conversation_engine_for_deaf_and_duumb (3).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Neccessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:01.757542Z",
     "start_time": "2022-08-24T07:13:31.593496Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np#used for numerical analysis\n",
    "import tensorflow #open source used for both ML and DL for computation\n",
    "from tensorflow.keras.models import Sequential #it is a plain stack of layers\n",
    "from tensorflow.keras import layers #A layer consists of a tensor-in tensor-out computation function\n",
    "#Dense layer is the regular deeply connected neural network layer\n",
    "from tensorflow.keras.layers import Dense,Flatten\n",
    "#Faltten-used fot flattening the input or change the dimension\n",
    "from tensorflow.keras.layers import Convolution2D,MaxPooling2D #Convolutional layer\n",
    "#MaxPooling2D-for downsampling the image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:01.840502Z",
     "start_time": "2022-08-24T07:14:01.762546Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.8.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensorflow.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:01.897983Z",
     "start_time": "2022-08-24T07:14:01.851496Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.8.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensorflow.keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Data Agumentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:01.991377Z",
     "start_time": "2022-08-24T07:14:01.908980Z"
    }
   },
   "outputs": [],
   "source": [
    "#setting parameter for Image Data agumentation to the training data\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)\n",
    "#Image Data agumentation to the testing data\n",
    "test_datagen=ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading our data and performing data agumentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:04.621603Z",
     "start_time": "2022-08-24T07:14:02.001372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 15750 images belonging to 9 classes.\n",
      "Found 2250 images belonging to 9 classes.\n"
     ]
    }
   ],
   "source": [
    "#performing data agumentation to train data\n",
    "x_train = train_datagen.flow_from_directory(r'F:\\\\Spider\\\\Conversation_engine_for_deaf_and_dumb\\\\conversation engine-updated\\\\flask new\\\\Flask\\\\Dataset\\\\training_set',target_size=(64, 64),batch_size=32,\n",
    "                                            color_mode='rgb',class_mode='categorical')\n",
    "#performing data agumentation to test data\n",
    "x_test = test_datagen.flow_from_directory(r'F:\\\\Spider\\\\Conversation_engine_for_deaf_and_dumb\\\\conversation engine-updated\\\\flask new\\\\Flask\\\\Dataset\\\\test_set',target_size=(64, 64),batch_size=32,\n",
    "                                            color_mode='rgb',class_mode='categorical') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:04.641591Z",
     "start_time": "2022-08-24T07:14:04.626601Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8}\n"
     ]
    }
   ],
   "source": [
    "print(x_train.class_indices)#checking the number of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:04.744608Z",
     "start_time": "2022-08-24T07:14:04.647588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8}\n"
     ]
    }
   ],
   "source": [
    "print(x_test.class_indices)#checking the number of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:04.868492Z",
     "start_time": "2022-08-24T07:14:04.749606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 1750,\n",
       "         1: 1750,\n",
       "         2: 1750,\n",
       "         3: 1750,\n",
       "         4: 1750,\n",
       "         5: 1750,\n",
       "         6: 1750,\n",
       "         7: 1750,\n",
       "         8: 1750})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter as c\n",
    "c(x_train .labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:07.503971Z",
     "start_time": "2022-08-24T07:14:04.875489Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initializing the CNN\n",
    "classifier = Sequential()\n",
    "\n",
    "# First convolution layer and pooling\n",
    "classifier.add(Convolution2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))\n",
    "classifier.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "# Second convolution layer and pooling\n",
    "#classifier.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "# input_shape is going to be the pooled feature maps from the previous convolution layer\n",
    "#classifier.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# Flattening the layers\n",
    "classifier.add(Flatten())\n",
    "\n",
    "# Adding a fully connected layer\n",
    "#classifier.add(Dense(units=128,,activation='softmax'))\n",
    "classifier.add(Dense(units=9, activation='softmax')) # softmax for more than 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:07.644316Z",
     "start_time": "2022-08-24T07:14:07.507093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 62, 62, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 31, 31, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 30752)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 9)                 276777    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 277,673\n",
      "Trainable params: 277,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier.summary()#summary of our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:14:07.770136Z",
     "start_time": "2022-08-24T07:14:07.651324Z"
    }
   },
   "outputs": [],
   "source": [
    "# Compiling the CNN\n",
    "# categorical_crossentropy for more than 2\n",
    "classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:20:41.113917Z",
     "start_time": "2022-08-24T07:14:07.777132Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SHARAN\\AppData\\Local\\Temp/ipykernel_18336/1807209069.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  classifier.fit_generator(x_train,steps_per_epoch =len(x_train),epochs=2, validation_data=x_test,validation_steps =len(x_test))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "493/493 [==============================] - 296s 592ms/step - loss: 0.2610 - accuracy: 0.9219 - val_loss: 0.2693 - val_accuracy: 0.9516\n",
      "Epoch 2/2\n",
      "493/493 [==============================] - 92s 186ms/step - loss: 0.0665 - accuracy: 0.9799 - val_loss: 0.3059 - val_accuracy: 0.9640\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x242d7929970>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit_generator(x_train,steps_per_epoch =len(x_train),epochs=2, validation_data=x_test,validation_steps =len(x_test))\n",
    "# No of images in test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:20:42.103310Z",
     "start_time": "2022-08-24T07:20:41.117577Z"
    }
   },
   "outputs": [],
   "source": [
    "# Save the model\n",
    "classifier.save('conversation.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:20:42.166113Z",
     "start_time": "2022-08-24T07:20:42.106252Z"
    }
   },
   "outputs": [],
   "source": [
    "model_json = classifier.to_json()\n",
    "with open(\"model-bw.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:20:42.804520Z",
     "start_time": "2022-08-24T07:20:42.170052Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image\n",
    "model = load_model(\"conversation.h5\") #loading the model for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:24:55.953310Z",
     "start_time": "2022-08-24T07:24:55.456815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = image.load_img(\"F:\\\\Spider\\\\Conversation_engine_for_deaf_and_dumb\\\\conversation engine-updated\\\\flask new\\\\Flask\\\\Dataset\\\\test_set\\\\A\\\\1.png\",target_size= (64,64))#loading of the image\n",
    "x = image.img_to_array(img)#image to array\n",
    "x = np.expand_dims(x,axis = 0)#changing the shape\n",
    "\n",
    "t=(model.predict(x))#predicting the classes\n",
    "pred=(np.argmax(t,axis=1))\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-24T07:25:43.817022Z",
     "start_time": "2022-08-24T07:25:43.798034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=['A', 'B', 'C','D','E','F','G','H','I']\n",
    "result=str(index[pred[0]])\n",
    "result"
   ]
  }
 ],
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