ConvNet_Masks.ipynb

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    {
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     "text": "Epoch 1/20\n32/32 [==============================] - 4s 113ms/step - loss: 1.5185 - accuracy: 0.5591 - val_loss: 0.6315 - val_accuracy: 0.6703\nEpoch 2/20\n32/32 [==============================] - 3s 94ms/step - loss: 0.5501 - accuracy: 0.7182 - val_loss: 0.4729 - val_accuracy: 0.8007\nEpoch 3/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.3916 - accuracy: 0.8291 - val_loss: 0.3338 - val_accuracy: 0.8659\nEpoch 4/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.2814 - accuracy: 0.8791 - val_loss: 0.2089 - val_accuracy: 0.9384\nEpoch 5/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.2281 - accuracy: 0.9155 - val_loss: 0.1937 - val_accuracy: 0.9384\nEpoch 6/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.1519 - accuracy: 0.9455 - val_loss: 0.2561 - val_accuracy: 0.8986\nEpoch 7/20\n32/32 [==============================] - 3s 94ms/step - loss: 0.1392 - accuracy: 0.9500 - val_loss: 0.1465 - val_accuracy: 0.9457\nEpoch 8/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.0996 - accuracy: 0.9700 - val_loss: 0.1099 - val_accuracy: 0.9565\nEpoch 9/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.0637 - accuracy: 0.9818 - val_loss: 0.1129 - val_accuracy: 0.9601\nEpoch 10/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.0547 - accuracy: 0.9827 - val_loss: 0.0949 - val_accuracy: 0.9783\nEpoch 11/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.0355 - accuracy: 0.9873 - val_loss: 0.0732 - val_accuracy: 0.9638\nEpoch 12/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.0290 - accuracy: 0.9900 - val_loss: 0.1318 - val_accuracy: 0.9420\nEpoch 13/20\n32/32 [==============================] - 3s 94ms/step - loss: 0.0751 - accuracy: 0.9673 - val_loss: 0.0945 - val_accuracy: 0.9746\nEpoch 14/20\n32/32 [==============================] - 3s 94ms/step - loss: 0.0474 - accuracy: 0.9864 - val_loss: 0.1193 - val_accuracy: 0.9529\nEpoch 15/20\n32/32 [==============================] - 3s 93ms/step - loss: 0.0740 - accuracy: 0.9791 - val_loss: 0.1001 - val_accuracy: 0.9746\nEpoch 16/20\n32/32 [==============================] - 3s 95ms/step - loss: 0.0236 - accuracy: 0.9918 - val_loss: 0.1273 - val_accuracy: 0.9710\nEpoch 17/20\n32/32 [==============================] - 3s 95ms/step - loss: 0.0257 - accuracy: 0.9909 - val_loss: 0.0796 - val_accuracy: 0.9783\nEpoch 18/20\n32/32 [==============================] - 3s 95ms/step - loss: 0.0247 - accuracy: 0.9900 - val_loss: 0.0776 - val_accuracy: 0.9746\nEpoch 19/20\n32/32 [==============================] - 3s 94ms/step - loss: 0.0145 - accuracy: 0.9955 - val_loss: 0.0930 - val_accuracy: 0.9710\nEpoch 20/20\n32/32 [==============================] - 3s 94ms/step - loss: 0.0276 - accuracy: 0.9891 - val_loss: 0.0787 - val_accuracy: 0.9746\n"
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D\n",
    "from tensorflow.python.client import device_lib\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "import numpy as np\n",
    "\n",
    "# print(device_lib.list_local_devices())\n",
    "# print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n",
    "\n",
    "X = np.load('Mask_features_v2.npy')\n",
    "y = np.load('Mask_labels_v2.npy')\n",
    "\n",
    "# normalize data, I think this means to make all the values in the tensor between 0-1\n",
    "# since its an image the highest pixel value can only be 255 hence why we divide it\n",
    "X = X/255.0\n",
    "\n",
    "model = Sequential()\n",
    "# 1st type of layer (64 units or 64 neurons)\n",
    "# 2nd kernel size (window size)\n",
    "# 3rd shape of image. In x it is represented as (X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)) we use [1:] so skip the -1 and take everything after which is the 50X50 grey\n",
    "model.add(Conv2D(100,(3,3), input_shape = X.shape[1:]))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(200,(3,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "# final layer takes in flatten image\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(500))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "# output layer\n",
    "# softmax probability distribution \n",
    "model.add(Dense(2))\n",
    "model.add(Activation(\"softmax\"))\n",
    "\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# train_data,test_data,train_target,test_target=train_test_split(X, y,test_size=0.1)\n",
    "\n",
    "# how much data we passing in at once\n",
    "history = model.fit(X, y, batch_size = 35, epochs=20, validation_split = 0.2)"
   ]
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   "cell_type": "code",
   "execution_count": 2,
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     "text": "WARNING:tensorflow:From C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:1813: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\nInstructions for updating:\nIf using Keras pass *_constraint arguments to layers.\nINFO:tensorflow:Assets written to: mask_detection_model_v8\\assets\n"
    }
   ],
   "source": [
    "# saving model\n",
    "model.save('mask_detection_model_v8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(history.history['loss'],'r',label='training loss')\n",
    "plt.plot(history.history['val_loss'],label='validation loss')\n",
    "plt.xlabel('# epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'],'r',label='training accuracy')\n",
    "plt.plot(history.history['val_accuracy'],label='validation accuracy')\n",
    "plt.xlabel('# epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38264bit6d69dc1561304dfe9826749846f97dc6",
   "display_name": "Python 3.8.2 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}