ethan_autoencoder_test.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ethan_autoencoder_test.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPeptSOJIJqyS7TjANxGwUV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CSCCNY/final-project-recomposeclassics/blob/main/ethan_autoencoder_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhjgVLGzW9MM",
        "outputId": "701e5388-798f-436c-d557-61460337a2e0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jb8EOvb6ZVCe"
      },
      "source": [
        "# add Colab Notebooks to path so we can access autoencoder module\n",
        "import sys\n",
        "sys.path.insert(0,\"/content/gdrive/My Drive/Colab Notebooks\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyljbZRtXBsZ"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from autoencoder import VAE"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIjLjF5pXmJk"
      },
      "source": [
        "learning_rate = 0.0005\n",
        "batch_size = 32\n",
        "epochs = 150"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSfxe8z4XrHm"
      },
      "source": [
        "spectograms_path = \"/content/gdrive/My Drive/Deep Learning/Final Project/fsdd_test/spectrograms/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpQfIzEJYE0P"
      },
      "source": [
        "def load_fsdd(spectrograms_path):\n",
        "    x_train = []\n",
        "    for root, _, file_names in os.walk(spectrograms_path):\n",
        "        for file_name in file_names:\n",
        "            file_path = os.path.join(root, file_name)\n",
        "            spectrogram = np.load(file_path) # (n_bins, n_frames, 1) -> (256, 388)\n",
        "            x_train.append(spectrogram)\n",
        "    x_train = np.array(x_train)\n",
        "    x_train = x_train[..., np.newaxis] # -> (1575, 256, 388, 1)\n",
        "    return x_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tpo6eCbqYIjQ"
      },
      "source": [
        "def train(x_train, learning_rate, batch_size, epochs):\n",
        "    autoencoder = VAE(\n",
        "        input_shape=(256, 256, 1),\n",
        "        conv_filters=(512, 256, 128, 64, 32), #(512, 256, 128, 64, 32)\n",
        "        conv_kernels=(3, 3, 3, 3, 3), # (3, 3, 3, 3, 3)\n",
        "        conv_strides=(2, 2, 2, 2, (2, 1)), # (2, 2, 2, 2, (2, 1))\n",
        "        latent_space_dim=128\n",
        "    )\n",
        "    autoencoder.summary()\n",
        "    autoencoder.compile(learning_rate)\n",
        "    autoencoder.train(x_train, batch_size, epochs)\n",
        "    return autoencoder"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nUgLDI3YYQ1",
        "outputId": "ca2c9040-53f8-47ed-fad9-ab6ea44e7341"
      },
      "source": [
        "# load the log spectograms \n",
        "x_train = load_fsdd(spectograms_path)\n",
        "x_train.shape # -> 1575 samples (midi files), 256 bins, 388 frames"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 256, 256, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hz3NnbrzcZ4z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86642c7b-163c-4f1e-b92d-e5fe2e6373b2"
      },
      "source": [
        "autoencoder = train(x_train, learning_rate, batch_size, epochs)\n",
        "autoencoder.save(\"model\")"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"encoder\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "encoder_input (InputLayer)      [(None, 256, 256, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_layer_1 (Conv2D)   (None, 128, 128, 512 5120        encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "encoder_relu_1 (ReLU)           (None, 128, 128, 512 0           encoder_conv_layer_1[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "encoder_bn_1 (BatchNormalizatio (None, 128, 128, 512 2048        encoder_relu_1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_layer_2 (Conv2D)   (None, 64, 64, 256)  1179904     encoder_bn_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "encoder_relu_2 (ReLU)           (None, 64, 64, 256)  0           encoder_conv_layer_2[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "encoder_bn_2 (BatchNormalizatio (None, 64, 64, 256)  1024        encoder_relu_2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_layer_3 (Conv2D)   (None, 32, 32, 128)  295040      encoder_bn_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "encoder_relu_3 (ReLU)           (None, 32, 32, 128)  0           encoder_conv_layer_3[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "encoder_bn_3 (BatchNormalizatio (None, 32, 32, 128)  512         encoder_relu_3[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_layer_4 (Conv2D)   (None, 16, 16, 64)   73792       encoder_bn_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "encoder_relu_4 (ReLU)           (None, 16, 16, 64)   0           encoder_conv_layer_4[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "encoder_bn_4 (BatchNormalizatio (None, 16, 16, 64)   256         encoder_relu_4[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "encoder_conv_layer_5 (Conv2D)   (None, 8, 16, 32)    18464       encoder_bn_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "encoder_relu_5 (ReLU)           (None, 8, 16, 32)    0           encoder_conv_layer_5[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "encoder_bn_5 (BatchNormalizatio (None, 8, 16, 32)    128         encoder_relu_5[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 4096)         0           encoder_bn_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "mu (Dense)                      (None, 128)          524416      flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "log_variance (Dense)            (None, 128)          524416      flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "encoder_output (Lambda)         (None, 128)          0           mu[0][0]                         \n",
            "                                                                 log_variance[0][0]               \n",
            "==================================================================================================\n",
            "Total params: 2,625,120\n",
            "Trainable params: 2,623,136\n",
            "Non-trainable params: 1,984\n",
            "__________________________________________________________________________________________________\n",
            "Model: \"decoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "decoder_input (InputLayer)   [(None, 128)]             0         \n",
            "_________________________________________________________________\n",
            "decoder_dense (Dense)        (None, 4096)              528384    \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 8, 16, 32)         0         \n",
            "_________________________________________________________________\n",
            "decoder_conv_transpose_layer (None, 16, 16, 32)        9248      \n",
            "_________________________________________________________________\n",
            "decoder_relu_1 (ReLU)        (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "decoder_bn_1 (BatchNormaliza (None, 16, 16, 32)        128       \n",
            "_________________________________________________________________\n",
            "decoder_conv_transpose_layer (None, 32, 32, 64)        18496     \n",
            "_________________________________________________________________\n",
            "decoder_relu_2 (ReLU)        (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "decoder_bn_2 (BatchNormaliza (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "decoder_conv_transpose_layer (None, 64, 64, 128)       73856     \n",
            "_________________________________________________________________\n",
            "decoder_relu_3 (ReLU)        (None, 64, 64, 128)       0         \n",
            "_________________________________________________________________\n",
            "decoder_bn_3 (BatchNormaliza (None, 64, 64, 128)       512       \n",
            "_________________________________________________________________\n",
            "decoder_conv_transpose_layer (None, 128, 128, 256)     295168    \n",
            "_________________________________________________________________\n",
            "decoder_relu_4 (ReLU)        (None, 128, 128, 256)     0         \n",
            "_________________________________________________________________\n",
            "decoder_bn_4 (BatchNormaliza (None, 128, 128, 256)     1024      \n",
            "_________________________________________________________________\n",
            "decoder_conv_transpose_layer (None, 256, 256, 1)       2305      \n",
            "_________________________________________________________________\n",
            "sigmoid_layer (Activation)   (None, 256, 256, 1)       0         \n",
            "=================================================================\n",
            "Total params: 929,377\n",
            "Trainable params: 928,417\n",
            "Non-trainable params: 960\n",
            "_________________________________________________________________\n",
            "Model: \"autoencoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "encoder_input (InputLayer)   [(None, 256, 256, 1)]     0         \n",
            "_________________________________________________________________\n",
            "encoder (Functional)         (None, 128)               2625120   \n",
            "_________________________________________________________________\n",
            "decoder (Functional)         (None, 256, 256, 1)       929377    \n",
            "=================================================================\n",
            "Total params: 3,554,497\n",
            "Trainable params: 3,551,553\n",
            "Non-trainable params: 2,944\n",
            "_________________________________________________________________\n",
            "Train on 3 samples\n",
            "Epoch 1/150\n",
            "3/3 [==============================] - 34s 11s/sample - loss: 143448.0469 - _calculate_reconstruction_loss: 0.1432 - _calculate_kl_loss: 244.0963\n",
            "Epoch 2/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 156778.4219 - _calculate_reconstruction_loss: 0.1358 - _calculate_kl_loss: 20960.4668\n",
            "Epoch 3/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 135260.6094 - _calculate_reconstruction_loss: 0.1350 - _calculate_kl_loss: 247.0975\n",
            "Epoch 4/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 132355.8594 - _calculate_reconstruction_loss: 0.1321 - _calculate_kl_loss: 270.5020\n",
            "Epoch 5/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 129035.8750 - _calculate_reconstruction_loss: 0.1286 - _calculate_kl_loss: 394.1820\n",
            "Epoch 6/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 126442.8984 - _calculate_reconstruction_loss: 0.1257 - _calculate_kl_loss: 701.7006\n",
            "Epoch 7/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 123572.2109 - _calculate_reconstruction_loss: 0.1230 - _calculate_kl_loss: 588.0341\n",
            "Epoch 8/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 120207.3203 - _calculate_reconstruction_loss: 0.1197 - _calculate_kl_loss: 517.5847\n",
            "Epoch 9/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 116413.4922 - _calculate_reconstruction_loss: 0.1159 - _calculate_kl_loss: 526.8481\n",
            "Epoch 10/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 111745.4062 - _calculate_reconstruction_loss: 0.1112 - _calculate_kl_loss: 574.8416\n",
            "Epoch 11/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 105787.0859 - _calculate_reconstruction_loss: 0.1051 - _calculate_kl_loss: 644.1277\n",
            "Epoch 12/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 100445.5391 - _calculate_reconstruction_loss: 0.0997 - _calculate_kl_loss: 726.6685\n",
            "Epoch 13/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 96613.3516 - _calculate_reconstruction_loss: 0.0958 - _calculate_kl_loss: 772.7136\n",
            "Epoch 14/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 94024.3125 - _calculate_reconstruction_loss: 0.0932 - _calculate_kl_loss: 830.0692\n",
            "Epoch 15/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 91220.0859 - _calculate_reconstruction_loss: 0.0903 - _calculate_kl_loss: 882.0464\n",
            "Epoch 16/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 87984.0938 - _calculate_reconstruction_loss: 0.0871 - _calculate_kl_loss: 929.3721\n",
            "Epoch 17/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 84937.0547 - _calculate_reconstruction_loss: 0.0840 - _calculate_kl_loss: 976.1092\n",
            "Epoch 18/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 82538.3125 - _calculate_reconstruction_loss: 0.0815 - _calculate_kl_loss: 1018.0279\n",
            "Epoch 19/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 80608.3047 - _calculate_reconstruction_loss: 0.0796 - _calculate_kl_loss: 1056.5621\n",
            "Epoch 20/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 78674.9844 - _calculate_reconstruction_loss: 0.0776 - _calculate_kl_loss: 1082.4198\n",
            "Epoch 21/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 76504.6250 - _calculate_reconstruction_loss: 0.0754 - _calculate_kl_loss: 1097.0062\n",
            "Epoch 22/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 74412.1328 - _calculate_reconstruction_loss: 0.0733 - _calculate_kl_loss: 1098.5458\n",
            "Epoch 23/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 72186.4375 - _calculate_reconstruction_loss: 0.0711 - _calculate_kl_loss: 1090.8055\n",
            "Epoch 24/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 69924.2109 - _calculate_reconstruction_loss: 0.0688 - _calculate_kl_loss: 1075.3114\n",
            "Epoch 25/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 67458.8359 - _calculate_reconstruction_loss: 0.0664 - _calculate_kl_loss: 1055.5070\n",
            "Epoch 26/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 64878.3320 - _calculate_reconstruction_loss: 0.0638 - _calculate_kl_loss: 1030.8976\n",
            "Epoch 27/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 62167.6992 - _calculate_reconstruction_loss: 0.0612 - _calculate_kl_loss: 998.3875\n",
            "Epoch 28/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 59332.6367 - _calculate_reconstruction_loss: 0.0584 - _calculate_kl_loss: 955.0891\n",
            "Epoch 29/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 56559.9492 - _calculate_reconstruction_loss: 0.0556 - _calculate_kl_loss: 910.5508\n",
            "Epoch 30/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 53550.3086 - _calculate_reconstruction_loss: 0.0527 - _calculate_kl_loss: 870.0457\n",
            "Epoch 31/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 50778.4688 - _calculate_reconstruction_loss: 0.0499 - _calculate_kl_loss: 830.7607\n",
            "Epoch 32/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 48243.1562 - _calculate_reconstruction_loss: 0.0475 - _calculate_kl_loss: 790.7207\n",
            "Epoch 33/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 46019.9570 - _calculate_reconstruction_loss: 0.0453 - _calculate_kl_loss: 751.1566\n",
            "Epoch 34/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 43779.1836 - _calculate_reconstruction_loss: 0.0431 - _calculate_kl_loss: 726.2502\n",
            "Epoch 35/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 41974.9805 - _calculate_reconstruction_loss: 0.0413 - _calculate_kl_loss: 703.1511\n",
            "Epoch 36/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 40360.5742 - _calculate_reconstruction_loss: 0.0397 - _calculate_kl_loss: 676.5936\n",
            "Epoch 37/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 39667.9062 - _calculate_reconstruction_loss: 0.0390 - _calculate_kl_loss: 661.2798\n",
            "Epoch 38/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 38652.5742 - _calculate_reconstruction_loss: 0.0380 - _calculate_kl_loss: 639.2766\n",
            "Epoch 39/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 36611.7539 - _calculate_reconstruction_loss: 0.0360 - _calculate_kl_loss: 648.4998\n",
            "Epoch 40/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 36187.1953 - _calculate_reconstruction_loss: 0.0355 - _calculate_kl_loss: 669.1096\n",
            "Epoch 41/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 35043.1211 - _calculate_reconstruction_loss: 0.0344 - _calculate_kl_loss: 661.0002\n",
            "Epoch 42/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 33561.0625 - _calculate_reconstruction_loss: 0.0329 - _calculate_kl_loss: 688.0879\n",
            "Epoch 43/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 32940.0352 - _calculate_reconstruction_loss: 0.0322 - _calculate_kl_loss: 710.7554\n",
            "Epoch 44/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 32382.0957 - _calculate_reconstruction_loss: 0.0317 - _calculate_kl_loss: 725.9016\n",
            "Epoch 45/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 31288.5020 - _calculate_reconstruction_loss: 0.0306 - _calculate_kl_loss: 727.6877\n",
            "Epoch 46/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 31368.4062 - _calculate_reconstruction_loss: 0.0306 - _calculate_kl_loss: 728.1341\n",
            "Epoch 47/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 30174.6543 - _calculate_reconstruction_loss: 0.0294 - _calculate_kl_loss: 747.0566\n",
            "Epoch 48/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 29931.1230 - _calculate_reconstruction_loss: 0.0292 - _calculate_kl_loss: 773.2026\n",
            "Epoch 49/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 28859.8125 - _calculate_reconstruction_loss: 0.0281 - _calculate_kl_loss: 785.9747\n",
            "Epoch 50/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 28401.3340 - _calculate_reconstruction_loss: 0.0276 - _calculate_kl_loss: 794.8539\n",
            "Epoch 51/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 28246.3125 - _calculate_reconstruction_loss: 0.0274 - _calculate_kl_loss: 804.7747\n",
            "Epoch 52/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 27513.5996 - _calculate_reconstruction_loss: 0.0267 - _calculate_kl_loss: 810.3723\n",
            "Epoch 53/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 27175.5391 - _calculate_reconstruction_loss: 0.0264 - _calculate_kl_loss: 817.7959\n",
            "Epoch 54/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 26961.7285 - _calculate_reconstruction_loss: 0.0261 - _calculate_kl_loss: 827.1260\n",
            "Epoch 55/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 26095.6172 - _calculate_reconstruction_loss: 0.0253 - _calculate_kl_loss: 822.5862\n",
            "Epoch 56/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 25940.7188 - _calculate_reconstruction_loss: 0.0251 - _calculate_kl_loss: 813.4448\n",
            "Epoch 57/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 25436.5645 - _calculate_reconstruction_loss: 0.0246 - _calculate_kl_loss: 807.8081\n",
            "Epoch 58/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 25013.8184 - _calculate_reconstruction_loss: 0.0242 - _calculate_kl_loss: 801.6545\n",
            "Epoch 59/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 24856.0781 - _calculate_reconstruction_loss: 0.0241 - _calculate_kl_loss: 797.4697\n",
            "Epoch 60/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 24313.9062 - _calculate_reconstruction_loss: 0.0235 - _calculate_kl_loss: 789.3406\n",
            "Epoch 61/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 23890.6816 - _calculate_reconstruction_loss: 0.0231 - _calculate_kl_loss: 775.0176\n",
            "Epoch 62/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 23462.1855 - _calculate_reconstruction_loss: 0.0227 - _calculate_kl_loss: 762.7480\n",
            "Epoch 63/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 23220.7090 - _calculate_reconstruction_loss: 0.0225 - _calculate_kl_loss: 751.6377\n",
            "Epoch 64/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 22789.6348 - _calculate_reconstruction_loss: 0.0221 - _calculate_kl_loss: 736.1260\n",
            "Epoch 65/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 22382.9785 - _calculate_reconstruction_loss: 0.0217 - _calculate_kl_loss: 719.5981\n",
            "Epoch 66/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 22051.8691 - _calculate_reconstruction_loss: 0.0214 - _calculate_kl_loss: 700.3813\n",
            "Epoch 67/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 21706.6758 - _calculate_reconstruction_loss: 0.0210 - _calculate_kl_loss: 677.7270\n",
            "Epoch 68/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 21360.6641 - _calculate_reconstruction_loss: 0.0207 - _calculate_kl_loss: 655.9373\n",
            "Epoch 69/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 21028.6133 - _calculate_reconstruction_loss: 0.0204 - _calculate_kl_loss: 637.9901\n",
            "Epoch 70/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 20640.1250 - _calculate_reconstruction_loss: 0.0200 - _calculate_kl_loss: 617.3499\n",
            "Epoch 71/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 20276.9473 - _calculate_reconstruction_loss: 0.0197 - _calculate_kl_loss: 595.4038\n",
            "Epoch 72/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 19900.1934 - _calculate_reconstruction_loss: 0.0193 - _calculate_kl_loss: 579.1719\n",
            "Epoch 73/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 19521.9883 - _calculate_reconstruction_loss: 0.0190 - _calculate_kl_loss: 561.1562\n",
            "Epoch 74/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 19211.5195 - _calculate_reconstruction_loss: 0.0187 - _calculate_kl_loss: 542.7296\n",
            "Epoch 75/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 18871.2734 - _calculate_reconstruction_loss: 0.0183 - _calculate_kl_loss: 526.8185\n",
            "Epoch 76/150\n",
            "3/3 [==============================] - 0s 38ms/sample - loss: 18503.8262 - _calculate_reconstruction_loss: 0.0180 - _calculate_kl_loss: 514.2291\n",
            "Epoch 77/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 18317.2109 - _calculate_reconstruction_loss: 0.0178 - _calculate_kl_loss: 496.9205\n",
            "Epoch 78/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 18014.7500 - _calculate_reconstruction_loss: 0.0175 - _calculate_kl_loss: 477.1690\n",
            "Epoch 79/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 17648.6895 - _calculate_reconstruction_loss: 0.0172 - _calculate_kl_loss: 464.8925\n",
            "Epoch 80/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 17361.5410 - _calculate_reconstruction_loss: 0.0169 - _calculate_kl_loss: 458.8971\n",
            "Epoch 81/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 17047.3711 - _calculate_reconstruction_loss: 0.0166 - _calculate_kl_loss: 450.7964\n",
            "Epoch 82/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 16809.3262 - _calculate_reconstruction_loss: 0.0164 - _calculate_kl_loss: 438.9386\n",
            "Epoch 83/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 16752.0762 - _calculate_reconstruction_loss: 0.0163 - _calculate_kl_loss: 428.3185\n",
            "Epoch 84/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 16545.4199 - _calculate_reconstruction_loss: 0.0161 - _calculate_kl_loss: 427.4706\n",
            "Epoch 85/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 16216.9893 - _calculate_reconstruction_loss: 0.0158 - _calculate_kl_loss: 433.9147\n",
            "Epoch 86/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 15953.1455 - _calculate_reconstruction_loss: 0.0155 - _calculate_kl_loss: 431.6297\n",
            "Epoch 87/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 15790.3838 - _calculate_reconstruction_loss: 0.0154 - _calculate_kl_loss: 427.5979\n",
            "Epoch 88/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 15594.9717 - _calculate_reconstruction_loss: 0.0152 - _calculate_kl_loss: 430.1465\n",
            "Epoch 89/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 15424.8818 - _calculate_reconstruction_loss: 0.0150 - _calculate_kl_loss: 434.4712\n",
            "Epoch 90/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 15152.8291 - _calculate_reconstruction_loss: 0.0147 - _calculate_kl_loss: 433.1240\n",
            "Epoch 91/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 15171.2188 - _calculate_reconstruction_loss: 0.0147 - _calculate_kl_loss: 432.4175\n",
            "Epoch 92/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 14946.3721 - _calculate_reconstruction_loss: 0.0145 - _calculate_kl_loss: 440.9425\n",
            "Epoch 93/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 14735.6836 - _calculate_reconstruction_loss: 0.0143 - _calculate_kl_loss: 442.1855\n",
            "Epoch 94/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 14748.6123 - _calculate_reconstruction_loss: 0.0143 - _calculate_kl_loss: 437.7959\n",
            "Epoch 95/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 14560.5156 - _calculate_reconstruction_loss: 0.0141 - _calculate_kl_loss: 442.7058\n",
            "Epoch 96/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 14386.4609 - _calculate_reconstruction_loss: 0.0139 - _calculate_kl_loss: 451.2509\n",
            "Epoch 97/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 14368.0029 - _calculate_reconstruction_loss: 0.0139 - _calculate_kl_loss: 454.2017\n",
            "Epoch 98/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 14170.3311 - _calculate_reconstruction_loss: 0.0137 - _calculate_kl_loss: 452.6248\n",
            "Epoch 99/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 14070.5693 - _calculate_reconstruction_loss: 0.0136 - _calculate_kl_loss: 451.4602\n",
            "Epoch 100/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 13929.9756 - _calculate_reconstruction_loss: 0.0135 - _calculate_kl_loss: 449.8118\n",
            "Epoch 101/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 13878.2305 - _calculate_reconstruction_loss: 0.0134 - _calculate_kl_loss: 454.9454\n",
            "Epoch 102/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 14122.3213 - _calculate_reconstruction_loss: 0.0137 - _calculate_kl_loss: 465.4316\n",
            "Epoch 103/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 14067.7539 - _calculate_reconstruction_loss: 0.0136 - _calculate_kl_loss: 466.7927\n",
            "Epoch 104/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 13571.6641 - _calculate_reconstruction_loss: 0.0131 - _calculate_kl_loss: 477.6803\n",
            "Epoch 105/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 13455.2373 - _calculate_reconstruction_loss: 0.0130 - _calculate_kl_loss: 490.5133\n",
            "Epoch 106/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 13711.7148 - _calculate_reconstruction_loss: 0.0132 - _calculate_kl_loss: 502.5842\n",
            "Epoch 107/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 13145.0195 - _calculate_reconstruction_loss: 0.0126 - _calculate_kl_loss: 502.3068\n",
            "Epoch 108/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 13168.3760 - _calculate_reconstruction_loss: 0.0127 - _calculate_kl_loss: 507.5156\n",
            "Epoch 109/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 12723.6299 - _calculate_reconstruction_loss: 0.0122 - _calculate_kl_loss: 517.3395\n",
            "Epoch 110/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 12816.3154 - _calculate_reconstruction_loss: 0.0123 - _calculate_kl_loss: 523.8710\n",
            "Epoch 111/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 12419.5107 - _calculate_reconstruction_loss: 0.0119 - _calculate_kl_loss: 525.1107\n",
            "Epoch 112/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 12271.3594 - _calculate_reconstruction_loss: 0.0117 - _calculate_kl_loss: 523.8548\n",
            "Epoch 113/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 12289.1904 - _calculate_reconstruction_loss: 0.0118 - _calculate_kl_loss: 522.1654\n",
            "Epoch 114/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 12008.5869 - _calculate_reconstruction_loss: 0.0115 - _calculate_kl_loss: 525.6915\n",
            "Epoch 115/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 11952.1475 - _calculate_reconstruction_loss: 0.0114 - _calculate_kl_loss: 530.1232\n",
            "Epoch 116/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 11675.7812 - _calculate_reconstruction_loss: 0.0111 - _calculate_kl_loss: 528.8259\n",
            "Epoch 117/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 11565.2656 - _calculate_reconstruction_loss: 0.0110 - _calculate_kl_loss: 523.5622\n",
            "Epoch 118/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 11433.2656 - _calculate_reconstruction_loss: 0.0109 - _calculate_kl_loss: 520.2827\n",
            "Epoch 119/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 11202.0225 - _calculate_reconstruction_loss: 0.0107 - _calculate_kl_loss: 519.2322\n",
            "Epoch 120/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 11100.2979 - _calculate_reconstruction_loss: 0.0106 - _calculate_kl_loss: 515.5920\n",
            "Epoch 121/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 10922.1104 - _calculate_reconstruction_loss: 0.0104 - _calculate_kl_loss: 508.8441\n",
            "Epoch 122/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 10895.6436 - _calculate_reconstruction_loss: 0.0104 - _calculate_kl_loss: 504.3966\n",
            "Epoch 123/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 10686.1904 - _calculate_reconstruction_loss: 0.0102 - _calculate_kl_loss: 501.7298\n",
            "Epoch 124/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 10566.5391 - _calculate_reconstruction_loss: 0.0101 - _calculate_kl_loss: 495.7062\n",
            "Epoch 125/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 10492.6943 - _calculate_reconstruction_loss: 0.0100 - _calculate_kl_loss: 484.3514\n",
            "Epoch 126/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 10377.1006 - _calculate_reconstruction_loss: 0.0099 - _calculate_kl_loss: 472.8900\n",
            "Epoch 127/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 10296.7314 - _calculate_reconstruction_loss: 0.0098 - _calculate_kl_loss: 464.3832\n",
            "Epoch 128/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 10193.9912 - _calculate_reconstruction_loss: 0.0097 - _calculate_kl_loss: 461.3539\n",
            "Epoch 129/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 10137.6523 - _calculate_reconstruction_loss: 0.0097 - _calculate_kl_loss: 456.7208\n",
            "Epoch 130/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 10052.0498 - _calculate_reconstruction_loss: 0.0096 - _calculate_kl_loss: 447.9196\n",
            "Epoch 131/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 9979.4258 - _calculate_reconstruction_loss: 0.0095 - _calculate_kl_loss: 436.1402\n",
            "Epoch 132/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 9881.0400 - _calculate_reconstruction_loss: 0.0095 - _calculate_kl_loss: 427.1715\n",
            "Epoch 133/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 9828.4053 - _calculate_reconstruction_loss: 0.0094 - _calculate_kl_loss: 422.6384\n",
            "Epoch 134/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 9719.9297 - _calculate_reconstruction_loss: 0.0093 - _calculate_kl_loss: 416.2257\n",
            "Epoch 135/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9693.0312 - _calculate_reconstruction_loss: 0.0093 - _calculate_kl_loss: 411.2962\n",
            "Epoch 136/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9601.0459 - _calculate_reconstruction_loss: 0.0092 - _calculate_kl_loss: 403.5851\n",
            "Epoch 137/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 9525.0430 - _calculate_reconstruction_loss: 0.0091 - _calculate_kl_loss: 397.5393\n",
            "Epoch 138/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 9499.1875 - _calculate_reconstruction_loss: 0.0091 - _calculate_kl_loss: 391.5037\n",
            "Epoch 139/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 9461.8711 - _calculate_reconstruction_loss: 0.0091 - _calculate_kl_loss: 390.0136\n",
            "Epoch 140/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9373.6992 - _calculate_reconstruction_loss: 0.0090 - _calculate_kl_loss: 386.3892\n",
            "Epoch 141/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9351.2988 - _calculate_reconstruction_loss: 0.0090 - _calculate_kl_loss: 385.1225\n",
            "Epoch 142/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 9271.1279 - _calculate_reconstruction_loss: 0.0089 - _calculate_kl_loss: 378.5403\n",
            "Epoch 143/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9284.9404 - _calculate_reconstruction_loss: 0.0089 - _calculate_kl_loss: 372.0376\n",
            "Epoch 144/150\n",
            "3/3 [==============================] - 0s 34ms/sample - loss: 9196.5342 - _calculate_reconstruction_loss: 0.0088 - _calculate_kl_loss: 376.9525\n",
            "Epoch 145/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9119.9990 - _calculate_reconstruction_loss: 0.0087 - _calculate_kl_loss: 376.2502\n",
            "Epoch 146/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9109.6455 - _calculate_reconstruction_loss: 0.0087 - _calculate_kl_loss: 375.7151\n",
            "Epoch 147/150\n",
            "3/3 [==============================] - 0s 37ms/sample - loss: 9029.6006 - _calculate_reconstruction_loss: 0.0087 - _calculate_kl_loss: 377.0111\n",
            "Epoch 148/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9012.5518 - _calculate_reconstruction_loss: 0.0086 - _calculate_kl_loss: 374.3448\n",
            "Epoch 149/150\n",
            "3/3 [==============================] - 0s 35ms/sample - loss: 9016.4717 - _calculate_reconstruction_loss: 0.0086 - _calculate_kl_loss: 372.7056\n",
            "Epoch 150/150\n",
            "3/3 [==============================] - 0s 36ms/sample - loss: 8927.3076 - _calculate_reconstruction_loss: 0.0086 - _calculate_kl_loss: 376.6690\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}