urban - rural image classifier

urban-rural-classification.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# kaggle challenge - https://www.kaggle.com/dansbecker/urban-and-rural-photos/home\n",
+    "from tensorflow.python.keras.applications import ResNet50\n",
+    "from tensorflow.python.keras.models import Sequential\n",
+    "from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout\n",
+    "\n",
+    "num_classes = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(ResNet50(include_top=False, pooling='avg'))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dense(num_classes, activation='softmax'))\n",
+    "\n",
+    "model.layers[0].trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 72 images belonging to 2 classes.\n",
+      "Found 20 images belonging to 2 classes.\n",
+      "Epoch 1/3\n",
+      "3/3 [==============================] - 22s 7s/step - loss: 0.6569 - acc: 0.6389 - val_loss: 0.5272 - val_acc: 0.7500\n",
+      "Epoch 2/3\n",
+      "3/3 [==============================] - 20s 7s/step - loss: 0.3589 - acc: 0.8750 - val_loss: 0.3867 - val_acc: 0.9500\n",
+      "Epoch 3/3\n",
+      "3/3 [==============================] - 20s 7s/step - loss: 0.2804 - acc: 0.9444 - val_loss: 0.2924 - val_acc: 0.9500\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1a198127588>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tensorflow.python.keras.applications.resnet50 import preprocess_input\n",
+    "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "image_size = 224\n",
+    "data_generator = ImageDataGenerator(\n",
+    "    preprocessing_function=preprocess_input,\n",
+    "    horizontal_flip=True,\n",
+    "    width_shift_range = 0.2,\n",
+    "    height_shift_range = 0.2\n",
+    ")\n",
+    "\n",
+    "train_generator = data_generator.flow_from_directory(\n",
+    "    'data/rural_and_urban_photos/train',\n",
+    "    target_size=(image_size, image_size),\n",
+    "    batch_size=24,\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "validation_generator = data_generator.flow_from_directory(\n",
+    "    'data/rural_and_urban_photos/val',\n",
+    "    target_size=(image_size, image_size),\n",
+    "    class_mode='categorical'\n",
+    ")\n",
+    "\n",
+    "model.fit_generator(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch=3,\n",
+    "    validation_data=validation_generator,\n",
+    "    validation_steps=1,\n",
+    "    epochs=3\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:nnseries]",
+   "language": "python",
+   "name": "conda-env-nnseries-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}