diff --git a/CNN/.ipynb_checkpoints/Image Recognition Model With CNN-checkpoint.ipynb b/CNN/.ipynb_checkpoints/Image Recognition Model With CNN-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b810e6f2f0dfc990825dc10eb48057391adac958 --- /dev/null +++ b/CNN/.ipynb_checkpoints/Image Recognition Model With CNN-checkpoint.ipynb @@ -0,0 +1,656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3DR-eO17geWu" + }, + "source": [ + "# Convolutional Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EMefrVPCg-60" + }, + "source": [ + "### Importing the libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "w3mybNcNuyRf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n", + "\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from keras.preprocessing.image import ImageDataGenerator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 38 + }, + "id": "Gk81TsV3vNYv", + "outputId": "ac51f686-a76e-4be8-ae56-5db116913a8b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.15.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oxQxCBWyoGPE" + }, + "source": [ + "## Part 1 - Data Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MvE-heJNo3GG" + }, + "source": [ + "### Preprocessing the Training set" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "IZPgIbSyzcZz" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 8000 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "train_datagen = ImageDataGenerator(rescale = 1./255,\n", + " shear_range = 0.2,\n", + " zoom_range = 0.2,\n", + " horizontal_flip = True)\n", + "training_set = train_datagen.flow_from_directory('dataset/training_set',\n", + " target_size = (64, 64),\n", + " batch_size = 32,\n", + " class_mode = 'binary')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mrCMmGw9pHys" + }, + "source": [ + "### Preprocessing the Test set" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "qTwLxo10zn-P" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2000 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "test_datagen = ImageDataGenerator(rescale = 1./255)\n", + "test_set = test_datagen.flow_from_directory('dataset/test_set',\n", + " target_size = (64, 64),\n", + " batch_size = 32,\n", + " class_mode = 'binary')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "af8O4l90gk7B" + }, + "source": [ + "## Part 2 - Building the CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ces1gXY2lmoX" + }, + "source": [ + "### Initialising the CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "OywsP_gTz8VA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], + "source": [ + "cnn=tf.keras.models.Sequential()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u5YJj_XMl5LF" + }, + "source": [ + "### Step 1 - Convolution" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dkA5f15L0IRA" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu',input_shape=[64,64,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tf87FpvxmNOJ" + }, + "source": [ + "### Step 2 - Pooling" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Khzh28Gj056f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\pooling\\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n", + "\n" + ] + } + ], + "source": [ + "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xaTOgD8rm4mU" + }, + "source": [ + "### Adding a second convolutional layer" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "G7GvlV8W1zFv" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))\n", + "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tmiEuvTunKfk" + }, + "source": [ + "### Step 3 - Flattening" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "CgmlA6xs16ub" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Flatten())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dAoSECOm203v" + }, + "source": [ + "### Step 4 - Full Connection" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "1DXEM0C-2Cv6" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Dense(units=128,activation='relu'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yTldFvbX28Na" + }, + "source": [ + "### Step 5 - Output Layer" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "bM9tOMr02dTk" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D6XkI90snSDl" + }, + "source": [ + "## Part 3 - Training the CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vfrFQACEnc6i" + }, + "source": [ + "### Compiling the CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "QRSs7B252tM8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n" + ] + } + ], + "source": [ + "cnn.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ehS-v3MIpX2h" + }, + "source": [ + "### Training the CNN on the Training set and evaluating it on the Test set" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "ibgjSnzf3Mai" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/25\n", + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n", + "\n", + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n", + "\n", + "250/250 [==============================] - 75s 298ms/step - loss: 0.6505 - accuracy: 0.6121 - val_loss: 0.5777 - val_accuracy: 0.7080\n", + "Epoch 2/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.5849 - accuracy: 0.6865 - val_loss: 0.5398 - val_accuracy: 0.7290\n", + "Epoch 3/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.5501 - accuracy: 0.7189 - val_loss: 0.5827 - val_accuracy: 0.6865\n", + "Epoch 4/25\n", + "250/250 [==============================] - 30s 122ms/step - loss: 0.5212 - accuracy: 0.7369 - val_loss: 0.5147 - val_accuracy: 0.7455\n", + "Epoch 5/25\n", + "250/250 [==============================] - 30s 119ms/step - loss: 0.4896 - accuracy: 0.7613 - val_loss: 0.5152 - val_accuracy: 0.7550\n", + "Epoch 6/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.4721 - accuracy: 0.7770 - val_loss: 0.4776 - val_accuracy: 0.7640\n", + "Epoch 7/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.4506 - accuracy: 0.7826 - val_loss: 0.4668 - val_accuracy: 0.7845\n", + "Epoch 8/25\n", + "250/250 [==============================] - 32s 126ms/step - loss: 0.4380 - accuracy: 0.7868 - val_loss: 0.4637 - val_accuracy: 0.7870\n", + "Epoch 9/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.4231 - accuracy: 0.8026 - val_loss: 0.4680 - val_accuracy: 0.7850\n", + "Epoch 10/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.4048 - accuracy: 0.8135 - val_loss: 0.5443 - val_accuracy: 0.7470\n", + "Epoch 11/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3917 - accuracy: 0.8242 - val_loss: 0.4698 - val_accuracy: 0.7875\n", + "Epoch 12/25\n", + "250/250 [==============================] - 37s 148ms/step - loss: 0.3743 - accuracy: 0.8284 - val_loss: 0.4812 - val_accuracy: 0.7940\n", + "Epoch 13/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3720 - accuracy: 0.8317 - val_loss: 0.4309 - val_accuracy: 0.8140\n", + "Epoch 14/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3497 - accuracy: 0.8443 - val_loss: 0.4593 - val_accuracy: 0.7980\n", + "Epoch 15/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.3407 - accuracy: 0.8514 - val_loss: 0.4699 - val_accuracy: 0.7925\n", + "Epoch 16/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.3249 - accuracy: 0.8593 - val_loss: 0.4712 - val_accuracy: 0.8035\n", + "Epoch 17/25\n", + "250/250 [==============================] - 30s 122ms/step - loss: 0.3044 - accuracy: 0.8748 - val_loss: 0.4846 - val_accuracy: 0.8060\n", + "Epoch 18/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3082 - accuracy: 0.8608 - val_loss: 0.4750 - val_accuracy: 0.8040\n", + "Epoch 19/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.2860 - accuracy: 0.8752 - val_loss: 0.4533 - val_accuracy: 0.8180\n", + "Epoch 20/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2761 - accuracy: 0.8792 - val_loss: 0.5786 - val_accuracy: 0.7975\n", + "Epoch 21/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2634 - accuracy: 0.8861 - val_loss: 0.4989 - val_accuracy: 0.8085\n", + "Epoch 22/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2491 - accuracy: 0.8946 - val_loss: 0.5098 - val_accuracy: 0.8160\n", + "Epoch 23/25\n", + "250/250 [==============================] - 31s 124ms/step - loss: 0.2385 - accuracy: 0.8981 - val_loss: 0.5296 - val_accuracy: 0.8115\n", + "Epoch 24/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2260 - accuracy: 0.9040 - val_loss: 0.5117 - val_accuracy: 0.8105\n", + "Epoch 25/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.2117 - accuracy: 0.9146 - val_loss: 0.5145 - val_accuracy: 0.8180\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnn.fit(x=training_set,validation_data=test_set,epochs=25)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U3PZasO0006Z" + }, + "source": [ + "## Part 4 - Making a single prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "PQGfPACq3mGS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 107ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_1.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "xMvlNcFN5yEX" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 25ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_2.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cat\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 26ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_3.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cat\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 25ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_4.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/CNN/Image Recognition Model With CNN.ipynb b/CNN/Image Recognition Model With CNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6fb604b3e711fc821e90a188355dd95ad08b9b3d --- /dev/null +++ b/CNN/Image Recognition Model With CNN.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3DR-eO17geWu" + }, + "source": [ + "## Image Recognition Model With CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EMefrVPCg-60" + }, + "source": [ + "### Importing the libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "w3mybNcNuyRf" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from keras.preprocessing.image import ImageDataGenerator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 38 + }, + "id": "Gk81TsV3vNYv", + "outputId": "ac51f686-a76e-4be8-ae56-5db116913a8b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.15.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oxQxCBWyoGPE" + }, + "source": [ + "## Part 1 - Data Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MvE-heJNo3GG" + }, + "source": [ + "### Preprocessing the Training set" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "IZPgIbSyzcZz" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 8000 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "train_datagen = ImageDataGenerator(rescale = 1./255,\n", + " shear_range = 0.2,\n", + " zoom_range = 0.2,\n", + " horizontal_flip = True)\n", + "training_set = train_datagen.flow_from_directory('dataset/training_set',\n", + " target_size = (64, 64),\n", + " batch_size = 32,\n", + " class_mode = 'binary')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mrCMmGw9pHys" + }, + "source": [ + "### Preprocessing the Test set" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "qTwLxo10zn-P" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2000 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "test_datagen = ImageDataGenerator(rescale = 1./255)\n", + "test_set = test_datagen.flow_from_directory('dataset/test_set',\n", + " target_size = (64, 64),\n", + " batch_size = 32,\n", + " class_mode = 'binary')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "af8O4l90gk7B" + }, + "source": [ + "## Part 2 - Building the CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ces1gXY2lmoX" + }, + "source": [ + "### Initialising the CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "OywsP_gTz8VA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n" + ] + } + ], + "source": [ + "cnn=tf.keras.models.Sequential()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u5YJj_XMl5LF" + }, + "source": [ + "### Step 1 - Convolution" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dkA5f15L0IRA" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu',input_shape=[64,64,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tf87FpvxmNOJ" + }, + "source": [ + "### Step 2 - Pooling" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Khzh28Gj056f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\pooling\\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n", + "\n" + ] + } + ], + "source": [ + "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xaTOgD8rm4mU" + }, + "source": [ + "### Adding a second convolutional layer" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "G7GvlV8W1zFv" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))\n", + "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tmiEuvTunKfk" + }, + "source": [ + "### Step 3 - Flattening" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "CgmlA6xs16ub" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Flatten())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dAoSECOm203v" + }, + "source": [ + "### Step 4 - Full Connection" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "1DXEM0C-2Cv6" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Dense(units=128,activation='relu'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yTldFvbX28Na" + }, + "source": [ + "### Step 5 - Output Layer" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "bM9tOMr02dTk" + }, + "outputs": [], + "source": [ + "cnn.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D6XkI90snSDl" + }, + "source": [ + "## Part 3 - Training the CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vfrFQACEnc6i" + }, + "source": [ + "### Compiling the CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "QRSs7B252tM8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n" + ] + } + ], + "source": [ + "cnn.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ehS-v3MIpX2h" + }, + "source": [ + "### Training the CNN on the Training set and evaluating it on the Test set" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "ibgjSnzf3Mai" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/25\n", + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n", + "\n", + "WARNING:tensorflow:From C:\\Users\\manik\\anaconda3\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n", + "\n", + "250/250 [==============================] - 75s 298ms/step - loss: 0.6505 - accuracy: 0.6121 - val_loss: 0.5777 - val_accuracy: 0.7080\n", + "Epoch 2/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.5849 - accuracy: 0.6865 - val_loss: 0.5398 - val_accuracy: 0.7290\n", + "Epoch 3/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.5501 - accuracy: 0.7189 - val_loss: 0.5827 - val_accuracy: 0.6865\n", + "Epoch 4/25\n", + "250/250 [==============================] - 30s 122ms/step - loss: 0.5212 - accuracy: 0.7369 - val_loss: 0.5147 - val_accuracy: 0.7455\n", + "Epoch 5/25\n", + "250/250 [==============================] - 30s 119ms/step - loss: 0.4896 - accuracy: 0.7613 - val_loss: 0.5152 - val_accuracy: 0.7550\n", + "Epoch 6/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.4721 - accuracy: 0.7770 - val_loss: 0.4776 - val_accuracy: 0.7640\n", + "Epoch 7/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.4506 - accuracy: 0.7826 - val_loss: 0.4668 - val_accuracy: 0.7845\n", + "Epoch 8/25\n", + "250/250 [==============================] - 32s 126ms/step - loss: 0.4380 - accuracy: 0.7868 - val_loss: 0.4637 - val_accuracy: 0.7870\n", + "Epoch 9/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.4231 - accuracy: 0.8026 - val_loss: 0.4680 - val_accuracy: 0.7850\n", + "Epoch 10/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.4048 - accuracy: 0.8135 - val_loss: 0.5443 - val_accuracy: 0.7470\n", + "Epoch 11/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3917 - accuracy: 0.8242 - val_loss: 0.4698 - val_accuracy: 0.7875\n", + "Epoch 12/25\n", + "250/250 [==============================] - 37s 148ms/step - loss: 0.3743 - accuracy: 0.8284 - val_loss: 0.4812 - val_accuracy: 0.7940\n", + "Epoch 13/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3720 - accuracy: 0.8317 - val_loss: 0.4309 - val_accuracy: 0.8140\n", + "Epoch 14/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3497 - accuracy: 0.8443 - val_loss: 0.4593 - val_accuracy: 0.7980\n", + "Epoch 15/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.3407 - accuracy: 0.8514 - val_loss: 0.4699 - val_accuracy: 0.7925\n", + "Epoch 16/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.3249 - accuracy: 0.8593 - val_loss: 0.4712 - val_accuracy: 0.8035\n", + "Epoch 17/25\n", + "250/250 [==============================] - 30s 122ms/step - loss: 0.3044 - accuracy: 0.8748 - val_loss: 0.4846 - val_accuracy: 0.8060\n", + "Epoch 18/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.3082 - accuracy: 0.8608 - val_loss: 0.4750 - val_accuracy: 0.8040\n", + "Epoch 19/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.2860 - accuracy: 0.8752 - val_loss: 0.4533 - val_accuracy: 0.8180\n", + "Epoch 20/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2761 - accuracy: 0.8792 - val_loss: 0.5786 - val_accuracy: 0.7975\n", + "Epoch 21/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2634 - accuracy: 0.8861 - val_loss: 0.4989 - val_accuracy: 0.8085\n", + "Epoch 22/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2491 - accuracy: 0.8946 - val_loss: 0.5098 - val_accuracy: 0.8160\n", + "Epoch 23/25\n", + "250/250 [==============================] - 31s 124ms/step - loss: 0.2385 - accuracy: 0.8981 - val_loss: 0.5296 - val_accuracy: 0.8115\n", + "Epoch 24/25\n", + "250/250 [==============================] - 30s 120ms/step - loss: 0.2260 - accuracy: 0.9040 - val_loss: 0.5117 - val_accuracy: 0.8105\n", + "Epoch 25/25\n", + "250/250 [==============================] - 30s 121ms/step - loss: 0.2117 - accuracy: 0.9146 - val_loss: 0.5145 - val_accuracy: 0.8180\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnn.fit(x=training_set,validation_data=test_set,epochs=25)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U3PZasO0006Z" + }, + "source": [ + "## Part 4 - Making a single prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "PQGfPACq3mGS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 107ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_1.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "xMvlNcFN5yEX" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 25ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_2.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cat\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 26ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_3.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cat\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 25ms/step\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.preprocessing import image\n", + "test_image=image.load_img('dataset/single_prediction/cat_or_dog_4.jpg',target_size=(64,64))\n", + "test_image=image.img_to_array(test_image)\n", + "test_image=np.expand_dims(test_image,axis=0)\n", + "result=cnn.predict(test_image)\n", + "training_set.class_indices\n", + "if result[0][0]==1:\n", + " prediction='dog'\n", + "else:\n", + " prediction='cat'" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog\n" + ] + } + ], + "source": [ + "print(prediction)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/CNN/dataset/.DS_Store b/CNN/dataset/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..9e2c7924542a528658e084f2302243a5d57b53cd Binary files /dev/null and b/CNN/dataset/.DS_Store differ diff --git a/CNN/dataset/__MACOSX/._dataset b/CNN/dataset/__MACOSX/._dataset new file mode 100644 index 0000000000000000000000000000000000000000..ed4798daca60bf9b5223915ec1f3630111a39d88 Binary files /dev/null and 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