2.2_train_gender_model.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1NUKFYqE-NM_odjfgsbvqScWl3PBVVefv","timestamp":1641452297053}],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["## Plan of Action\n","\n","*   **UTK Face Dataset** images have labels embedded in their name, as per the following nomenclature:\n"," * [age]\\_[gender]\\_[race]\\_[time].jpg\n","* **Import Gender Dataset** from Drive and **read gender labels** from file name\n","* Convert all images to **grayscale**\n","* Using **X epochs** on our **defined CNN Architecture**, comprising of:\n","    - an input *Conv2D* layer (with 32 filters) paired with an *MaxPooling2D* layer,\n","    - 3 pairs of *Conv2D* (with 64, 128 & 256 filters) and *MaxPooling2D* layers,\n","    - 1 *Dense* layer with 128 nodes, and\n","    - an output *Dense* layer with 3 nodes."],"metadata":{"id":"UIdtXzi6ct4Z"}},{"cell_type":"markdown","metadata":{"id":"1XRMQvEV6SoR"},"source":["## Mount Google Drive"]},{"cell_type":"code","metadata":{"id":"q9dSqCFGLtZN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723371902512,"user_tz":-330,"elapsed":28487,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"11aecf37-2429-4f1c-a91b-4d93fe60af17"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"id":"EMyWraGEvA6u","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723371905907,"user_tz":-330,"elapsed":611,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"0fbdacbc-c43d-4d6d-e6ea-fd0f44d1458c"},"source":["%cd /content/drive/MyDrive/Project5_AgeGenderEmotion_Detection/1.2_gender_input_output\n","! ls"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Project5_AgeGenderEmotion_Detection/1.2_gender_input_output\n","input  output\n"]}]},{"cell_type":"markdown","metadata":{"id":"AMmC9XTg6Xeq"},"source":["## Import Libraries/ Functions"]},{"cell_type":"code","source":["# Imports\n","\n","import pandas as pd\n","import numpy as np\n","\n","import matplotlib.pyplot as plt\n","import cv2\n","import os\n","\n","import tensorflow as tf\n","\n","from google.colab.patches import cv2_imshow\n","import matplotlib.pyplot as plt\n","\n","from keras.models import Sequential,load_model,Model\n","from keras.layers import Conv2D,MaxPooling2D,AvgPool2D,GlobalAveragePooling2D,Dense,Dropout,BatchNormalization,Flatten,Input\n","from sklearn.model_selection import train_test_split\n","\n","\n","from tensorflow.keras.layers import Input,Activation,Add\n","from tensorflow.keras.regularizers import l2\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import ModelCheckpoint"],"metadata":{"id":"Ennv5gyfOxX8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ze8LWdvx6bHI"},"source":["## Import Dataset"]},{"cell_type":"code","metadata":{"id":"li2Nlfi-vNQy"},"source":["# Ref:  https://github.com/ChibaniMohamed/cnn_age_gender/blob/main/train.py\n","\n","path = \"./input/UTKFace\"\n","pixels = []\n","age = []\n","gender = []\n","\n","i=0\n","for img in os.listdir(path):\n","  i=i+1\n","  genders = img.split(\"_\")[1]\n","  img = cv2.imread(str(path)+\"/\"+str(img))\n","  img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n","  img=cv2.resize(img,(100,100))\n","  pixels.append(np.array(img))\n","  gender.append(np.array(genders))\n","\n","pixels = np.array(pixels)\n","gender = np.array(gender,np.uint64)"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(pixels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RKdKGqpwzxOt","executionInfo":{"status":"ok","timestamp":1723372077760,"user_tz":-330,"elapsed":489,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"5fe75657-a7a2-41b9-99d7-bfb4db4d2b7f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["4765"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["len(gender)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NO4NmL3a6adk","executionInfo":{"status":"ok","timestamp":1723372079883,"user_tz":-330,"elapsed":6,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"0778f351-fbe4-40b6-8b54-60f780c89bf6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["4765"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["## Split into Train/ Test"],"metadata":{"id":"GXv27o_rqB-c"}},{"cell_type":"code","source":["x_train,x_test,y_train,y_test = train_test_split(pixels,gender,random_state=100)"],"metadata":{"id":"YPo5xu_aqA1a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(x_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XhQNSqH54edd","executionInfo":{"status":"ok","timestamp":1723372090156,"user_tz":-330,"elapsed":516,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"cea1c5e3-c110-4a6b-aa81-b00af88a7f5a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3573"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["len(x_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBRtEkL84xgN","executionInfo":{"status":"ok","timestamp":1723372092208,"user_tz":-330,"elapsed":6,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"f1c00652-3d12-49a9-fe60-44f9fb8498a8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1192"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"Ie6tMjtN6fUr"},"source":["## Define CNN Architecture"]},{"cell_type":"code","source":["input = Input(shape = (100,100,1))\n","\n","conv1 = Conv2D(32,(3, 3), padding = 'same', strides=(1, 1), kernel_regularizer=l2(0.001))(input)\n","conv1 = Dropout(0.1)(conv1)\n","conv1 = Activation('relu')(conv1)\n","pool1 = MaxPooling2D(pool_size = (2,2)) (conv1)\n","\n","conv2 = Conv2D(64,(3, 3), padding = 'same', strides=(1, 1), kernel_regularizer=l2(0.001))(pool1)\n","conv2 = Dropout(0.1)(conv2)\n","conv2 = Activation('relu')(conv2)\n","pool2 = MaxPooling2D(pool_size = (2,2)) (conv2)\n","\n","conv3 = Conv2D(128,(3, 3), padding = 'same', strides=(1, 1), kernel_regularizer=l2(0.001))(pool2)\n","conv3 = Dropout(0.1)(conv3)\n","conv3 = Activation('relu')(conv3)\n","pool3 = MaxPooling2D(pool_size = (2,2)) (conv3)\n","\n","conv4 = Conv2D(256,(3, 3), padding = 'same', strides=(1, 1), kernel_regularizer=l2(0.001))(pool3)\n","conv4 = Dropout(0.1)(conv4)\n","conv4 = Activation('relu')(conv4)\n","pool4 = MaxPooling2D(pool_size = (2,2)) (conv4)\n","\n","flatten = Flatten()(pool4)\n","\n","dense_1 = Dense(128,activation='relu')(flatten)\n","\n","drop_1 = Dropout(0.2)(dense_1)\n","\n","output = Dense(2,activation=\"sigmoid\")(drop_1)"],"metadata":{"id":"a7SedAakhxYn"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UPZTCwG9vY3T","colab":{"base_uri":"https://localhost:8080/","height":801},"executionInfo":{"status":"ok","timestamp":1723372173448,"user_tz":-330,"elapsed":502,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"6bf492ba-8837-4ba5-abcd-88bd701327af"},"source":["model = Model(inputs=input,outputs=output)\n","model.compile(optimizer=\"adam\",loss=[\"sparse_categorical_crossentropy\"],metrics=['accuracy'])\n","model.summary()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"functional_1\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m320\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation (\u001b[38;5;33mActivation\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9216\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │       \u001b[38;5;34m1,179,776\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)                   │             \u001b[38;5;34m258\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9216</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,179,776</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">258</span> │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,567,874\u001b[0m (5.98 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,567,874</span> (5.98 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,567,874\u001b[0m (5.98 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,567,874</span> (5.98 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["model_path='./output/gender_model.keras'  # Change the file extension to '.keras'\n","checkpointer = ModelCheckpoint(model_path, monitor='loss',verbose=1,save_best_only=True,\n","                               save_weights_only=False, mode='auto',save_freq='epoch')\n","callback_list=[checkpointer]"],"metadata":{"id":"g1mNgkXQ_jxF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model_path='./output/gender_model.h5'\n","checkpointer = ModelCheckpoint(model_path, monitor='loss',verbose=1,save_best_only=True,\n","                               save_weights_only=False, mode='auto',save_freq='epoch')\n","callback_list=[checkpointer]"],"metadata":{"id":"K-U0OB-_oAH_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DKpJOxpA6lvO"},"source":["## Model Training"]},{"cell_type":"code","metadata":{"id":"dpf9OUqZvanW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1d73d82f-fed9-4592-b3d0-912909a7c958","executionInfo":{"status":"ok","timestamp":1723372533308,"user_tz":-330,"elapsed":106393,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}}},"source":["save = model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=50,callbacks=[callback_list])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9692 - loss: 0.2173\n","Epoch 1: loss did not improve from 0.21122\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9690 - loss: 0.2180 - val_accuracy: 0.8633 - val_loss: 0.5125\n","Epoch 2/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9644 - loss: 0.2268\n","Epoch 2: loss did not improve from 0.21122\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - accuracy: 0.9644 - loss: 0.2271 - val_accuracy: 0.8490 - val_loss: 0.6692\n","Epoch 3/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9591 - loss: 0.2452\n","Epoch 3: loss did not improve from 0.21122\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9592 - loss: 0.2445 - val_accuracy: 0.8532 - val_loss: 0.7433\n","Epoch 4/50\n","\u001b[1m110/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9705 - loss: 0.2046\n","Epoch 4: loss did not improve from 0.21122\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9705 - loss: 0.2048 - val_accuracy: 0.8473 - val_loss: 0.6015\n","Epoch 5/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9757 - loss: 0.2097\n","Epoch 5: loss did not improve from 0.21122\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.9754 - loss: 0.2102 - val_accuracy: 0.8440 - val_loss: 0.5004\n","Epoch 6/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9646 - loss: 0.2209\n","Epoch 6: loss did not improve from 0.21122\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.9646 - loss: 0.2210 - val_accuracy: 0.8624 - val_loss: 0.5340\n","Epoch 7/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9734 - loss: 0.2067\n","Epoch 7: loss improved from 0.21122 to 0.20649, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - accuracy: 0.9733 - loss: 0.2067 - val_accuracy: 0.8565 - val_loss: 0.5374\n","Epoch 8/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9775 - loss: 0.1868\n","Epoch 8: loss improved from 0.20649 to 0.19598, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 18ms/step - accuracy: 0.9774 - loss: 0.1871 - val_accuracy: 0.8523 - val_loss: 0.6178\n","Epoch 9/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9756 - loss: 0.2060\n","Epoch 9: loss did not improve from 0.19598\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9755 - loss: 0.2061 - val_accuracy: 0.8532 - val_loss: 0.5583\n","Epoch 10/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9719 - loss: 0.1952\n","Epoch 10: loss improved from 0.19598 to 0.19544, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 16ms/step - accuracy: 0.9719 - loss: 0.1952 - val_accuracy: 0.8658 - val_loss: 0.5774\n","Epoch 11/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9729 - loss: 0.1971\n","Epoch 11: loss did not improve from 0.19544\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9727 - loss: 0.1974 - val_accuracy: 0.8683 - val_loss: 0.5431\n","Epoch 12/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9752 - loss: 0.1869\n","Epoch 12: loss improved from 0.19544 to 0.19015, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - accuracy: 0.9752 - loss: 0.1871 - val_accuracy: 0.8523 - val_loss: 0.5431\n","Epoch 13/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9666 - loss: 0.1999\n","Epoch 13: loss did not improve from 0.19015\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9668 - loss: 0.1997 - val_accuracy: 0.8557 - val_loss: 0.6005\n","Epoch 14/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9778 - loss: 0.1906\n","Epoch 14: loss improved from 0.19015 to 0.18878, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - accuracy: 0.9778 - loss: 0.1906 - val_accuracy: 0.8666 - val_loss: 0.5394\n","Epoch 15/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9791 - loss: 0.1699\n","Epoch 15: loss improved from 0.18878 to 0.18071, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.9790 - loss: 0.1703 - val_accuracy: 0.8314 - val_loss: 0.6555\n","Epoch 16/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9752 - loss: 0.2009\n","Epoch 16: loss did not improve from 0.18071\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9751 - loss: 0.2010 - val_accuracy: 0.8465 - val_loss: 0.5384\n","Epoch 17/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9850 - loss: 0.1584\n","Epoch 17: loss improved from 0.18071 to 0.17043, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 16ms/step - accuracy: 0.9848 - loss: 0.1588 - val_accuracy: 0.8230 - val_loss: 0.6560\n","Epoch 18/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9676 - loss: 0.1953\n","Epoch 18: loss did not improve from 0.17043\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9678 - loss: 0.1950 - val_accuracy: 0.8565 - val_loss: 0.5366\n","Epoch 19/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9828 - loss: 0.1541\n","Epoch 19: loss did not improve from 0.17043\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9827 - loss: 0.1547 - val_accuracy: 0.8700 - val_loss: 0.5040\n","Epoch 20/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9834 - loss: 0.1524\n","Epoch 20: loss improved from 0.17043 to 0.15675, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - accuracy: 0.9834 - loss: 0.1526 - val_accuracy: 0.8649 - val_loss: 0.5819\n","Epoch 21/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9820 - loss: 0.1585\n","Epoch 21: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9818 - loss: 0.1590 - val_accuracy: 0.8607 - val_loss: 0.5704\n","Epoch 22/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9799 - loss: 0.1612\n","Epoch 22: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - accuracy: 0.9798 - loss: 0.1613 - val_accuracy: 0.8507 - val_loss: 0.5480\n","Epoch 23/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9805 - loss: 0.1672\n","Epoch 23: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.1673 - val_accuracy: 0.8557 - val_loss: 0.5597\n","Epoch 24/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9794 - loss: 0.1669\n","Epoch 24: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9795 - loss: 0.1666 - val_accuracy: 0.8549 - val_loss: 0.5890\n","Epoch 25/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9769 - loss: 0.1749\n","Epoch 25: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.9768 - loss: 0.1750 - val_accuracy: 0.8565 - val_loss: 0.5562\n","Epoch 26/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9767 - loss: 0.1635\n","Epoch 26: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.9767 - loss: 0.1634 - val_accuracy: 0.8633 - val_loss: 0.6024\n","Epoch 27/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9686 - loss: 0.1768\n","Epoch 27: loss did not improve from 0.15675\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - accuracy: 0.9689 - loss: 0.1764 - val_accuracy: 0.8591 - val_loss: 0.6486\n","Epoch 28/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9859 - loss: 0.1404\n","Epoch 28: loss improved from 0.15675 to 0.14814, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 18ms/step - accuracy: 0.9858 - loss: 0.1407 - val_accuracy: 0.8515 - val_loss: 0.6936\n","Epoch 29/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9754 - loss: 0.1814\n","Epoch 29: loss did not improve from 0.14814\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9755 - loss: 0.1813 - val_accuracy: 0.8565 - val_loss: 0.5635\n","Epoch 30/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9664 - loss: 0.1906\n","Epoch 30: loss did not improve from 0.14814\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9666 - loss: 0.1903 - val_accuracy: 0.8574 - val_loss: 0.5472\n","Epoch 31/50\n","\u001b[1m110/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9899 - loss: 0.1401\n","Epoch 31: loss improved from 0.14814 to 0.14189, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 16ms/step - accuracy: 0.9898 - loss: 0.1401 - val_accuracy: 0.8591 - val_loss: 0.5642\n","Epoch 32/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9826 - loss: 0.1533\n","Epoch 32: loss did not improve from 0.14189\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9825 - loss: 0.1537 - val_accuracy: 0.8582 - val_loss: 0.5611\n","Epoch 33/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9836 - loss: 0.1585\n","Epoch 33: loss did not improve from 0.14189\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.9836 - loss: 0.1584 - val_accuracy: 0.8607 - val_loss: 0.5359\n","Epoch 34/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9895 - loss: 0.1291\n","Epoch 34: loss improved from 0.14189 to 0.13637, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 19ms/step - accuracy: 0.9894 - loss: 0.1294 - val_accuracy: 0.8372 - val_loss: 0.6368\n","Epoch 35/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9805 - loss: 0.1494\n","Epoch 35: loss did not improve from 0.13637\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9805 - loss: 0.1492 - val_accuracy: 0.8515 - val_loss: 0.6538\n","Epoch 36/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9840 - loss: 0.1453\n","Epoch 36: loss improved from 0.13637 to 0.13211, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.9841 - loss: 0.1449 - val_accuracy: 0.8624 - val_loss: 0.7699\n","Epoch 37/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9786 - loss: 0.1554\n","Epoch 37: loss did not improve from 0.13211\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9786 - loss: 0.1552 - val_accuracy: 0.8456 - val_loss: 0.6468\n","Epoch 38/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9776 - loss: 0.1650\n","Epoch 38: loss did not improve from 0.13211\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9775 - loss: 0.1651 - val_accuracy: 0.8725 - val_loss: 0.5235\n","Epoch 39/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9852 - loss: 0.1426\n","Epoch 39: loss did not improve from 0.13211\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9851 - loss: 0.1429 - val_accuracy: 0.8574 - val_loss: 0.5412\n","Epoch 40/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9865 - loss: 0.1312\n","Epoch 40: loss did not improve from 0.13211\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - accuracy: 0.9863 - loss: 0.1315 - val_accuracy: 0.8842 - val_loss: 0.4958\n","Epoch 41/50\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9840 - loss: 0.1517\n","Epoch 41: loss did not improve from 0.13211\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9840 - loss: 0.1517 - val_accuracy: 0.8742 - val_loss: 0.5989\n","Epoch 42/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9891 - loss: 0.1252\n","Epoch 42: loss improved from 0.13211 to 0.12886, saving model to ./output/gender_model.keras\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.9891 - loss: 0.1254 - val_accuracy: 0.8658 - val_loss: 0.5418\n","Epoch 43/50\n","\u001b[1m110/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9856 - loss: 0.1231\n","Epoch 43: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9855 - loss: 0.1232 - val_accuracy: 0.8599 - val_loss: 0.5712\n","Epoch 44/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9790 - loss: 0.1522\n","Epoch 44: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9791 - loss: 0.1517 - val_accuracy: 0.8742 - val_loss: 0.6923\n","Epoch 45/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9756 - loss: 0.1616\n","Epoch 45: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - accuracy: 0.9756 - loss: 0.1613 - val_accuracy: 0.8733 - val_loss: 0.5381\n","Epoch 46/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9825 - loss: 0.1441\n","Epoch 46: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 16ms/step - accuracy: 0.9824 - loss: 0.1443 - val_accuracy: 0.8674 - val_loss: 0.5723\n","Epoch 47/50\n","\u001b[1m111/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9892 - loss: 0.1269\n","Epoch 47: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - accuracy: 0.9891 - loss: 0.1270 - val_accuracy: 0.8574 - val_loss: 0.5674\n","Epoch 48/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9788 - loss: 0.1435\n","Epoch 48: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.9788 - loss: 0.1435 - val_accuracy: 0.8607 - val_loss: 0.5816\n","Epoch 49/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9829 - loss: 0.1321\n","Epoch 49: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9828 - loss: 0.1323 - val_accuracy: 0.8683 - val_loss: 0.6208\n","Epoch 50/50\n","\u001b[1m109/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9791 - loss: 0.1468\n","Epoch 50: loss did not improve from 0.12886\n","\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9790 - loss: 0.1474 - val_accuracy: 0.8624 - val_loss: 0.5028\n"]}]},{"cell_type":"code","source":["# Checking the train and test loss and accuracy values from the neural network above.\n","\n","train_loss = save.history['loss']\n","test_loss = save.history['val_loss']\n","train_accuracy = save.history['accuracy']\n","test_accuracy = save.history['val_accuracy']"],"metadata":{"id":"0dPotRzT8XeV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Plotting a line chart to visualize the loss and accuracy values by epochs.\n","\n","fig, ax = plt.subplots(ncols=2, figsize=(15,7))\n","\n","ax = ax.ravel()\n","\n","ax[0].plot(train_loss, label='Train Loss', color='royalblue', marker='o', markersize=5)\n","ax[0].plot(test_loss, label='Test Loss', color = 'orangered', marker='o', markersize=5)\n","\n","ax[0].set_xlabel('Epochs', fontsize=14)\n","ax[0].set_ylabel('Categorical Crossentropy', fontsize=14)\n","\n","ax[0].legend(fontsize=14)\n","ax[0].tick_params(axis='both', labelsize=12)\n","\n","ax[1].plot(train_accuracy, label='Train Accuracy', color='royalblue', marker='o', markersize=5)\n","ax[1].plot(test_accuracy, label='Test Accuracy', color='orangered', marker='o', markersize=5)\n","\n","ax[1].set_xlabel('Epochs', fontsize=14)\n","ax[1].set_ylabel('Accuracy', fontsize=14)\n","\n","ax[1].legend(fontsize=14)\n","ax[1].tick_params(axis='both', labelsize=12)\n","\n","fig.suptitle(x=0.5, y=0.92, t=\"Lineplots showing loss and accuracy of CNN model by epochs\", fontsize=16)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":606},"id":"QLiMy79W8ip0","executionInfo":{"status":"ok","timestamp":1723372598184,"user_tz":-330,"elapsed":1540,"user":{"displayName":"Mahaveer Chandel","userId":"05057949728483692197"}},"outputId":"f20a5b75-cc7e-47c3-b932-53f800d55135"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0.92, 'Lineplots showing loss and accuracy of CNN model by epochs')"]},"metadata":{},"execution_count":24},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x700 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}