gai-MobileNetV2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q04h17M1e3SX"
   },
   "outputs": [],
   "source": [
    "#   This software component is licensed by ST under BSD-3-Clause license,\n",
    "#   the \"License\"; You may not use this file except in compliance with the\n",
    "#   License. You may obtain a copy of the License at:\n",
    "#             https://opensource.org/licenses/BSD-3-Clause"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hzWLuhG3EI6R"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/STMicroelectronics/stm32ai/blob/master/AI_resources/HAR/Human_Activity_Recognition.ipynb)\n",
    "\n",
    "# Human Activity Recognition\n",
    "\n",
    "This notebook shows the process of creating a basic motion sensing activity classifier model, using Keras, for STM32 embedded applications.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o9fzjxmBEoJf"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zHAlJ9au_YR9"
   },
   "source": [
    "## Import TensorFlow\n",
    "\n",
    "Use TensorFlow 2.5.0 to be aligned with X-CUBE-AI v7.0.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WJHjyWFm8C_t",
    "outputId": "7303e14a-d26e-47f2-d641-8e07b748926b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.version.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pTZNr-iyTfAi"
   },
   "source": [
    "## Loading the dataset\n",
    "\n",
    "The dataset is composed of 3-axis acceleration values stored in `.csv` text format. Each individual file corresponds to a capture characterised by it's containing folder name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWZ2KVSIk0Ss"
   },
   "source": [
    "Download and extract the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7iQZYED6ljkB",
    "outputId": "5780dcda-6e1a-4927-ff8b-13cc8661a189"
   },
   "outputs": [],
   "source": [
    "!wget -nc https://github.com/STMicroelectronics/stm32ai/raw/master/AI_resources/HAR/dataset.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2pFBme27ZADe",
    "outputId": "ab6cf7aa-4057-4540-9e7c-8d6f1c040431"
   },
   "outputs": [],
   "source": [
    "!unzip -n dataset.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_-UlWb8jk4bp"
   },
   "source": [
    "Load the dataset into memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wLB8JLSma7kz",
    "outputId": "537ae7b2-d5a2-4511-c66e-d0682c49001b"
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "\n",
    "# Load data into memory\n",
    "labels = ['egg_kongzai', 'egg_zhongjian', 'egg_wentai','guanzi_zhongjian',\n",
    "          'guanzi_wentai', 'shengnvguo_zhongjian', 'shengnvguo_wentai']\n",
    "x_recordings = []\n",
    "y_recordings = []\n",
    "recordings_filenames = []\n",
    "for i, label in enumerate(labels):\n",
    "    filenames = glob.glob('dataset/' + label + '/*.csv')\n",
    "    for filename in filenames:\n",
    "        data = np.loadtxt(filename, delimiter=',')\n",
    "        x_recordings.append(data)\n",
    "        y_recordings.append(i)\n",
    "        recordings_filenames.append(filename)\n",
    "x_recordings=np.array(x_recordings)\n",
    "x_recordings.shape\n",
    "y_recordings = np.array(y_recordings)\n",
    "#x_recordings = np.array(x_recordings).reshape(len(x_recordings), -1, 2)\n",
    "#y_recordings = np.array(y_recordings)\n",
    "\n",
    "#print(x_recordings.shape)\n",
    "#print(y_recordings.shape)\n",
    "#x_recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iliQcaEoH2l_"
   },
   "source": [
    "## Exploring the dataset\n",
    "To verify that the dataset has been loaded correctly, let's display and plot a few captures at random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "idQplarEH4Ez",
    "outputId": "98914d70-7c71-424c-9e9c-321bf42ee2ed"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Plot some captures\n",
    "random.seed(10)\n",
    "unique_rands = random.sample(range(len(x_recordings)), 10)\n",
    "plt.figure(figsize=(18, 10))\n",
    "for i, n in enumerate(unique_rands):\n",
    "    plt.subplot(5, 2, i + 1)\n",
    "    plt.margins(x=0, y=-0.25)\n",
    "    plt.plot(x_recordings[n])\n",
    "    plt.ylim(-4000, 4000)  # 4000 mg acc. range\n",
    "    plt.title(recordings_filenames[n].split('/')[-1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3uhehf6Imlj6"
   },
   "source": [
    "## Frame data\n",
    "\n",
    "Each capture is framed into smaller windows. The window size will be the input dimension for the neural network. Optional overlapping can be added to further increase the number of points in our data for training and testing.\n",
    "\n",
    "Here we are dividing the dataset into 1 second frames (26 samples at 26 Hz):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cK39FTaBeIdn",
    "outputId": "0192ff45-b036-4f27-d561-90baa796afb6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(921, 1, 4000, 2)\n",
      "(921, 4000, 2)\n",
      "(921,)\n",
      "['egg_kongzai', 'egg_zhongjian', 'egg_wentai', 'guanzi_zhongjian', 'guanzi_wentai', 'shengnvguo_zhongjian', 'shengnvguo_wentai']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def frame(x, frame_len, hop_len):\n",
    "    '''Slice a 3D data array into (overlapping) frames.\n",
    "    \n",
    "    Example\n",
    "    --------\n",
    "    >>> x = np.array([[0, 1, 2],\n",
    "                      [10, 11, 12],\n",
    "                      [20, 21, 22],\n",
    "                      [30, 31, 32],\n",
    "                      [40, 41, 42],\n",
    "                      [50, 51, 52],\n",
    "                      [60, 61, 62]])\n",
    "    >>> frames = x.frame(x, 3, 2)\n",
    "    >>> x.shape\n",
    "    (7, 3)\n",
    "    >>> frames.shape\n",
    "    (3, 3, 3)\n",
    "    '''\n",
    "\n",
    "    assert(x.shape == (len(x), 2))\n",
    "    assert(x.shape[0] >= frame_len)\n",
    "    assert(hop_len >= 1)\n",
    "\n",
    "    n_frames = 1 + (x.shape[0] - frame_len) // hop_len\n",
    "    shape = (n_frames, frame_len, x.shape[1])\n",
    "    strides = ((hop_len * x.strides[0],) + x.strides)\n",
    "    return np.lib.stride_tricks.as_strided(x, shape=shape, strides=strides)\n",
    "\n",
    "x_frames = []\n",
    "y_frames = []\n",
    "for i in range(x_recordings.shape[0]):\n",
    "    # frames = frame(x_recordings[i], 26, 26) # no overlap\n",
    "    frames = frame(x_recordings[i], 4000, 2000) # 50% overlap\n",
    "    x_frames.append(frames)\n",
    "    y_frames.append(np.full(frames.shape[0], y_recordings[i]))\n",
    "\n",
    "print(np.array(x_frames).shape)\n",
    "x_frames = np.concatenate(x_frames)\n",
    "y_frames = np.concatenate(y_frames)\n",
    "print(x_frames.shape)\n",
    "\n",
    "# Each output label is an integer between 0 and 2:\n",
    "print(y_frames.shape)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GMRufxHrH4xs"
   },
   "source": [
    "## Preprocessing the dataset\n",
    "\n",
    "To improve the model's performance, it is recommended to perform some kind of pre-processing on the input data before feeding them to the neural network model. Data pre-processing can include filtering, gravity rotation and/or suppression as it is done in [FP-AI-SENSING1]. In this example, a simple data scaling will suffice.\n",
    "\n",
    "Data scaling can be achieved by normalizing or standardizing input data. In this example, data is normalized into floating point values between `-1.0` and `1.0` by dividing them by the sensor's full range (`4000 mg`).\n",
    "\n",
    "[FP-AI-SENSING1]:https://www.st.com/en/embedded-software/fp-ai-sensing1.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "lncND4sLH6ug"
   },
   "outputs": [],
   "source": [
    "# Normalize input data between [-1;1]\n",
    "x_frames_normed = x_frames / 4000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I2YJ-4z2H7dU"
   },
   "source": [
    "### Preparing the dataset (train, test, split)\n",
    "The dataset is split into a _train_ and _test_ set. The data is first shuffled and then split with 25% reserved for model evaluation and testing purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gXVZmTLNH-ZP",
    "outputId": "224e1426-b57b-443b-88c3-4baed5897ca1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainning samples: (690, 4000, 2)\n",
      "Testing samples: (231, 4000, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x_frames_normed, y_frames, test_size=0.25)\n",
    "\n",
    "print(\"Trainning samples:\", x_train.shape)\n",
    "print(\"Testing samples:\", x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wVJATu_oIBjS"
   },
   "source": [
    "## Creating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lUlwf0k-InpG"
   },
   "source": [
    "Let's create a classifier model using Keras.\n",
    "\n",
    "Build the tf.keras.Sequential model by stacking layers. Choose an optimizer and loss function for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  tensorflow as tf\n",
    "\n",
    "def conv_block (x, filters, kernel=1, stride=1):\n",
    "\n",
    "    x = tf.keras.layers.Conv1D(filters, kernel, strides=stride, padding='same')(x)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.Activation(tf.nn.relu)(x)\n",
    "\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def depthwise_res_block(x, filters, kernel, stride, t, resdiual=False):\n",
    "\n",
    "    input_tensor = x\n",
    "    exp_channels = x.shape[-1]*t  #扩展维度\n",
    "\n",
    "    x = conv_block(x, exp_channels, 1, 1)\n",
    "\n",
    "    x = tf.keras.layers.SeparableConv1D(filters=filters, kernel_size=kernel, padding='same', strides=stride)(x)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.Activation(tf.nn.relu)(x)\n",
    "\n",
    "    x = tf.keras.layers.Conv1D(filters/2, 1, padding='same', strides=(1))(x)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "    if resdiual:\n",
    "        x = tf.keras.layers.add([x, input_tensor])\n",
    "\n",
    "    return x\n",
    "\n",
    "def inverted_residual_layers(x, filters, stride, t, n):\n",
    "\n",
    "    x = depthwise_res_block(x, filters, 3, stride, t, False)\n",
    "\n",
    "    for i in range(1, n):\n",
    "        x = depthwise_res_block(x, filters, 3, 1, t, True)\n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MovblieNetV2 (classes):\n",
    "\n",
    "    img_input = tf.keras.layers.Input(shape=(4000, 2))\n",
    "\n",
    "    x = conv_block(img_input, 32, 3, 2)\n",
    "    \n",
    "    x = tf.keras.layers.SeparableConv1D(filters=32,kernel_size=3, padding='same', strides=1)(x)\n",
    "    x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.Activation(tf.nn.relu)(x)\n",
    "\n",
    "    x = inverted_residual_layers(x, 32, 2, 6, 3)\n",
    "    x = inverted_residual_layers(x, 16, 2, 6, 1)\n",
    "    x = inverted_residual_layers(x, 8, 1, 1, 1)\n",
    "\n",
    "    x = conv_block(x, 16, 1, 2)\n",
    "\n",
    "    x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
    "    #x = tf.keras.layers.Reshape((1,1,1280))(x)\n",
    "\n",
    "    x = tf.keras.layers.Dense(classes, activation='softmax')(x)\n",
    "\n",
    "\n",
    "    model = tf.keras.Model(img_input, x)\n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 4000, 2)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 2000, 32)     224         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 2000, 32)     128         conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 2000, 32)     0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d (SeparableConv (None, 2000, 32)     1152        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 2000, 32)     128         separable_conv1d[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 2000, 32)     0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 2000, 192)    6336        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 2000, 192)    768         conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 2000, 192)    0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_1 (SeparableCo (None, 1000, 32)     6752        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 1000, 32)     128         separable_conv1d_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 1000, 32)     0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 1000, 16)     528         activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 1000, 16)     64          conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 1000, 96)     1632        batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 1000, 96)     384         conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 1000, 96)     0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_2 (SeparableCo (None, 1000, 32)     3392        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 1000, 32)     128         separable_conv1d_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 1000, 32)     0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 1000, 16)     528         activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 1000, 16)     64          conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 1000, 16)     0           batch_normalization_7[0][0]      \n",
      "                                                                 batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 1000, 96)     1632        add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1000, 96)     384         conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 1000, 96)     0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_3 (SeparableCo (None, 1000, 32)     3392        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 1000, 32)     128         separable_conv1d_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 1000, 32)     0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 1000, 16)     528         activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 1000, 16)     64          conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 1000, 16)     0           batch_normalization_10[0][0]     \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 1000, 96)     1632        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 1000, 96)     384         conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 1000, 96)     0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_4 (SeparableCo (None, 500, 16)      1840        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 500, 16)      64          separable_conv1d_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 500, 16)      0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 500, 8)       136         activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 500, 8)       32          conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 500, 8)       72          batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 500, 8)       32          conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 500, 8)       0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_5 (SeparableCo (None, 500, 8)       96          activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 500, 8)       32          separable_conv1d_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 500, 8)       0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 500, 4)       36          activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 500, 4)       16          conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 250, 16)      80          batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 250, 16)      64          conv1d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 250, 16)      0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 16)           0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 7)            119         global_average_pooling1d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 33,099\n",
      "Trainable params: 31,603\n",
      "Non-trainable params: 1,496\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = MovblieNetV2(7)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/130\n",
      "22/22 [==============================] - 2s 81ms/step - loss: 1.8277 - accuracy: 0.2884\n",
      "Epoch 2/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 1.6049 - accuracy: 0.3884\n",
      "Epoch 3/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 1.4507 - accuracy: 0.4333\n",
      "Epoch 4/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 1.3100 - accuracy: 0.5304\n",
      "Epoch 5/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 1.1418 - accuracy: 0.6623\n",
      "Epoch 6/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 1.0299 - accuracy: 0.7087\n",
      "Epoch 7/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.9321 - accuracy: 0.7217\n",
      "Epoch 8/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.8162 - accuracy: 0.7638\n",
      "Epoch 9/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.7413 - accuracy: 0.7928\n",
      "Epoch 10/130\n",
      "22/22 [==============================] - 1s 64ms/step - loss: 0.7050 - accuracy: 0.8449\n",
      "Epoch 11/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.6317 - accuracy: 0.8913\n",
      "Epoch 12/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.6443 - accuracy: 0.8681\n",
      "Epoch 13/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.5291 - accuracy: 0.9362\n",
      "Epoch 14/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.5227 - accuracy: 0.9246\n",
      "Epoch 15/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.4786 - accuracy: 0.9464\n",
      "Epoch 16/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.4395 - accuracy: 0.9478\n",
      "Epoch 17/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.3999 - accuracy: 0.9739\n",
      "Epoch 18/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.4076 - accuracy: 0.9551\n",
      "Epoch 19/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.3853 - accuracy: 0.9565\n",
      "Epoch 20/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.3341 - accuracy: 0.9725\n",
      "Epoch 21/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.3301 - accuracy: 0.9681\n",
      "Epoch 22/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.3838 - accuracy: 0.9493\n",
      "Epoch 23/130\n",
      "22/22 [==============================] - 1s 57ms/step - loss: 0.3151 - accuracy: 0.9696\n",
      "Epoch 24/130\n",
      "22/22 [==============================] - 1s 57ms/step - loss: 0.3261 - accuracy: 0.9536\n",
      "Epoch 25/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.2803 - accuracy: 0.9754\n",
      "Epoch 26/130\n",
      "22/22 [==============================] - 1s 58ms/step - loss: 0.2970 - accuracy: 0.9609\n",
      "Epoch 27/130\n",
      "22/22 [==============================] - 1s 58ms/step - loss: 0.2414 - accuracy: 0.9739\n",
      "Epoch 28/130\n",
      "22/22 [==============================] - 1s 64ms/step - loss: 0.2682 - accuracy: 0.9580\n",
      "Epoch 29/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.2566 - accuracy: 0.9638\n",
      "Epoch 30/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.2016 - accuracy: 0.9826\n",
      "Epoch 31/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.2438 - accuracy: 0.9609\n",
      "Epoch 32/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.2025 - accuracy: 0.9812\n",
      "Epoch 33/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.2181 - accuracy: 0.9638\n",
      "Epoch 34/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.2030 - accuracy: 0.9739\n",
      "Epoch 35/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1811 - accuracy: 0.9768\n",
      "Epoch 36/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1778 - accuracy: 0.9826\n",
      "Epoch 37/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1561 - accuracy: 0.9768\n",
      "Epoch 38/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1777 - accuracy: 0.9739\n",
      "Epoch 39/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1441 - accuracy: 0.9812\n",
      "Epoch 40/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1466 - accuracy: 0.9797\n",
      "Epoch 41/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1437 - accuracy: 0.9826\n",
      "Epoch 42/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1344 - accuracy: 0.9870\n",
      "Epoch 43/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1535 - accuracy: 0.9739\n",
      "Epoch 44/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1260 - accuracy: 0.9797\n",
      "Epoch 45/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1700 - accuracy: 0.9609\n",
      "Epoch 46/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.1215 - accuracy: 0.9899\n",
      "Epoch 47/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.1381 - accuracy: 0.9710\n",
      "Epoch 48/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.1248 - accuracy: 0.9841\n",
      "Epoch 49/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1155 - accuracy: 0.9841\n",
      "Epoch 50/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.1126 - accuracy: 0.9797\n",
      "Epoch 51/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.1333 - accuracy: 0.9754\n",
      "Epoch 52/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1217 - accuracy: 0.9797\n",
      "Epoch 53/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1298 - accuracy: 0.9768\n",
      "Epoch 54/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.1252 - accuracy: 0.9797\n",
      "Epoch 55/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1029 - accuracy: 0.9783\n",
      "Epoch 56/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1124 - accuracy: 0.9783\n",
      "Epoch 57/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0851 - accuracy: 0.9826\n",
      "Epoch 58/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1250 - accuracy: 0.9667\n",
      "Epoch 59/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.1356 - accuracy: 0.9739\n",
      "Epoch 60/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0837 - accuracy: 0.9899\n",
      "Epoch 61/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.0608 - accuracy: 0.9913\n",
      "Epoch 62/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0604 - accuracy: 0.9899\n",
      "Epoch 63/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0830 - accuracy: 0.9841\n",
      "Epoch 64/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0770 - accuracy: 0.9812\n",
      "Epoch 65/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0675 - accuracy: 0.9913\n",
      "Epoch 66/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0715 - accuracy: 0.9913\n",
      "Epoch 67/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0636 - accuracy: 0.9928\n",
      "Epoch 68/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0724 - accuracy: 0.9884\n",
      "Epoch 69/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0570 - accuracy: 0.9942\n",
      "Epoch 70/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0536 - accuracy: 0.9928\n",
      "Epoch 71/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0635 - accuracy: 0.9899\n",
      "Epoch 72/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0831 - accuracy: 0.9870\n",
      "Epoch 73/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.0571 - accuracy: 0.9942\n",
      "Epoch 74/130\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.0810 - accuracy: 0.9812\n",
      "Epoch 75/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0844 - accuracy: 0.9812\n",
      "Epoch 76/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.1132 - accuracy: 0.9739\n",
      "Epoch 77/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0791 - accuracy: 0.9884\n",
      "Epoch 78/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0501 - accuracy: 0.9899\n",
      "Epoch 79/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0513 - accuracy: 0.9928\n",
      "Epoch 80/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0764 - accuracy: 0.9855\n",
      "Epoch 81/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0525 - accuracy: 0.9899\n",
      "Epoch 82/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0423 - accuracy: 0.9928\n",
      "Epoch 83/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0496 - accuracy: 0.9928\n",
      "Epoch 84/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0441 - accuracy: 0.9942\n",
      "Epoch 85/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0708 - accuracy: 0.9884\n",
      "Epoch 86/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0499 - accuracy: 0.9957\n",
      "Epoch 87/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0650 - accuracy: 0.9870\n",
      "Epoch 88/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0467 - accuracy: 0.9899\n",
      "Epoch 89/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0321 - accuracy: 0.9986\n",
      "Epoch 90/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0331 - accuracy: 0.9942\n",
      "Epoch 91/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0395 - accuracy: 0.9928\n",
      "Epoch 92/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0287 - accuracy: 0.9971\n",
      "Epoch 93/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0423 - accuracy: 0.9913\n",
      "Epoch 94/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0346 - accuracy: 0.9942\n",
      "Epoch 95/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0460 - accuracy: 0.9899\n",
      "Epoch 96/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0396 - accuracy: 0.9928\n",
      "Epoch 97/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0223 - accuracy: 0.9986\n",
      "Epoch 98/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0315 - accuracy: 0.9942\n",
      "Epoch 99/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0354 - accuracy: 0.9971\n",
      "Epoch 100/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0412 - accuracy: 0.9942\n",
      "Epoch 101/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0447 - accuracy: 0.9884\n",
      "Epoch 102/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0494 - accuracy: 0.9870\n",
      "Epoch 103/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0984 - accuracy: 0.9725\n",
      "Epoch 104/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0482 - accuracy: 0.9928\n",
      "Epoch 105/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0259 - accuracy: 0.9986\n",
      "Epoch 106/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0321 - accuracy: 0.9971\n",
      "Epoch 107/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0235 - accuracy: 0.9971\n",
      "Epoch 108/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0303 - accuracy: 0.9942\n",
      "Epoch 109/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0389 - accuracy: 0.9942\n",
      "Epoch 110/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0473 - accuracy: 0.9855\n",
      "Epoch 111/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0284 - accuracy: 0.9928\n",
      "Epoch 112/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0308 - accuracy: 0.9942\n",
      "Epoch 113/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0295 - accuracy: 0.9957\n",
      "Epoch 114/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0396 - accuracy: 0.9928\n",
      "Epoch 115/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0298 - accuracy: 0.9913\n",
      "Epoch 116/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0325 - accuracy: 0.9942\n",
      "Epoch 117/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0215 - accuracy: 0.9971\n",
      "Epoch 118/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0166 - accuracy: 1.0000\n",
      "Epoch 119/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0222 - accuracy: 0.9957\n",
      "Epoch 120/130\n",
      "22/22 [==============================] - 1s 63ms/step - loss: 0.0229 - accuracy: 0.9986\n",
      "Epoch 121/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0296 - accuracy: 0.9928\n",
      "Epoch 122/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0220 - accuracy: 0.9942\n",
      "Epoch 123/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0398 - accuracy: 0.9942\n",
      "Epoch 124/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0265 - accuracy: 0.9942\n",
      "Epoch 125/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0407 - accuracy: 0.9957\n",
      "Epoch 126/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0403 - accuracy: 0.9942\n",
      "Epoch 127/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0226 - accuracy: 0.9971\n",
      "Epoch 128/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0295 - accuracy: 0.9971\n",
      "Epoch 129/130\n",
      "22/22 [==============================] - 1s 62ms/step - loss: 0.0345 - accuracy: 0.9957\n",
      "Epoch 130/130\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.0223 - accuracy: 0.9957\n",
      "8/8 - 0s - loss: 3.1748 - accuracy: 0.5931\n",
      "Test loss: 3.174807548522949\n",
      "Test acc: 0.5930736064910889\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 4000, 2)]    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 2000, 32)     224         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 2000, 32)     128         conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 2000, 32)     0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d (SeparableConv (None, 2000, 32)     1152        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 2000, 32)     128         separable_conv1d[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 2000, 32)     0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 2000, 192)    6336        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 2000, 192)    768         conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 2000, 192)    0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_1 (SeparableCo (None, 1000, 32)     6752        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 1000, 32)     128         separable_conv1d_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 1000, 32)     0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 1000, 16)     528         activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 1000, 16)     64          conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 1000, 96)     1632        batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 1000, 96)     384         conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 1000, 96)     0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_2 (SeparableCo (None, 1000, 32)     3392        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 1000, 32)     128         separable_conv1d_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 1000, 32)     0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 1000, 16)     528         activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 1000, 16)     64          conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 1000, 16)     0           batch_normalization_7[0][0]      \n",
      "                                                                 batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 1000, 96)     1632        add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 1000, 96)     384         conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 1000, 96)     0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_3 (SeparableCo (None, 1000, 32)     3392        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 1000, 32)     128         separable_conv1d_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 1000, 32)     0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 1000, 16)     528         activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 1000, 16)     64          conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 1000, 16)     0           batch_normalization_10[0][0]     \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 1000, 96)     1632        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 1000, 96)     384         conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 1000, 96)     0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_4 (SeparableCo (None, 500, 16)      1840        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 500, 16)      64          separable_conv1d_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 500, 16)      0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 500, 8)       136         activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 500, 8)       32          conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 500, 8)       72          batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 500, 8)       32          conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 500, 8)       0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv1d_5 (SeparableCo (None, 500, 8)       96          activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 500, 8)       32          separable_conv1d_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 500, 8)       0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 500, 4)       36          activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 500, 4)       16          conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 250, 16)      80          batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 250, 16)      64          conv1d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 250, 16)      0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 16)           0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 7)            119         global_average_pooling1d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 33,099\n",
      "Trainable params: 31,603\n",
      "Non-trainable params: 1,496\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "model_train = model.fit(x=x_train,y=y_train,validation_split=0.2,epochs=20,batch_size=300,verbose=1)\n",
    "def train_history(model_train,train,val):\n",
    "    plt.plot(model_train.history[train])\n",
    "    plt.plot(model_train.history[val])\n",
    "    plt.title('Train History')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel(train)\n",
    "    plt.legend(['train','validation'],loc='upper left')\n",
    "'''\n",
    "#model = tf.keras.models.Model(input,x)\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(x_train, y_train, epochs=130)\n",
    "test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)\n",
    "\n",
    "print(\"Test loss:\", test_loss)\n",
    "print(\"Test acc:\", test_acc)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "id": "ESYZJveHbAEV",
    "outputId": "efdd9e72-a65a-4a5f-f206-ff852d2155a8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "Y_pred = model.predict(x_test)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "confusion_matrix = tf.math.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure()\n",
    "sns.heatmap(confusion_matrix,\n",
    "            annot=True,\n",
    "            xticklabels=labels,\n",
    "            yticklabels=labels,\n",
    "            cmap=plt.cm.Blues,\n",
    "            fmt='d', cbar=False)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Dk5y66JXIr3a"
   },
   "outputs": [],
   "source": [
    "# Save the model into an HDF5 file ‘model.h5’\n",
    "model.save('MobileNetV2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "model = tf.keras.models.load_model('MobileNetV2.h5')\n",
    "\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "\n",
    "# Save the model.\n",
    "with open('MobileNetV2.tflite', 'wb') as f:\n",
    "  f.write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "collapsed_sections": [],
   "name": "Human Activity Recognition",
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   "language": "python",
   "name": "python3"
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  "language_info": {
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