep / Wide Convolutional Inception Module]\n",
    "Apply supporting convolutional inception filters to learn spatial features \n",
    "from the weighted heatmap. Derivation of GoogleNet Inception Module.\n",
    "\"\"\"\n",
    "# Initialize and reshape input. Assume feature maps are time-invariant. \n",
    "input = Input(shape=(S, S, K))\n",
    "\n",
    "# Lightweight Convolutional Inception Module (5-Path Filter) for Feature Mixing\n",
    "t1 = Conv2D(64, (1, 1), padding='same', data_format='channels_last')(input)\n",
    "\n",
    "t2 = Conv2D(32, (1, 1), padding='same', data_format='channels_last')(input)\n",
    "t2 = Conv2D(32, (3, 3), padding='same', data_format='channels_last')(t2)\n",
    "\n",
    "t3 = Conv2D(32, (1, 1), padding='same', data_format='channels_last')(input)\n",
    "t3 = Conv2D(16, (5, 5), padding='same', data_format='channels_last')(t3)\n",
    "\n",
    "t4 = Conv2D(32, (1, 1), padding='same', data_format='channels_last')(input)\n",
    "t4 = Conv2D(8, (11, 11), padding='same', data_format='channels_last')(t4)\n",
    "\n",
    "t5 = Conv2D(16, (1, 1), padding='same', data_format='channels_last')(input)\n",
    "t5 = Conv2D(2, (47, 47), padding='same', data_format='channels_last')(t5)\n",
    "\n",
    "t6 = MaxPooling2D(pool_size=(3,3), strides=(1,1), \n",
    "                  padding='same', data_format='channels_last')(input)\n",
    "t6 = Conv2D(8, (1, 1), padding='same', data_format='channels_last')(t6)\n",
    "\n",
    "# Compute output.\n",
    "output = concatenate([t1, t2, t3, t4, t5, t6])\n",
    "\n",
    "# Construct layer/model.\n",
    "deep_conv_incptn = Model(inputs=input, outputs=output)\n",
    "\n",
    "\"\"\"\n",
    "[Deep Spatio-Temporal Convolutional Neural Net for Imitation Learning]\n",
    "Reinforcement/Imitation Learning with time-distributed inception CNN to compare with the inception-convolutional LSTM.\n",
    "\n",
    "\"\"\"\n",
    "# Initialize Sequential().\n",
    "td_cnn = Sequential()\n",
    "\n",
    "# Insert a deep, time-distributed Inception-CNN.\n",
    "td_cnn.add(TimeDistributed(deep_conv_incptn, input_shape=(T, S, S, K)))\n",
    "td_cnn.add(TimeDistributed(BatchNormalization()))\n",
    "\n",
    "# Mix and compress features to 1.\n",
    "td_cnn.add(TimeDistributed(Conv2D(filters=K, kernel_size=(3, 3), padding='same', data_format='channels_last')))\n",
    "td_cnn.add(TimeDistributed(BatchNormalization()))\n",
    "td_cnn.add(TimeDistributed(Conv2D(filters=K, kernel_size=(3, 3), padding='same', data_format='channels_last')))\n",
    "td_cnn.add(TimeDistributed(BatchNormalization()))\n",
    "\n",
    "# Apply Conv3D layer(s) to mix spatio-temporal heatmap time-slices for Transitional Imitation Learning.\n",
    "# Layers sufficient for temporal receptive field > T.\n",
    "td_cnn.add(Conv3D(filters=T, kernel_size=(3, 3, 3), padding='same', data_format='channels_last'))\n",
    "td_cnn.add(TimeDistributed(BatchNormalization()))\n",
    "td_cnn.add(Conv3D(filters=5, kernel_size=(3, 3, 3), padding='same', data_format='channels_last'))\n",
    "td_cnn.add(TimeDistributed(BatchNormalization()))\n",
    "td_cnn.add(Conv3D(filters=1, kernel_size=(3, 3, 3), padding='same', data_format='channels_last'))\n",
    "td_cnn.add(TimeDistributed(BatchNormalization()))\n",
    "td_cnn.add(Reshape((T, S, S)))\n",
    "\n",
    "# Collapse time-dimension. Re-shape to heatmap.\n",
    "td_cnn.add(Permute((2, 3, 1)))\n",
    "td_cnn.add(Conv2D(filters=1, kernel_size=(3, 3), padding='same', data_format='channels_last'))\n",
    "td_cnn.add(Reshape((S, S)))\n",
    "\n",
    "# Weak LeakyReLU to avoid neuron death and approximate ReLU.\n",
    "td_cnn.add(LeakyReLU(alpha=0.1))\n",
    "\n",
    "# Compile the model. Nesterov SGD with thresholded gradient and MSE Loss.\n",
    "td_cnn.compile(optimizer=SGD(lr=1e-3, momentum=0.2, nesterov=True), loss=mean_squared_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 722
    },
    "colab_type": "code",
    "id": "ToqfBdOT4GVs",
    "outputId": "20462118-ff78-4fe5-f2b7-778f1e67252c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "time_distributed_41 (TimeDis (None, 15, 121, 121, 130) 129626    \n",
      "_________________________________________________________________\n",
      "time_distributed_42 (TimeDis (None, 15, 121, 121, 130) 520       \n",
      "_________________________________________________________________\n",
      "time_distributed_43 (TimeDis (None, 15, 121, 121, 31)  36301     \n",
      "_________________________________________________________________\n",
      "time_distributed_44 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_45 (TimeDis (None, 15, 121, 121, 1)   280       \n",
      "_________________________________________________________________\n",
      "time_distributed_46 (TimeDis (None, 15, 121, 121, 1)   4         \n",
      "_________________________________________________________________\n",
      "conv3d_1 (Conv3D)            (None, 15, 121, 121, 15)  420       \n",
      "_________________________________________________________________\n",
      "time_distributed_47 (TimeDis (None, 15, 121, 121, 15)  60        \n",
      "_________________________________________________________________\n",
      "conv3d_2 (Conv3D)            (None, 15, 121, 121, 5)   2030      \n",
      "_________________________________________________________________\n",
      "time_distributed_48 (TimeDis (None, 15, 121, 121, 5)   20        \n",
      "_________________________________________________________________\n",
      "conv3d_3 (Conv3D)            (None, 15, 121, 121, 1)   136       \n",
      "_________________________________________________________________\n",
      "time_distributed_49 (TimeDis (None, 15, 121, 121, 1)   4         \n",
      "_________________________________________________________________\n",
      "reshape_6 (Reshape)          (None, 15, 121, 121)      0         \n",
      "_________________________________________________________________\n",
      "permute_1 (Permute)          (None, 121, 121, 15)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_38 (Conv2D)           (None, 121, 121, 1)       136       \n",
      "_________________________________________________________________\n",
      "reshape_7 (Reshape)          (None, 121, 121)          0         \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 121, 121)          0         \n",
      "=================================================================\n",
      "Total params: 169,661\n",
      "Trainable params: 169,295\n",
      "Non-trainable params: 366\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "td_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XN_4r0xuqBD2"
   },
   "outputs": [],
   "source": [
    "# Set model checkpoint to save the trained model.\n",
    "cp_path_alt = \"/content/drive/My Drive/trained_models/incptn_conv_td\"\n",
    "cp_callback_alt = ModelCheckpoint(filepath=cp_path_alt, monitor='val_loss', save_weights_only=False, save_best_only=True, mode='min', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-T_6Pk5PqW2W"
   },
   "outputs": [],
   "source": [
    "# Load pre-trained model. (Overwrites un-trained model!)\n",
    "td_cnn = load_model(cp_path_alt)\n",
    "td_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "CsflnNZ9qhf5",
    "outputId": "3fa94e8f-1fe0-49a4-d709-25b58fee4c57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 38 samples, validate on 3 samples\n",
      "Epoch 1/30\n",
      "38/38 [==============================] - 362s 10s/step - loss: 7.3063 - val_loss: 4.3624\n",
      "Epoch 2/30\n",
      "38/38 [==============================] - 339s 9s/step - loss: 6.7507 - val_loss: 3.9257\n",
      "Epoch 3/30\n",
      "38/38 [==============================] - 338s 9s/step - loss: 6.4083 - val_loss: 3.7944\n",
      "Epoch 4/30\n",
      "38/38 [==============================] - 344s 9s/step - loss: 6.3291 - val_loss: 3.6859\n",
      "Epoch 5/30\n",
      "38/38 [==============================] - 344s 9s/step - loss: 6.2892 - val_loss: 3.6456\n",
      "Epoch 6/30\n",
      "38/38 [==============================] - 340s 9s/step - loss: 6.1995 - val_loss: 3.6152\n",
      "Epoch 7/30\n",
      "38/38 [==============================] - 346s 9s/step - loss: 6.2348 - val_loss: 3.5714\n",
      "Epoch 8/30\n",
      "38/38 [==============================] - 338s 9s/step - loss: 6.1698 - val_loss: 3.5783\n",
      "Epoch 9/30\n",
      "38/38 [==============================] - 340s 9s/step - loss: 6.2084 - val_loss: 3.5259\n",
      "Epoch 10/30\n",
      "38/38 [==============================] - 345s 9s/step - loss: 6.1680 - val_loss: 3.5375\n",
      "Epoch 11/30\n",
      "38/38 [==============================] - 347s 9s/step - loss: 6.1110 - val_loss: 3.5334\n",
      "Epoch 12/30\n",
      "38/38 [==============================] - 353s 9s/step - loss: 6.0877 - val_loss: 3.5610\n",
      "Epoch 13/30\n",
      "38/38 [==============================] - 341s 9s/step - loss: 6.0811 - val_loss: 3.6272\n",
      "Epoch 14/30\n",
      "38/38 [==============================] - 340s 9s/step - loss: 6.0636 - val_loss: 3.5966\n",
      "Epoch 15/30\n",
      "38/38 [==============================] - 343s 9s/step - loss: 6.1046 - val_loss: 3.6166\n",
      "Epoch 16/30\n",
      "38/38 [==============================] - 345s 9s/step - loss: 6.0570 - val_loss: 3.6110\n",
      "Epoch 17/30\n",
      "38/38 [==============================] - 338s 9s/step - loss: 6.0139 - val_loss: 3.6666\n",
      "Epoch 18/30\n",
      "38/38 [==============================] - 350s 9s/step - loss: 6.1185 - val_loss: 3.5649\n",
      "Epoch 19/30\n",
      "38/38 [==============================] - 340s 9s/step - loss: 6.0308 - val_loss: 3.6601\n",
      "Epoch 20/30\n",
      "38/38 [==============================] - 343s 9s/step - loss: 6.0366 - val_loss: 3.6186\n",
      "Epoch 21/30\n",
      "38/38 [==============================] - 346s 9s/step - loss: 6.0049 - val_loss: 3.5895\n",
      "Epoch 22/30\n",
      "38/38 [==============================] - 341s 9s/step - loss: 6.0045 - val_loss: 3.6234\n",
      "Epoch 23/30\n",
      "38/38 [==============================] - 349s 9s/step - loss: 5.9845 - val_loss: 3.5009\n",
      "Epoch 24/30\n",
      "38/38 [==============================] - 340s 9s/step - loss: 5.9406 - val_loss: 3.4846\n",
      "Epoch 25/30\n",
      "38/38 [==============================] - 337s 9s/step - loss: 5.9598 - val_loss: 3.4666\n",
      "Epoch 26/30\n",
      "38/38 [==============================] - 342s 9s/step - loss: 5.9953 - val_loss: 3.4999\n",
      "Epoch 27/30\n",
      "38/38 [==============================] - 346s 9s/step - loss: 5.9912 - val_loss: 3.4721\n",
      "Epoch 28/30\n",
      "38/38 [==============================] - 341s 9s/step - loss: 5.9816 - val_loss: 3.4043\n",
      "Epoch 29/30\n",
      "38/38 [==============================] - 349s 9s/step - loss: 5.9630 - val_loss: 3.4341\n",
      "Epoch 30/30\n",
      "38/38 [==============================] - 339s 9s/step - loss: 5.9674 - val_loss: 3.4227\n"
     ]
    }
   ],
   "source": [
    "# Train the Hybrid Inceptive-Conv-LSTM Model on the Time-Series Heatmap Data.\n",
    "num_epoch_alt = 30\n",
    "model_stats_alt = td_cnn.fit(x=x_train, y=y_train, batch_size=int(N/4), epochs=num_epoch_alt, validation_data=(x_val, y_val), callbacks=[cp_callback_alt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O3XBaA-eqwq2"
   },
   "outputs": [],
   "source": [
    "# Part I - Cache loss data. (Initialize loss cache to track model training.)\n",
    "train_loss_alt = model_stats_alt.history['loss']\n",
    "val_loss_alt = model_stats_alt.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gp__v-_Xq3XY"
   },
   "outputs": [],
   "source": [
    "# Part II - Append loss data to cache. (To collect loss data through multiple training sessions.)\n",
    "train_loss_alt = train_loss_alt + model_stats_alt.history['loss']\n",
    "val_loss_alt = val_loss_alt + model_stats_alt.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dluLVdtUq_AT"
   },
   "outputs": [],
   "source": [
    "# Part III - Export loss data for analysis or preservation.\n",
    "np.save(\"/content/drive/My Drive/pred_results/train_loss_alt\", train_loss_alt)\n",
    "np.save(\"/content/drive/My Drive/pred_results/val_loss_alt\", val_loss_alt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6r_ynzifBYK3"
   },
   "outputs": [],
   "source": [
    "# Part IV - Load loss statistics.\n",
    "train_loss_alt = np.load(\"/content/drive/My Drive/pred_results/train_loss_alt.npy\")\n",
    "val_loss_alt = np.load(\"/content/drive/My Drive/pred_results/val_loss_alt.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 747
    },
    "colab_type": "code",
    "id": "iqswW7AHrFo2",
    "outputId": "3791b4e6-7592-4a85-f3fc-ec1de99ab1de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean-Squared Error Validation Loss')"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NtoSZ2YcB3H0zkODum+seBDfxbk4ScBxws7sfC5QC329gn2nAmQTJ3o/MbEID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\nIiHw33pJWga/yglgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Output loss statistics. Plot training and validation loss over epochs.\n",
    "plt.rcParams['figure.figsize'] = (10.0, 12.0) # set default size of plots\n",
    "plt.subplots_adjust(hspace = 0.3)\n",
    "# Training Loss\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(range(len(train_loss_alt)), train_loss_alt)\n",
    "plt.title('Inception-Convolutional Neural Net w/ Nesterov SGD')\n",
    "plt.xlabel('Training Epochs')\n",
    "plt.ylabel('Mean-Squared Error Training Loss')\n",
    "# Validation Loss\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(range(len(val_loss_alt)), val_loss_alt)\n",
    "plt.title('Inception-Convolutional Neural Net w/ Nesterov SGD')\n",
    "plt.xlabel('Training Epochs')\n",
    "plt.ylabel('Mean-Squared Error Validation Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PAcK6utarYE4"
   },
   "outputs": [],
   "source": [
    "# Save model manually. (Deletes the optimal model! Sometimes desirable if the model is close to prior optimal, but needs more training without overwriting descent progress.)\n",
    "td_cnn.save(cp_path_alt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 722
    },
    "colab_type": "code",
    "id": "5aKc9oq1radB",
    "outputId": "e804d3e5-95f4-4887-b313-ea979501ec57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "time_distributed_41 (TimeDis (None, 15, 121, 121, 130) 129626    \n",
      "_________________________________________________________________\n",
      "time_distributed_42 (TimeDis (None, 15, 121, 121, 130) 520       \n",
      "_________________________________________________________________\n",
      "time_distributed_43 (TimeDis (None, 15, 121, 121, 31)  36301     \n",
      "_________________________________________________________________\n",
      "time_distributed_44 (TimeDis (None, 15, 121, 121, 31)  124       \n",
      "_________________________________________________________________\n",
      "time_distributed_45 (TimeDis (None, 15, 121, 121, 1)   280       \n",
      "_________________________________________________________________\n",
      "time_distributed_46 (TimeDis (None, 15, 121, 121, 1)   4         \n",
      "_________________________________________________________________\n",
      "conv3d_1 (Conv3D)            (None, 15, 121, 121, 15)  420       \n",
      "_________________________________________________________________\n",
      "time_distributed_47 (TimeDis (None, 15, 121, 121, 15)  60        \n",
      "_________________________________________________________________\n",
      "conv3d_2 (Conv3D)            (None, 15, 121, 121, 5)   2030      \n",
      "_________________________________________________________________\n",
      "time_distributed_48 (TimeDis (None, 15, 121, 121, 5)   20        \n",
      "_________________________________________________________________\n",
      "conv3d_3 (Conv3D)            (None, 15, 121, 121, 1)   136       \n",
      "_________________________________________________________________\n",
      "time_distributed_49 (TimeDis (None, 15, 121, 121, 1)   4         \n",
      "_________________________________________________________________\n",
      "reshape_6 (Reshape)          (None, 15, 121, 121)      0         \n",
      "_________________________________________________________________\n",
      "permute_1 (Permute)          (None, 121, 121, 15)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_38 (Conv2D)           (None, 121, 121, 1)       136       \n",
      "_________________________________________________________________\n",
      "reshape_7 (Reshape)          (None, 121, 121)          0         \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 121, 121)          0         \n",
      "=================================================================\n",
      "Total params: 169,661\n",
      "Trainable params: 169,295\n",
      "Non-trainable params: 366\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Load optimal pre-trained model.\n",
    "td_cnn_opt = load_model(cp_path_alt)\n",
    "td_cnn_opt.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "HTldllI1roZC",
    "outputId": "b6c543ee-fc31-4d58-80f2-aec61005815b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - 3s 3s/step\n",
      "Average Test Loss:  13.345495223999023\n"
     ]
    }
   ],
   "source": [
    "# Test the trained model. Print test loss for all test sets.\n",
    "pred_alt = td_cnn_opt.predict(x_test)\n",
    "print(\"Average Test Loss: \", td_cnn_opt.evaluate(x_test, np.log(y_test + eps + 1)))\n",
    "\n",
    "# Rectify and un-normalize the output data.\n",
    "pred_alt = np.exp(pred_alt) - eps - 1\n",
    "pred_alt[pred_alt < eps] = 0\n",
    "\n",
    "# Export the heatmaps.\n",
    "path = \"/content/drive/My Drive/pred_results\"\n",
    "for ts in range(len(y_test)):\n",
    "  np.savetxt(path + '/' + \"pred_heatmap_alt_{}.csv\".format(ts), pred_alt[ts], delimiter=\",\")\n",
    "  np.savetxt(path + '/' + \"test_heatmap_alt_{}.csv\".format(ts), y_test[ts], delimiter=\",\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "conv_lstm_heatmap_pred.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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}