gender classification with dataset added

machine_learning/Gender_Classifiacation/Gender Class.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bc968054-3bbb-4aea-acee-d3ea85b8052f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras import layers, models\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.preprocessing import image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abe0d9f2-5b25-4e25-b896-0261689c5910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dir = r\"Training_dir\"\n",
+    "val_dir = r\"Validation_dir\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72e33751-c2f7-488e-951a-356bd4eee573",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_datagen = ImageDataGenerator(rescale = 1./225)\n",
+    "val_datagen =  ImageDataGenerator(rescale = 1./225)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6f4f22c6-4d1a-4833-9edc-781704dd95ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 47009 images belonging to 2 classes.\n",
+      "Found 11649 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "    train_dir,\n",
+    "    target_size = (150, 150),\n",
+    "    batch_size = 32,\n",
+    "    class_mode = \"binary\"\n",
+    ")\n",
+    "validation_generator = val_datagen.flow_from_directory(\n",
+    "    val_dir,\n",
+    "    target_size = (150, 150),\n",
+    "    batch_size = 32,\n",
+    "    class_mode = \"binary\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d2cc1c68-4d51-445b-afd9-5f80f89353fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ombaj\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = models.Sequential([\n",
+    "    layers.Conv2D(32, (3, 3), activation = \"relu\", input_shape = (150, 150, 3)),\n",
+    "    layers.MaxPooling2D(2, 2),\n",
+    "    layers.Conv2D(64, (3,3), activation = \"relu\"),\n",
+    "    layers.MaxPooling2D(2, 2),\n",
+    "    layers.Conv2D(128, (3, 3), activation = \"relu\"),\n",
+    "    layers.MaxPooling2D(2, 2),\n",
+    "    layers.Flatten(),\n",
+    "    layers.Dense(512, activation = \"relu\"),\n",
+    "    layers.Dense(1, activation = \"sigmoid\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e2751754-9bad-40d9-ad64-09897dcd456a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:TensorFlow GPU support is not available on native Windows for TensorFlow >= 2.11. Even if CUDA/cuDNN are installed, GPU will not be used. Please use WSL2 or the TensorFlow-DirectML plugin.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(\n",
+    "    optimizer = \"adam\",\n",
+    "    loss = \"binary_crossentropy\",\n",
+    "    metrics = [\"accuracy\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bd8c8820-ef2a-40fb-aa64-463a3c81e246",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "\u001b[1m  14/1470\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m6:47\u001b[0m 280ms/step - accuracy: 0.4598 - loss: 1.3940"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "KeyboardInterrupt\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "Trained_model = model.fit(\n",
+    "    train_generator,\n",
+    "    epochs = 5,\n",
+    "    validation_data = validation_generator\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baf947b4-b5c0-441b-9fe6-fa9a7773309f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step\n",
+      "Prediction: Female\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predict_gender(path):\n",
+    "    img = image.load_img(path, target_size=(150, 150))\n",
+    "    x = image.img_to_array(img) / 255.0  \n",
+    "    x = np.expand_dims(x, axis=0)        \n",
+    "\n",
+    "    result = model.predict(x)\n",
+    "    if result[0] > 0.5:\n",
+    "        print(\"Prediction: Male\")\n",
+    "    else:\n",
+    "        print(\"Prediction: Female\")\n",
+    "\n",
+    "predict_gender(r'YOUR_IMAGE_PATH')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8fb4ec5-5c6b-4ba0-806a-a525de92529f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11 (Stable)",
+   "language": "python",
+   "name": "python311"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}