Denoise_Train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Denoise_Train.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EoZmdDMJV1l",
        "outputId": "6c3f921a-c98d-427c-8669-5bfceaddbf78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJXwUmG8JX6R"
      },
      "source": [
        "\n",
        "import librosa\n",
        "import pandas as pd\n",
        "import os\n",
        "import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import IPython.display as ipd\n",
        "import librosa.display\n",
        "import scipy\n",
        "import glob\n",
        "import numpy as np\n",
        "import math\n",
        "import warnings\n",
        "import pickle\n",
        "from sklearn.utils import shuffle\n",
        "import zipfile\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWlRjtqAJX-8"
      },
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()\n",
        "\n",
        "tf.random.set_seed(999)\n",
        "np.random.seed(999)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDqVCgA2JYEP"
      },
      "source": [
        "path_to_dataset = \"./dataset/tfrecords\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvdtY6QrJYG-",
        "outputId": "6f209e7b-8775-4bec-e0ab-84c14baeb851",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# get training and validation tf record file names\n",
        "train_tfrecords_filenames = glob.glob(os.path.join(path_to_dataset, 'train_*'))\n",
        "val_tfrecords_filenames = glob.glob(os.path.join(path_to_dataset, 'val_*'))\n",
        "\n",
        "# suffle the file names for training\n",
        "np.random.shuffle(train_tfrecords_filenames)\n",
        "print(\"Training file names: \", train_tfrecords_filenames)\n",
        "print(\"Validation file names: \", val_tfrecords_filenames)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training file names:  ['./dataset/tfrecords/train_0.tfrecords']\n",
            "Validation file names:  ['./dataset/tfrecords/val_0.tfrecords']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEAvaiwmJYJR",
        "outputId": "85377c79-33a8-467e-cf2f-3ca679056a14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "windowLength = 256\n",
        "overlap      = round(0.25 * windowLength) # overlap of 75%\n",
        "ffTLength    = windowLength\n",
        "inputFs      = 48e3\n",
        "fs           = 16e3\n",
        "numFeatures  = ffTLength//2 + 1\n",
        "numSegments  = 8\n",
        "print(\"windowLength:\",windowLength)\n",
        "print(\"overlap:\",overlap)\n",
        "print(\"ffTLength:\",ffTLength)\n",
        "print(\"inputFs:\",inputFs)\n",
        "print(\"fs:\",fs)\n",
        "print(\"numFeatures:\",numFeatures)\n",
        "print(\"numSegments:\",numSegments)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "windowLength: 256\n",
            "overlap: 64\n",
            "ffTLength: 256\n",
            "inputFs: 48000.0\n",
            "fs: 16000.0\n",
            "numFeatures: 129\n",
            "numSegments: 8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTB7Lvn2JYOQ"
      },
      "source": [
        "\n",
        "def tf_record_parser(record):\n",
        "    keys_to_features = {\n",
        "        \"noise_stft_phase\": tf.io.FixedLenFeature((), tf.string, default_value=\"\"),\n",
        "        'noise_stft_mag_features': tf.io.FixedLenFeature([], tf.string),\n",
        "        \"clean_stft_magnitude\": tf.io.FixedLenFeature((), tf.string)\n",
        "    }\n",
        "\n",
        "    features = tf.io.parse_single_example(record, keys_to_features)\n",
        "\n",
        "    noise_stft_mag_features = tf.io.decode_raw(features['noise_stft_mag_features'], tf.float32)\n",
        "    clean_stft_magnitude = tf.io.decode_raw(features['clean_stft_magnitude'], tf.float32)\n",
        "    noise_stft_phase = tf.io.decode_raw(features['noise_stft_phase'], tf.float32)\n",
        "\n",
        "    # reshape input and annotation images\n",
        "    noise_stft_mag_features = tf.reshape(noise_stft_mag_features, (129, 8, 1), name=\"noise_stft_mag_features\")\n",
        "    clean_stft_magnitude = tf.reshape(clean_stft_magnitude, (129, 1, 1), name=\"clean_stft_magnitude\")\n",
        "    noise_stft_phase = tf.reshape(noise_stft_phase, (129,), name=\"noise_stft_phase\")\n",
        "\n",
        "    return noise_stft_mag_features, clean_stft_magnitude"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qOfW-B7JYRF"
      },
      "source": [
        "\n",
        "train_dataset = tf.data.TFRecordDataset([train_tfrecords_filenames])\n",
        "train_dataset = train_dataset.map(tf_record_parser)\n",
        "train_dataset = train_dataset.shuffle(8192)\n",
        "train_dataset = train_dataset.repeat()\n",
        "train_dataset = train_dataset.batch(512)\n",
        "train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "test_dataset = tf.data.TFRecordDataset([val_tfrecords_filenames])\n",
        "test_dataset = test_dataset.map(tf_record_parser)\n",
        "test_dataset = test_dataset.repeat(1)\n",
        "test_dataset = test_dataset.batch(512)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63o8Ya8GJYVs"
      },
      "source": [
        "from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, Flatten, Dense, Reshape, Conv2DTranspose, BatchNormalization, Activation\n",
        "from tensorflow.keras import Model, Sequential\n",
        "\n",
        "def conv_block(x, filters, kernel_size, strides, padding='same', use_bn=True):\n",
        "  x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(0.0006))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  if use_bn:\n",
        "    x = BatchNormalization()(x)\n",
        "  return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1yb3sdNJYf2"
      },
      "source": [
        "\n",
        "def full_pre_activation_block(x, filters, kernel_size, strides, padding='same', use_bn=True):\n",
        "  shortcut = x\n",
        "  in_channels = x.shape[-1]\n",
        "\n",
        "  x = BatchNormalization()(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(x)\n",
        "\n",
        "  x = BatchNormalization()(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = Conv2D(filters=in_channels, kernel_size=kernel_size, strides=strides, padding='same')(x)\n",
        "\n",
        "  return shortcut + x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClrDtfuQJYjB"
      },
      "source": [
        "def build_model(l2_strength):\n",
        "  inputs = Input(shape=[numFeatures,numSegments,1])\n",
        "  x = inputs\n",
        "\n",
        "  # -----\n",
        "  x = tf.keras.layers.ZeroPadding2D(((4,4), (0,0)))(x)\n",
        "  x = Conv2D(filters=18, kernel_size=[9,8], strides=[1, 1], padding='valid', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  skip0 = Conv2D(filters=30, kernel_size=[5,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "                 kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(skip0)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=8, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  # -----\n",
        "  x = Conv2D(filters=18, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  skip1 = Conv2D(filters=30, kernel_size=[5,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "                 kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(skip1)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=8, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  # ----\n",
        "  x = Conv2D(filters=18, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "  \n",
        "  x = Conv2D(filters=30, kernel_size=[5,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=8, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  # ----\n",
        "  x = Conv2D(filters=18, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=30, kernel_size=[5,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "             kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = x + skip1\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=8, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  # ----\n",
        "  x = Conv2D(filters=18, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=30, kernel_size=[5,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "             kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = x + skip0\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  x = Conv2D(filters=8, kernel_size=[9,1], strides=[1, 1], padding='same', use_bias=False,\n",
        "              kernel_regularizer=tf.keras.regularizers.l2(l2_strength))(x)\n",
        "  x = Activation('relu')(x)\n",
        "  x = BatchNormalization()(x)\n",
        "\n",
        "  # ----\n",
        "  x = tf.keras.layers.SpatialDropout2D(0.2)(x)\n",
        "  x = Conv2D(filters=1, kernel_size=[129,1], strides=[1, 1], padding='same')(x)\n",
        "\n",
        "  model = Model(inputs=inputs, outputs=x)\n",
        "\n",
        "  optimizer = tf.keras.optimizers.Adam(3e-4)\n",
        "  model.compile(optimizer=optimizer, loss='mse', \n",
        "                metrics=[tf.keras.metrics.RootMeanSquaredError('rmse')])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aco2zR9GJYna",
        "outputId": "094fd841-bf19-49c2-bf34-037bd2c7d652",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "model = build_model(l2_strength=0.0)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 129, 8, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d (ZeroPadding2D)  (None, 137, 8, 1)    0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 129, 1, 18)   1296        zero_padding2d[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 129, 1, 18)   0           conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 129, 1, 18)   72          activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 129, 1, 30)   2700        batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 129, 1, 30)   0           conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 129, 1, 30)   120         activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 129, 1, 8)    2160        batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 129, 1, 8)    0           conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 129, 1, 8)    32          activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 129, 1, 18)   1296        batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 129, 1, 18)   0           conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 129, 1, 18)   72          activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 129, 1, 30)   2700        batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 129, 1, 30)   0           conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 129, 1, 30)   120         activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 129, 1, 8)    2160        batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 129, 1, 8)    0           conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 129, 1, 8)    32          activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 129, 1, 18)   1296        batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 129, 1, 18)   0           conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 129, 1, 18)   72          activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 129, 1, 30)   2700        batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 129, 1, 30)   0           conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 129, 1, 30)   120         activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 129, 1, 8)    2160        batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 129, 1, 8)    0           conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 129, 1, 8)    32          activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 129, 1, 18)   1296        batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 129, 1, 18)   0           conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 129, 1, 18)   72          activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 129, 1, 30)   2700        batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2 (TensorFlowOp [(None, 129, 1, 30)] 0           conv2d_10[0][0]                  \n",
            "                                                                 conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 129, 1, 30)   0           tf_op_layer_AddV2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 129, 1, 30)   120         activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 129, 1, 8)    2160        batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 129, 1, 8)    0           conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 129, 1, 8)    32          activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 129, 1, 18)   1296        batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 129, 1, 18)   0           conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 129, 1, 18)   72          activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 129, 1, 30)   2700        batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_1 (TensorFlow [(None, 129, 1, 30)] 0           conv2d_13[0][0]                  \n",
            "                                                                 conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 129, 1, 30)   0           tf_op_layer_AddV2_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 129, 1, 30)   120         activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 129, 1, 8)    2160        batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 129, 1, 8)    0           conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 129, 1, 8)    32          activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d (SpatialDropo (None, 129, 1, 8)    0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 129, 1, 1)    1033        spatial_dropout2d[0][0]          \n",
            "==================================================================================================\n",
            "Total params: 32,933\n",
            "Trainable params: 32,373\n",
            "Non-trainable params: 560\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f16hZds8JYsS",
        "outputId": "2223dc52-4348-41f6-b7e2-142c2286bf59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# You might need to install the following dependencies: sudo apt install python-pydot python-pydot-ng graphviz\n",
        "tf.keras.utils.plot_model(model, show_shapes=True, dpi=64)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTxFhzvpJYqN",
        "outputId": "88b506bd-dc77-470a-91d6-1de2858f8c8b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "baseline_val_loss = model.evaluate(test_dataset)[0]\n",
        "print(f\"Baseline accuracy {baseline_val_loss}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "393/393 [==============================] - 12s 30ms/step - loss: 0.2847 - rmse: 0.5336\n",
            "Baseline accuracy 0.2847049832344055\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh-MMeg9JYlg"
      },
      "source": [
        "def l2_norm(vector):\n",
        "    return np.square(vector)\n",
        "\n",
        "def SDR(denoised, cleaned, eps=1e-7): # Signal to Distortion Ratio\n",
        "    a = l2_norm(denoised)\n",
        "    b = l2_norm(denoised - cleaned)\n",
        "    a_b = a / b\n",
        "    return np.mean(10 * np.log10(a_b + eps))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDsF4MBxJYeK",
        "outputId": "a9581a77-ff3d-4a4c-f46a-8abfd108e3a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True, baseline=None)\n",
        "\n",
        "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, update_freq='batch')\n",
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath='./denoiser_cnn_log_mel_generator.h5', \n",
        "                                                         monitor='val_loss', save_best_only=True)\n",
        "\n",
        "model.fit(train_dataset,\n",
        "         steps_per_epoch=600, # you might need to change this\n",
        "         validation_data=test_dataset,\n",
        "         epochs=400,\n",
        "         callbacks=[early_stopping_callback, tensorboard_callback, checkpoint_callback]\n",
        "        )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "  1/600 [..............................] - ETA: 0s - loss: 1.8016 - rmse: 1.3422WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
            "Instructions for updating:\n",
            "use `tf.profiler.experimental.stop` instead.\n",
            "  2/600 [..............................] - ETA: 52s - loss: 1.7936 - rmse: 1.3392WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0475s vs `on_train_batch_end` time: 0.1270s). Check your callbacks.\n",
            "600/600 [==============================] - 55s 91ms/step - loss: 0.2753 - rmse: 0.5247 - val_loss: 0.1507 - val_rmse: 0.3882\n",
            "Epoch 2/400\n",
            "600/600 [==============================] - 55s 91ms/step - loss: 0.1336 - rmse: 0.3655 - val_loss: 0.1264 - val_rmse: 0.3555\n",
            "Epoch 3/400\n",
            "600/600 [==============================] - 54s 90ms/step - loss: 0.1275 - rmse: 0.3570 - val_loss: 0.1184 - val_rmse: 0.3440\n",
            "Epoch 4/400\n",
            "600/600 [==============================] - 54s 91ms/step - loss: 0.1181 - rmse: 0.3437 - val_loss: 0.1157 - val_rmse: 0.3401\n",
            "Epoch 5/400\n",
            "600/600 [==============================] - 42s 71ms/step - loss: 0.1194 - rmse: 0.3455 - val_loss: 0.1161 - val_rmse: 0.3407\n",
            "Epoch 6/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1155 - rmse: 0.3399 - val_loss: 0.1126 - val_rmse: 0.3356\n",
            "Epoch 7/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1089 - rmse: 0.3301 - val_loss: 0.1081 - val_rmse: 0.3288\n",
            "Epoch 8/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1127 - rmse: 0.3357 - val_loss: 0.1077 - val_rmse: 0.3282\n",
            "Epoch 9/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1074 - rmse: 0.3277 - val_loss: 0.1039 - val_rmse: 0.3224\n",
            "Epoch 10/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1062 - rmse: 0.3258 - val_loss: 0.1113 - val_rmse: 0.3336\n",
            "Epoch 11/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1054 - rmse: 0.3247 - val_loss: 0.1036 - val_rmse: 0.3218\n",
            "Epoch 12/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.1017 - rmse: 0.3190 - val_loss: 0.1035 - val_rmse: 0.3217\n",
            "Epoch 13/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.1058 - rmse: 0.3253 - val_loss: 0.1026 - val_rmse: 0.3203\n",
            "Epoch 14/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1028 - rmse: 0.3206 - val_loss: 0.1043 - val_rmse: 0.3229\n",
            "Epoch 15/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0973 - rmse: 0.3120 - val_loss: 0.1000 - val_rmse: 0.3162\n",
            "Epoch 16/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.1023 - rmse: 0.3199 - val_loss: 0.1003 - val_rmse: 0.3168\n",
            "Epoch 17/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0982 - rmse: 0.3134 - val_loss: 0.1027 - val_rmse: 0.3205\n",
            "Epoch 18/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.1006 - rmse: 0.3172 - val_loss: 0.0980 - val_rmse: 0.3131\n",
            "Epoch 19/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0987 - rmse: 0.3142 - val_loss: 0.1024 - val_rmse: 0.3200\n",
            "Epoch 20/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0948 - rmse: 0.3079 - val_loss: 0.0967 - val_rmse: 0.3109\n",
            "Epoch 21/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.1008 - rmse: 0.3176 - val_loss: 0.0984 - val_rmse: 0.3137\n",
            "Epoch 22/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0970 - rmse: 0.3115 - val_loss: 0.0976 - val_rmse: 0.3124\n",
            "Epoch 23/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0940 - rmse: 0.3067 - val_loss: 0.1021 - val_rmse: 0.3195\n",
            "Epoch 24/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0972 - rmse: 0.3118 - val_loss: 0.0972 - val_rmse: 0.3117\n",
            "Epoch 25/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0946 - rmse: 0.3075 - val_loss: 0.0949 - val_rmse: 0.3081\n",
            "Epoch 26/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0970 - rmse: 0.3114 - val_loss: 0.0973 - val_rmse: 0.3119\n",
            "Epoch 27/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0959 - rmse: 0.3096 - val_loss: 0.1004 - val_rmse: 0.3168\n",
            "Epoch 28/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0911 - rmse: 0.3019 - val_loss: 0.0960 - val_rmse: 0.3098\n",
            "Epoch 29/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0967 - rmse: 0.3110 - val_loss: 0.0956 - val_rmse: 0.3091\n",
            "Epoch 30/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0931 - rmse: 0.3051 - val_loss: 0.0943 - val_rmse: 0.3071\n",
            "Epoch 31/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0951 - rmse: 0.3083 - val_loss: 0.0948 - val_rmse: 0.3079\n",
            "Epoch 32/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0936 - rmse: 0.3059 - val_loss: 0.0970 - val_rmse: 0.3114\n",
            "Epoch 33/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0905 - rmse: 0.3008 - val_loss: 0.0941 - val_rmse: 0.3068\n",
            "Epoch 34/400\n",
            "600/600 [==============================] - 26s 44ms/step - loss: 0.0967 - rmse: 0.3110 - val_loss: 0.0951 - val_rmse: 0.3084\n",
            "Epoch 35/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0933 - rmse: 0.3055 - val_loss: 0.0973 - val_rmse: 0.3120\n",
            "Epoch 36/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0902 - rmse: 0.3004 - val_loss: 0.0934 - val_rmse: 0.3057\n",
            "Epoch 37/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0938 - rmse: 0.3062 - val_loss: 0.0944 - val_rmse: 0.3072\n",
            "Epoch 38/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0902 - rmse: 0.3004 - val_loss: 0.0968 - val_rmse: 0.3112\n",
            "Epoch 39/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0942 - rmse: 0.3070 - val_loss: 0.0961 - val_rmse: 0.3100\n",
            "Epoch 40/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0924 - rmse: 0.3039 - val_loss: 0.0975 - val_rmse: 0.3123\n",
            "Epoch 41/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0893 - rmse: 0.2989 - val_loss: 0.0966 - val_rmse: 0.3108\n",
            "Epoch 42/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0933 - rmse: 0.3054 - val_loss: 0.0961 - val_rmse: 0.3100\n",
            "Epoch 43/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0916 - rmse: 0.3026 - val_loss: 0.0959 - val_rmse: 0.3097\n",
            "Epoch 44/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0911 - rmse: 0.3018 - val_loss: 0.0974 - val_rmse: 0.3120\n",
            "Epoch 45/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0912 - rmse: 0.3019 - val_loss: 0.0929 - val_rmse: 0.3048\n",
            "Epoch 46/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0895 - rmse: 0.2992 - val_loss: 0.0978 - val_rmse: 0.3127\n",
            "Epoch 47/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0932 - rmse: 0.3054 - val_loss: 0.0963 - val_rmse: 0.3103\n",
            "Epoch 48/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0913 - rmse: 0.3021 - val_loss: 0.0952 - val_rmse: 0.3085\n",
            "Epoch 49/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0872 - rmse: 0.2953 - val_loss: 0.0966 - val_rmse: 0.3108\n",
            "Epoch 50/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0920 - rmse: 0.3034 - val_loss: 0.0926 - val_rmse: 0.3043\n",
            "Epoch 51/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0893 - rmse: 0.2988 - val_loss: 0.0948 - val_rmse: 0.3080\n",
            "Epoch 52/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0912 - rmse: 0.3020 - val_loss: 0.0955 - val_rmse: 0.3090\n",
            "Epoch 53/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0900 - rmse: 0.3001 - val_loss: 0.0935 - val_rmse: 0.3058\n",
            "Epoch 54/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0872 - rmse: 0.2953 - val_loss: 0.0940 - val_rmse: 0.3066\n",
            "Epoch 55/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0925 - rmse: 0.3042 - val_loss: 0.0979 - val_rmse: 0.3129\n",
            "Epoch 56/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0901 - rmse: 0.3001 - val_loss: 0.0951 - val_rmse: 0.3083\n",
            "Epoch 57/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0873 - rmse: 0.2954 - val_loss: 0.0936 - val_rmse: 0.3059\n",
            "Epoch 58/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0903 - rmse: 0.3005 - val_loss: 0.0936 - val_rmse: 0.3060\n",
            "Epoch 59/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0882 - rmse: 0.2969 - val_loss: 0.0921 - val_rmse: 0.3035\n",
            "Epoch 60/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0906 - rmse: 0.3010 - val_loss: 0.0939 - val_rmse: 0.3065\n",
            "Epoch 61/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0893 - rmse: 0.2989 - val_loss: 0.0941 - val_rmse: 0.3068\n",
            "Epoch 62/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0863 - rmse: 0.2937 - val_loss: 0.0928 - val_rmse: 0.3047\n",
            "Epoch 63/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0902 - rmse: 0.3003 - val_loss: 0.0939 - val_rmse: 0.3064\n",
            "Epoch 64/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0878 - rmse: 0.2964 - val_loss: 0.0930 - val_rmse: 0.3049\n",
            "Epoch 65/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0893 - rmse: 0.2989 - val_loss: 0.0969 - val_rmse: 0.3114\n",
            "Epoch 66/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0882 - rmse: 0.2970 - val_loss: 0.0964 - val_rmse: 0.3104\n",
            "Epoch 67/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0857 - rmse: 0.2928 - val_loss: 0.0965 - val_rmse: 0.3107\n",
            "Epoch 68/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0912 - rmse: 0.3020 - val_loss: 0.0954 - val_rmse: 0.3088\n",
            "Epoch 69/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0883 - rmse: 0.2972 - val_loss: 0.0943 - val_rmse: 0.3071\n",
            "Epoch 70/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0857 - rmse: 0.2927 - val_loss: 0.0922 - val_rmse: 0.3037\n",
            "Epoch 71/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0890 - rmse: 0.2984 - val_loss: 0.0915 - val_rmse: 0.3025\n",
            "Epoch 72/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0861 - rmse: 0.2934 - val_loss: 0.0985 - val_rmse: 0.3138\n",
            "Epoch 73/400\n",
            "600/600 [==============================] - 26s 44ms/step - loss: 0.0894 - rmse: 0.2991 - val_loss: 0.0935 - val_rmse: 0.3058\n",
            "Epoch 74/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0880 - rmse: 0.2966 - val_loss: 0.0933 - val_rmse: 0.3054\n",
            "Epoch 75/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0853 - rmse: 0.2920 - val_loss: 0.0939 - val_rmse: 0.3065\n",
            "Epoch 76/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0888 - rmse: 0.2979 - val_loss: 0.0931 - val_rmse: 0.3052\n",
            "Epoch 77/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0874 - rmse: 0.2957 - val_loss: 0.0936 - val_rmse: 0.3059\n",
            "Epoch 78/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0865 - rmse: 0.2940 - val_loss: 0.1094 - val_rmse: 0.3307\n",
            "Epoch 79/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0870 - rmse: 0.2950 - val_loss: 0.0932 - val_rmse: 0.3052\n",
            "Epoch 80/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0853 - rmse: 0.2921 - val_loss: 0.0929 - val_rmse: 0.3048\n",
            "Epoch 81/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0885 - rmse: 0.2975 - val_loss: 0.0933 - val_rmse: 0.3055\n",
            "Epoch 82/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0873 - rmse: 0.2955 - val_loss: 0.0932 - val_rmse: 0.3054\n",
            "Epoch 83/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0833 - rmse: 0.2886 - val_loss: 0.0917 - val_rmse: 0.3028\n",
            "Epoch 84/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0880 - rmse: 0.2967 - val_loss: 0.0927 - val_rmse: 0.3044\n",
            "Epoch 85/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0857 - rmse: 0.2927 - val_loss: 0.0932 - val_rmse: 0.3054\n",
            "Epoch 86/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0874 - rmse: 0.2957 - val_loss: 0.0967 - val_rmse: 0.3110\n",
            "Epoch 87/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0859 - rmse: 0.2930 - val_loss: 0.0962 - val_rmse: 0.3102\n",
            "Epoch 88/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0839 - rmse: 0.2897 - val_loss: 0.0957 - val_rmse: 0.3094\n",
            "Epoch 89/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0883 - rmse: 0.2971 - val_loss: 0.0934 - val_rmse: 0.3056\n",
            "Epoch 90/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0865 - rmse: 0.2941 - val_loss: 0.0925 - val_rmse: 0.3042\n",
            "Epoch 91/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0838 - rmse: 0.2895 - val_loss: 0.0932 - val_rmse: 0.3053\n",
            "Epoch 92/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0866 - rmse: 0.2943 - val_loss: 0.0934 - val_rmse: 0.3057\n",
            "Epoch 93/400\n",
            "600/600 [==============================] - 27s 44ms/step - loss: 0.0847 - rmse: 0.2910 - val_loss: 0.0925 - val_rmse: 0.3042\n",
            "Epoch 94/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0868 - rmse: 0.2946 - val_loss: 0.0948 - val_rmse: 0.3079\n",
            "Epoch 95/400\n",
            "600/600 [==============================] - 26s 43ms/step - loss: 0.0860 - rmse: 0.2933 - val_loss: 0.0921 - val_rmse: 0.3035\n",
            "Epoch 96/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0832 - rmse: 0.2884 - val_loss: 0.0916 - val_rmse: 0.3027\n",
            "Epoch 97/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0863 - rmse: 0.2938 - val_loss: 0.0928 - val_rmse: 0.3047\n",
            "Epoch 98/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0848 - rmse: 0.2913 - val_loss: 0.0920 - val_rmse: 0.3033\n",
            "Epoch 99/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0861 - rmse: 0.2934 - val_loss: 0.0953 - val_rmse: 0.3087\n",
            "Epoch 100/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0848 - rmse: 0.2911 - val_loss: 0.0926 - val_rmse: 0.3042\n",
            "Epoch 101/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0830 - rmse: 0.2881 - val_loss: 0.0964 - val_rmse: 0.3104\n",
            "Epoch 102/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0871 - rmse: 0.2951 - val_loss: 0.0945 - val_rmse: 0.3075\n",
            "Epoch 103/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0853 - rmse: 0.2921 - val_loss: 0.0941 - val_rmse: 0.3068\n",
            "Epoch 104/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0825 - rmse: 0.2873 - val_loss: 0.0915 - val_rmse: 0.3025\n",
            "Epoch 105/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0857 - rmse: 0.2928 - val_loss: 0.0898 - val_rmse: 0.2997\n",
            "Epoch 106/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0834 - rmse: 0.2888 - val_loss: 0.0967 - val_rmse: 0.3109\n",
            "Epoch 107/400\n",
            "600/600 [==============================] - 24s 39ms/step - loss: 0.0860 - rmse: 0.2933 - val_loss: 0.0932 - val_rmse: 0.3053\n",
            "Epoch 108/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0849 - rmse: 0.2914 - val_loss: 0.0920 - val_rmse: 0.3034\n",
            "Epoch 109/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0823 - rmse: 0.2868 - val_loss: 0.0928 - val_rmse: 0.3046\n",
            "Epoch 110/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0858 - rmse: 0.2929 - val_loss: 0.0926 - val_rmse: 0.3043\n",
            "Epoch 111/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0849 - rmse: 0.2914 - val_loss: 0.0948 - val_rmse: 0.3079\n",
            "Epoch 112/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0832 - rmse: 0.2885 - val_loss: 0.1025 - val_rmse: 0.3202\n",
            "Epoch 113/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0843 - rmse: 0.2903 - val_loss: 0.0928 - val_rmse: 0.3047\n",
            "Epoch 114/400\n",
            "600/600 [==============================] - 24s 39ms/step - loss: 0.0831 - rmse: 0.2882 - val_loss: 0.0944 - val_rmse: 0.3072\n",
            "Epoch 115/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0852 - rmse: 0.2920 - val_loss: 0.0918 - val_rmse: 0.3029\n",
            "Epoch 116/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0850 - rmse: 0.2915 - val_loss: 0.0919 - val_rmse: 0.3031\n",
            "Epoch 117/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0810 - rmse: 0.2846 - val_loss: 0.0959 - val_rmse: 0.3098\n",
            "Epoch 118/400\n",
            "600/600 [==============================] - 24s 39ms/step - loss: 0.0855 - rmse: 0.2923 - val_loss: 0.0925 - val_rmse: 0.3042\n",
            "Epoch 119/400\n",
            "600/600 [==============================] - 23s 39ms/step - loss: 0.0836 - rmse: 0.2891 - val_loss: 0.0911 - val_rmse: 0.3019\n",
            "Epoch 120/400\n",
            "600/600 [==============================] - 24s 39ms/step - loss: 0.0850 - rmse: 0.2916 - val_loss: 0.0947 - val_rmse: 0.3077\n",
            "Epoch 121/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0832 - rmse: 0.2884 - val_loss: 0.0927 - val_rmse: 0.3045\n",
            "Epoch 122/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0816 - rmse: 0.2857 - val_loss: 0.0954 - val_rmse: 0.3089\n",
            "Epoch 123/400\n",
            "600/600 [==============================] - 24s 39ms/step - loss: 0.0858 - rmse: 0.2930 - val_loss: 0.0928 - val_rmse: 0.3046\n",
            "Epoch 124/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0840 - rmse: 0.2898 - val_loss: 0.0931 - val_rmse: 0.3052\n",
            "Epoch 125/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0819 - rmse: 0.2862 - val_loss: 0.0947 - val_rmse: 0.3078\n",
            "Epoch 126/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0841 - rmse: 0.2900 - val_loss: 0.0928 - val_rmse: 0.3046\n",
            "Epoch 127/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0823 - rmse: 0.2869 - val_loss: 0.0929 - val_rmse: 0.3047\n",
            "Epoch 128/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0847 - rmse: 0.2911 - val_loss: 0.0943 - val_rmse: 0.3070\n",
            "Epoch 129/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0837 - rmse: 0.2893 - val_loss: 0.0953 - val_rmse: 0.3087\n",
            "Epoch 130/400\n",
            "600/600 [==============================] - 24s 41ms/step - loss: 0.0816 - rmse: 0.2857 - val_loss: 0.0909 - val_rmse: 0.3016\n",
            "Epoch 131/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0841 - rmse: 0.2900 - val_loss: 0.0926 - val_rmse: 0.3042\n",
            "Epoch 132/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0829 - rmse: 0.2879 - val_loss: 0.0901 - val_rmse: 0.3001\n",
            "Epoch 133/400\n",
            "600/600 [==============================] - 24s 39ms/step - loss: 0.0839 - rmse: 0.2897 - val_loss: 0.1006 - val_rmse: 0.3172\n",
            "Epoch 134/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0828 - rmse: 0.2877 - val_loss: 0.0920 - val_rmse: 0.3033\n",
            "Epoch 135/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0813 - rmse: 0.2851 - val_loss: 0.0947 - val_rmse: 0.3078\n",
            "Epoch 136/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0849 - rmse: 0.2914 - val_loss: 0.0934 - val_rmse: 0.3055\n",
            "Epoch 137/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0834 - rmse: 0.2888 - val_loss: 0.0948 - val_rmse: 0.3079\n",
            "Epoch 138/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0810 - rmse: 0.2846 - val_loss: 0.0937 - val_rmse: 0.3061\n",
            "Epoch 139/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0836 - rmse: 0.2891 - val_loss: 0.0911 - val_rmse: 0.3018\n",
            "Epoch 140/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0818 - rmse: 0.2859 - val_loss: 0.0941 - val_rmse: 0.3067\n",
            "Epoch 141/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0840 - rmse: 0.2898 - val_loss: 0.0947 - val_rmse: 0.3078\n",
            "Epoch 142/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0831 - rmse: 0.2882 - val_loss: 0.0914 - val_rmse: 0.3023\n",
            "Epoch 143/400\n",
            "600/600 [==============================] - 24s 40ms/step - loss: 0.0807 - rmse: 0.2841 - val_loss: 0.0922 - val_rmse: 0.3036\n",
            "Epoch 144/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0843 - rmse: 0.2903 - val_loss: 0.0940 - val_rmse: 0.3066\n",
            "Epoch 145/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0829 - rmse: 0.2879 - val_loss: 0.0963 - val_rmse: 0.3102\n",
            "Epoch 146/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0816 - rmse: 0.2856 - val_loss: 0.0974 - val_rmse: 0.3121\n",
            "Epoch 147/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0829 - rmse: 0.2879 - val_loss: 0.0927 - val_rmse: 0.3045\n",
            "Epoch 148/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0815 - rmse: 0.2855 - val_loss: 0.0959 - val_rmse: 0.3098\n",
            "Epoch 149/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0835 - rmse: 0.2890 - val_loss: 0.0928 - val_rmse: 0.3046\n",
            "Epoch 150/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0835 - rmse: 0.2890 - val_loss: 0.0953 - val_rmse: 0.3087\n",
            "Epoch 151/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0796 - rmse: 0.2822 - val_loss: 0.0942 - val_rmse: 0.3070\n",
            "Epoch 152/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0838 - rmse: 0.2895 - val_loss: 0.0925 - val_rmse: 0.3042\n",
            "Epoch 153/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0817 - rmse: 0.2858 - val_loss: 0.0916 - val_rmse: 0.3026\n",
            "Epoch 154/400\n",
            "600/600 [==============================] - 25s 41ms/step - loss: 0.0836 - rmse: 0.2892 - val_loss: 0.0940 - val_rmse: 0.3067\n",
            "Epoch 155/400\n",
            "600/600 [==============================] - 25s 42ms/step - loss: 0.0817 - rmse: 0.2858 - val_loss: 0.0919 - val_rmse: 0.3032\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5ba7de2940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2RPnfQlJYaz",
        "outputId": "59be7688-3d46-42b6-a05a-d95d438c9999",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "val_loss = model.evaluate(test_dataset)[0]\n",
        "if val_loss < baseline_val_loss:\n",
        "  print(\"New model saved.\")\n",
        "  model.save('./denoiser_CNN.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "393/393 [==============================] - 9s 24ms/step - loss: 0.0898 - rmse: 0.2997\n",
            "New model saved.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}