SW-Wiktor-Bombola/SW-Unity/Plants Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "comprehensive-talent",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from sklearn.metrics import classification_report\n",
    "import re\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "macro-michigan",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_dir=\"../Trees\"\n",
    "validation_data_dir=\"../Trees\"\n",
    "batch_size=14\n",
    "img_height, img_width = 60,80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "defined-briefing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 428 images belonging to 3 classes.\n",
      "Found 105 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    validation_split=0.2) # set validation split\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical',\n",
    "    subset='training') # set as training data\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    validation_data_dir, # same directory as training data\n",
    "    target_size=(img_height, img_width),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical',\n",
    "    subset='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "natural-cutting",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "conservative-hypothetical",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(32, (3,3), activation='relu', input_shape=(60, 80, 3)))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(MaxPooling2D((2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "\n",
    "model.add(Dense(3, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "irish-monitoring",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pydot in c:\\program files\\python39\\lib\\site-packages (1.4.2)\n",
      "Requirement already satisfied: pyparsing>=2.1.4 in c:\\users\\wbloc\\appdata\\roaming\\python\\python39\\site-packages (from pydot) (2.4.7)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: graphviz in c:\\program files\\python39\\lib\\site-packages (0.19.1)\n",
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model \n",
    "!pip install pydot\n",
    "!pip install graphviz\n",
    "plot_model(model, to_file='model1_plot.png', show_shapes=True,show_dtype=True, show_layer_names=True, expand_nested=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "illegal-zoning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 58, 78, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 29, 39, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 27, 37, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 13, 18, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 11, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 5, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 2560)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 512)               1311232   \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 3)                 1539      \n",
      "=================================================================\n",
      "Total params: 1,631,747\n",
      "Trainable params: 1,631,747\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cardiac-highland",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop(learning_rate=0.0001),\n",
    "             loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "informed-baker",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 3s 393ms/step - loss: 0.9368 - accuracy: 0.5897 - val_loss: 0.7715 - val_accuracy: 0.6786\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 2s 352ms/step - loss: 0.8682 - accuracy: 0.6310 - val_loss: 0.7431 - val_accuracy: 0.6786\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 2s 352ms/step - loss: 0.7690 - accuracy: 0.6667 - val_loss: 0.6637 - val_accuracy: 0.7143\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 2s 351ms/step - loss: 0.7816 - accuracy: 0.6310 - val_loss: 0.6656 - val_accuracy: 0.6607\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 2s 346ms/step - loss: 0.7070 - accuracy: 0.6667 - val_loss: 0.7131 - val_accuracy: 0.5893\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 2s 351ms/step - loss: 0.6677 - accuracy: 0.6905 - val_loss: 0.5662 - val_accuracy: 0.7143\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 2s 350ms/step - loss: 0.6171 - accuracy: 0.7262 - val_loss: 0.7703 - val_accuracy: 0.5893\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 2s 351ms/step - loss: 0.6001 - accuracy: 0.7738 - val_loss: 0.4881 - val_accuracy: 0.8036\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 2s 362ms/step - loss: 0.4903 - accuracy: 0.7821 - val_loss: 0.4565 - val_accuracy: 0.9107\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 2s 351ms/step - loss: 0.5107 - accuracy: 0.7976 - val_loss: 0.5301 - val_accuracy: 0.8393\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_generator, steps_per_epoch=6, epochs=10,  verbose=1,\n",
    "                   validation_data = validation_generator, validation_steps = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "inclusive-chess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0ecf5e190>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.5, 1])\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "marine-satellite",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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