test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-01 18:12:48.529061: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-08-01 18:12:48.529085: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "from os import listdir\n",
    "from os.path import isdir, join\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ajuda', 'Seguranca', 'Policia', 'Ambulancia', 'Roubo', 'Arma', 'Faca', 'Assassinato']\n"
     ]
    }
   ],
   "source": [
    "# Create list of all targets (minus background noise)\n",
    "dataset_path = '/home/anass/Projet_Stage/Database-20220727T140818Z-001/organized/'\n",
    "all_targets = all_targets = [name for name in listdir(dataset_path) if isdir(join(dataset_path, name))]\n",
    "all_targets.remove('_background_noise_')\n",
    "print(all_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Settings\n",
    "feature_sets_path = '/home/anass/Projet_Stage/Database-20220727T140818Z-001/'\n",
    "feature_sets_filename = 'all_targets_mfcc_sets.npz'\n",
    "model_filename = 'wake_word_stop_model.h5'\n",
    "wake_word = 'Ajuda'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x_train', 'y_train', 'x_val', 'y_val', 'x_test', 'y_test']\n"
     ]
    }
   ],
   "source": [
    "# Load feature sets\n",
    "feature_sets = np.load(join(feature_sets_path, feature_sets_filename))\n",
    "print(feature_sets.files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign feature sets\n",
    "x_train = feature_sets['x_train']\n",
    "y_train = feature_sets['y_train']\n",
    "x_val = feature_sets['x_val']\n",
    "y_val = feature_sets['y_val']\n",
    "x_test = feature_sets['x_test']\n",
    "y_test = feature_sets['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77491, 16, 16)\n",
      "(9707, 16, 16)\n",
      "(9658, 16, 16)\n"
     ]
    }
   ],
   "source": [
    "# Look at tensor dimensions\n",
    "print(x_train.shape)\n",
    "print(x_val.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12. 23. 11. ... 16.  6. 11.]\n"
     ]
    }
   ],
   "source": [
    "# Peek at labels\n",
    "print(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert ground truth arrays to one wake word (1) and 'other' (0)\n",
    "wake_word_index = all_targets.index(wake_word)\n",
    "y_train = np.equal(y_train, wake_word_index).astype('float64')\n",
    "y_val = np.equal(y_val, wake_word_index).astype('float64')\n",
    "y_test = np.equal(y_test, wake_word_index).astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. ... 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# Peek at labels after conversion\n",
    "print(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9788812197383332\n",
      "0.021118780261666803\n"
     ]
    }
   ],
   "source": [
    "# What percentage of 'stop' appear in validation labels\n",
    "print(sum(y_val) / len(y_val))\n",
    "print(1 - sum(y_val) / len(y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77491, 16, 16)\n"
     ]
    }
   ],
   "source": [
    "# View the dimensions of our input data\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77491, 16, 16, 1)\n",
      "(9707, 16, 16, 1)\n",
      "(9658, 16, 16, 1)\n"
     ]
    }
   ],
   "source": [
    "# CNN for TF expects (batch, height, width, channels)\n",
    "# So we reshape the input tensors with a \"color\" channel of 1\n",
    "x_train = x_train.reshape(x_train.shape[0], \n",
    "                          x_train.shape[1], \n",
    "                          x_train.shape[2], \n",
    "                          1)\n",
    "x_val = x_val.reshape(x_val.shape[0], \n",
    "                      x_val.shape[1], \n",
    "                      x_val.shape[2], \n",
    "                      1)\n",
    "x_test = x_test.reshape(x_test.shape[0], \n",
    "                        x_test.shape[1], \n",
    "                        x_test.shape[2], \n",
    "                        1)\n",
    "print(x_train.shape)\n",
    "print(x_val.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 16, 1)\n"
     ]
    }
   ],
   "source": [
    "# Input shape for CNN is size of MFCC of 1 sample\n",
    "sample_shape = x_test.shape[1:]\n",
    "print(sample_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build model\n",
    "# Based on: https://www.geeksforgeeks.org/python-image-classification-using-keras/\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, \n",
    "                        (2, 2), \n",
    "                        activation='relu',\n",
    "                        input_shape=sample_shape))\n",
    "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(layers.Conv2D(32, (2, 2), activation='relu'))\n",
    "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(layers.Conv2D(64, (2, 2), activation='relu'))\n",
    "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# Classifier\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 15, 15, 32)        160       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 7, 7, 32)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 6, 6, 32)          4128      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 3, 3, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 2, 2, 64)          8256      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 1, 1, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16,769\n",
      "Trainable params: 16,769\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Display model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add training parameters to model\n",
    "model.compile(loss='binary_crossentropy', \n",
    "              optimizer='rmsprop', \n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0518 - acc: 0.9858 - val_loss: 0.0556 - val_acc: 0.9783\n",
      "Epoch 2/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0248 - acc: 0.9931 - val_loss: 0.0379 - val_acc: 0.9896\n",
      "Epoch 3/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0219 - acc: 0.9941 - val_loss: 0.0178 - val_acc: 0.9957\n",
      "Epoch 4/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0208 - acc: 0.9947 - val_loss: 0.0188 - val_acc: 0.9948\n",
      "Epoch 5/30\n",
      "775/775 [==============================] - 11s 14ms/step - loss: 0.0203 - acc: 0.9952 - val_loss: 0.0161 - val_acc: 0.9956\n",
      "Epoch 6/30\n",
      "775/775 [==============================] - 10s 13ms/step - loss: 0.0215 - acc: 0.9949 - val_loss: 0.0197 - val_acc: 0.9954\n",
      "Epoch 7/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0216 - acc: 0.9951 - val_loss: 0.0239 - val_acc: 0.9958\n",
      "Epoch 8/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0236 - acc: 0.9943 - val_loss: 0.0446 - val_acc: 0.9894\n",
      "Epoch 9/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0230 - acc: 0.9948 - val_loss: 0.0538 - val_acc: 0.9910\n",
      "Epoch 10/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0244 - acc: 0.9949 - val_loss: 0.0248 - val_acc: 0.9954\n",
      "Epoch 11/30\n",
      "775/775 [==============================] - 8s 11ms/step - loss: 0.0250 - acc: 0.9948 - val_loss: 0.0330 - val_acc: 0.9944\n",
      "Epoch 12/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0249 - acc: 0.9947 - val_loss: 0.0259 - val_acc: 0.9957\n",
      "Epoch 13/30\n",
      "775/775 [==============================] - 10s 13ms/step - loss: 0.0237 - acc: 0.9952 - val_loss: 0.0234 - val_acc: 0.9952\n",
      "Epoch 14/30\n",
      "775/775 [==============================] - 9s 12ms/step - loss: 0.0239 - acc: 0.9950 - val_loss: 0.0336 - val_acc: 0.9922\n",
      "Epoch 15/30\n",
      "775/775 [==============================] - 8s 11ms/step - loss: 0.0249 - acc: 0.9948 - val_loss: 0.0328 - val_acc: 0.9957\n",
      "Epoch 16/30\n",
      "775/775 [==============================] - 10s 13ms/step - loss: 0.0245 - acc: 0.9947 - val_loss: 0.0786 - val_acc: 0.9877\n",
      "Epoch 17/30\n",
      "775/775 [==============================] - 8s 11ms/step - loss: 0.0248 - acc: 0.9950 - val_loss: 0.0309 - val_acc: 0.9945\n",
      "Epoch 18/30\n",
      "775/775 [==============================] - 10s 13ms/step - loss: 0.0267 - acc: 0.9951 - val_loss: 0.0423 - val_acc: 0.9913\n",
      "Epoch 19/30\n",
      "775/775 [==============================] - 9s 12ms/step - loss: 0.0275 - acc: 0.9953 - val_loss: 0.0251 - val_acc: 0.9961\n",
      "Epoch 20/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0259 - acc: 0.9951 - val_loss: 0.0373 - val_acc: 0.9954\n",
      "Epoch 21/30\n",
      "775/775 [==============================] - 8s 10ms/step - loss: 0.0254 - acc: 0.9950 - val_loss: 0.0405 - val_acc: 0.9959\n",
      "Epoch 22/30\n",
      "775/775 [==============================] - 8s 11ms/step - loss: 0.0253 - acc: 0.9951 - val_loss: 0.0384 - val_acc: 0.9946\n",
      "Epoch 23/30\n",
      "775/775 [==============================] - 8s 10ms/step - loss: 0.0265 - acc: 0.9953 - val_loss: 0.0347 - val_acc: 0.9926\n",
      "Epoch 24/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0261 - acc: 0.9952 - val_loss: 0.0262 - val_acc: 0.9954\n",
      "Epoch 25/30\n",
      "775/775 [==============================] - 8s 11ms/step - loss: 0.0243 - acc: 0.9952 - val_loss: 0.0196 - val_acc: 0.9961\n",
      "Epoch 26/30\n",
      "775/775 [==============================] - 9s 12ms/step - loss: 0.0241 - acc: 0.9954 - val_loss: 0.0216 - val_acc: 0.9956\n",
      "Epoch 27/30\n",
      "775/775 [==============================] - 8s 10ms/step - loss: 0.0260 - acc: 0.9952 - val_loss: 0.0283 - val_acc: 0.9948\n",
      "Epoch 28/30\n",
      "775/775 [==============================] - 9s 12ms/step - loss: 0.0254 - acc: 0.9947 - val_loss: 0.0300 - val_acc: 0.9954\n",
      "Epoch 29/30\n",
      "775/775 [==============================] - 9s 11ms/step - loss: 0.0302 - acc: 0.9947 - val_loss: 0.0280 - val_acc: 0.9958\n",
      "Epoch 30/30\n",
      "775/775 [==============================] - 8s 10ms/step - loss: 0.0238 - acc: 0.9954 - val_loss: 0.0254 - val_acc: 0.9955\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "history = model.fit(x_train, \n",
    "                    y_train, \n",
    "                    epochs=30, \n",
    "                    batch_size=100, \n",
    "                    validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model as a file\n",
    "models.save_model(model, model_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# See which are 'stop'\n",
    "for idx, y in enumerate(y_test):\n",
    "    if y == 1:\n",
    "        print(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 76ms/step\n",
      "Answer: 1.0  Prediction: [[0.9987285]]\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "Answer: 1.0  Prediction: [[1.]]\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "Answer: 1.0  Prediction: [[1.]]\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "Answer: 1.0  Prediction: [[1.]]\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "Answer: 1.0  Prediction: [[1.]]\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "Answer: 1.0  Prediction: [[0.9999791]]\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "Answer: 1.0  Prediction: [[0.99993336]]\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "Answer: 1.0  Prediction: [[1.]]\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "Answer: 1.0  Prediction: [[0.9999999]]\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "Answer: 1.0  Prediction: [[1.]]\n"
     ]
    }
   ],
   "source": [
    "# TEST: Load model and run it against test set\n",
    "model = models.load_model(model_filename)\n",
    "for i in range(100, 110):\n",
    "    print('Answer:', y_test[i], ' Prediction:', model.predict(np.expand_dims(x_test[i], 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "302/302 [==============================] - 1s 2ms/step - loss: 0.0224 - acc: 0.9961\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.022379912436008453, 0.9960654377937317]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate model with test set\n",
    "model.evaluate(x=x_test, y=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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