diff --git a/Final Task/A4_200698.ipynb b/Final Task/A4_200698.ipynb new file mode 100644 index 0000000..8fc4a5e --- /dev/null +++ b/Final Task/A4_200698.ipynb @@ -0,0 +1,983 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rtI19Rt-H7Uc" + }, + "source": [ + "## Final Task:\n", + "This is your final evaluation for the project. As decided, we will be predicting images of people into three classes: `without_mask`, `mask_weared_incorrect` and `with_mask`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "c2CiXcHQTbX8" + }, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QKDPyiZTIm1c" + }, + "source": [ + "### Loading the dataset\n", + "Make a copy of the dataset given to you in your Google Drive (keep it outside, don't put it in any folder to avoid inconvenience). Ensure it is named as `Mask_Dataset` or change the path (the variable `data_dir`) accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hNEMe7XsIjrK", + "outputId": "337cfe80-edfe-4895-a2c8-e1619aded4d0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "# Mount Google Drive\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "8CXzo4MOJOl8" + }, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "path='/content/drive/MyDrive/Mask_Dataset/'\n", + "data_dir = pathlib.Path(path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YHPHkGyDKscK" + }, + "source": [ + "### Know the Dataset\n", + "Most of the code is written for you as you aren't used to these libraries. You are to go through the documentation for your benefit." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PzbSy-vXKjD-", + "outputId": "ceb5309a-6ad4-4e7c-a7df-598c397888a4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9002\n" + ] + } + ], + "source": [ + "# Print image count\n", + "image_count = len(list(data_dir.glob('*/*.png')))\n", + "print(image_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rFHWFYj5NCVm", + "outputId": "64ff4e69-1a0d-49d7-91a5-93a54b482bb0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['without_mask', 'mask_weared_incorrect', 'with_mask']\n" + ] + } + ], + "source": [ + "# Print Output Labels\n", + "import os\n", + "output_classes = os.listdir(data_dir)\n", + "print(output_classes)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "id": "fESyMw90KaxN", + "outputId": "504b5151-d124-49c6-d0ac-0541126adcbd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/drive/MyDrive/Mask_Dataset/without_mask\n", + "/content/drive/MyDrive/Mask_Dataset/mask_weared_incorrect\n", + "/content/drive/MyDrive/Mask_Dataset/with_mask\n", + "[3014, 2994, 2994]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot count of each ouput label\n", + "import matplotlib.pyplot as plt\n", + "\n", + "count=[]\n", + "for label in output_classes:\n", + " this_path=path+label\n", + " dir=pathlib.Path(this_path)\n", + " im_count=os.listdir(dir)\n", + " print(dir)\n", + " count.append(len(im_count))\n", + "\n", + "print(count)\n", + "\n", + "plt.bar(output_classes,count)\n", + "plt.title(\"Statistics\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401 + }, + "id": "HDSJ2Zk5a14s", + "outputId": "940d5127-e21a-4ded-97ba-96d324ea798b" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check some sample images (Use of cv2)\n", + "import cv2\n", + "from google.colab.patches import cv2_imshow\n", + "\n", + "for label in output_classes:\n", + " this_path=path+label\n", + " dir=pathlib.Path(this_path)\n", + " im_count=os.listdir(dir)\n", + " # lets say we want to show the image at index 7 in every class list\n", + " img_show = this_path + '/' + im_count[7]\n", + " img = cv2.imread(img_show)\n", + " cv2_imshow(img)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jWBEMC1FUfXS", + "outputId": "f5df0985-aab9-46a9-d480-d9e5b97d0688" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(128, 128, 3)\n" + ] + } + ], + "source": [ + "# Check shape of the images in your dataset. This will be helpful while specifying input_shape in your Transfer Learning Model\n", + "print(img.shape)\n", + "image_shape = img.shape\n", + "# print(image_shape)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "52BhBWRab5yc", + "outputId": "69eed97e-32c3-4c00-93f4-f143e1347179" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), 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128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3), (128, 128, 3)]\n", + "9002\n" + ] + } + ], + "source": [ + "# Check if all the images have same shape, else you need to resize them to some common size\n", + "# I have created a list which stores the sizes of all the images. \n", + "# In order to check for different shapes, we will count the unique shapes present in the list\n", + "combined_images_shape = []\n", + "dict \n", + "for label in output_classes:\n", + " this_path=path+label\n", + " dir=pathlib.Path(this_path)\n", + " im_count = os.listdir(dir)\n", + "\n", + " for i in im_count:\n", + " img_show = this_path + '/' + i\n", + " img = cv2.imread(img_show)\n", + " combined_images_shape.append(img.shape) \n", + "\n", + "print(combined_images_shape)\n", + "print(len(combined_images_shape))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G-Atau4Rfc-x", + "outputId": "679f08d4-ec83-4758-d1f8-08f8fc1d1783" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The size of all the images is : dict_keys([(128, 128, 3)])\n" + ] + } + ], + "source": [ + "# If the shape is variable, reshape to a common size \n", + "# If it is same, prove it\n", + "# counted the number of unique shapes present in the above list\n", + "\n", + "from collections import Counter\n", + "items = Counter(combined_images_shape).keys()\n", + "if len(items)==1:\n", + " print(f\"The size of all the images is : {items}\")\n", + "else:\n", + " print(f\"The size of all the images are not same\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zSoUXS1cRbnu" + }, + "source": [ + "### Model Definition\n", + "Choose a model for Transfer Learning (You may also experment with multiple models and keep all of them in this notebook)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "QKZmIgXMTHfy" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.preprocessing import image\n", + "from tensorflow.keras.models import Sequential\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9xWLUibHRNGj", + "outputId": "38f34bdb-b86a-42c7-d21a-cad645a4912e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58892288/58889256 [==============================] - 0s 0us/step\n", + "58900480/58889256 [==============================] - 0s 0us/step\n" + ] + } + ], + "source": [ + "# Choose and define base model\n", + "# In this step we are going to import the VGG16 architecture from keras\n", + "from tensorflow.keras.applications.vgg16 import VGG16\n", + "from tensorflow.keras.applications.vgg16 import preprocess_input\n", + "# include_top is false because we don't need to import whole architecture.\n", + "# We need only the Convolutional architecture of VGG16. After that we will add our layer and which will be trained according to our dataset.\n", + "vgg16 = VGG16(input_shape=image_shape, weights='imagenet', include_top=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J3TwB_GLd7BU", + "outputId": "8724b52f-1b8c-4547-c67f-0680b557f31e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"vgg16\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 128, 128, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, 128, 128, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, 128, 128, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, 64, 64, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", + " \n", + "=================================================================\n", + "Total params: 14,714,688\n", + "Trainable params: 14,714,688\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Print base model summary and have a look at the layers\n", + "vgg16.summary()\n", + "# here if we see the last layer, i.e prediction layer, we can see that this model can predict from 1000 class. \n", + "# but here we dont need 1000 class as we only have 3 class" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F_Heq3C1eKd-", + "outputId": "6d39c5c3-b3e2-4368-87c5-ac325951340b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"vgg16\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 128, 128, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, 128, 128, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, 128, 128, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, 64, 64, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", + " \n", + "=================================================================\n", + "Total params: 14,714,688\n", + "Trainable params: 0\n", + "Non-trainable params: 14,714,688\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# As we're using Transfer Learning, you do not need to train all the layers. Freeze all of the layers or train some layers (experiment)\n", + "# here I will choose to free all the layer except the last layer named as predictions.\n", + "# here as you can see above we are having Trainable parameters as 14,714,688. What we want is to freeze them so that \n", + "# while doing back propagation they dont get changed\n", + "\n", + "for layer in vgg16.layers:\n", + " layer.trainable = False\n", + "vgg16.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "MKx1EtUJea6D" + }, + "outputs": [], + "source": [ + "# Append Fully connected/custom Conv2D/Dropout/MaxPooling layers to the base model\n", + "# Now we need to add some more trainable layer which will be trained according to our dataset\n", + "flatten = Flatten()(vgg16.output)\n", + "dense_1 = Dense(units=500, activation='relu')(flatten)\n", + "dense_2 = Dense(units=500, activation='relu')(dense_1)\n", + "dense_3 = Dense(units=100, activation='relu')(dense_2)\n", + "dense_4 = Dense(units=100, activation='relu')(dense_3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "q6I3oTTNgP8L" + }, + "outputs": [], + "source": [ + "# Add the final output layer\n", + "dense_output = Dense(units=3, activation='sigmoid')(dense_4)\n", + "model = Model(inputs=vgg16.input, outputs=dense_output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6aVQocJwgN5r", + "outputId": "bb20833e-bd82-4f97-c0de-7b4085db7575" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 128, 128, 3)] 0 \n", + " \n", + " block1_conv1 (Conv2D) (None, 128, 128, 64) 1792 \n", + " \n", + " block1_conv2 (Conv2D) (None, 128, 128, 64) 36928 \n", + " \n", + " block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 \n", + " \n", + " block2_conv1 (Conv2D) (None, 64, 64, 128) 73856 \n", + " \n", + " block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 \n", + " \n", + " block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 \n", + " \n", + " block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 \n", + " \n", + " block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 \n", + " \n", + " block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 \n", + " \n", + " block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 \n", + " \n", + " block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 \n", + " \n", + " block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 \n", + " \n", + " block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n", + " \n", + " block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", + " \n", + " flatten (Flatten) (None, 8192) 0 \n", + " \n", + " dense (Dense) (None, 500) 4096500 \n", + " \n", + " dense_1 (Dense) (None, 500) 250500 \n", + " \n", + " dense_2 (Dense) (None, 100) 50100 \n", + " \n", + " dense_3 (Dense) (None, 100) 10100 \n", + " \n", + " dense_4 (Dense) (None, 3) 303 \n", + " \n", + "=================================================================\n", + "Total params: 19,122,191\n", + "Trainable params: 4,407,503\n", + "Non-trainable params: 14,714,688\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Print your model's summary\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "qdC71fUBgXAg" + }, + "outputs": [], + "source": [ + "# Compile you model (set the parameters like loss/optimizers/metrics)\n", + "model.compile(optimizer = \"adam\", loss = \"categorical_crossentropy\", metrics = \"accuracy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RdUSMLggifex" + }, + "source": [ + "### Data Augmentation and Pre-processing\n", + "Augment the data. You may also try dyanamic augmentation using [`tf.keras.preprocessing.image.ImageDataGenerator `](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator). \n", + "You may use [`tf.keras.applications.vgg16.preprocess_input`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/vgg16/preprocess_input)(or some other base model's utility) for pre-processing (can also be passed as a parameter to `ImageDataGenerator`)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "DBscSsvkgn39" + }, + "outputs": [], + "source": [ + "from keras.applications.vgg16 import preprocess_input # Change according to your base model\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "# training_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range = 0.2)\n", + " \n", + "image_generator = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n", + "\n", + "# Your code \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IcKPxCpOkcuG" + }, + "source": [ + "### Training and Validation Dataset \n", + "Split the dataset into training and validation (We'll be looking for your validation accuracy, assume we are using complete dataset for now). \n", + "\n", + "Hint: `flow_from_directory` used with `ImageDataGenerator` will simplify things for you." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sB7hb3ybkJRq", + "outputId": "454eeba1-eb5c-447e-875a-4143160b7c78" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7204 images belonging to 3 classes.\n", + "Found 1798 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Your code\n", + "# image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2) \n", + "\n", + "train_dataset = image_generator.flow_from_directory(batch_size=32,\n", + " directory=path,\n", + " \n", + " target_size=(128, 128), \n", + " subset=\"training\",\n", + " class_mode='categorical')\n", + "\n", + "validation_dataset = image_generator.flow_from_directory(batch_size=32,\n", + " directory=path,\n", + " \n", + " target_size=(128,128), \n", + " subset=\"validation\",\n", + " class_mode='categorical')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZZPsjpT1mp3z" + }, + "source": [ + "### Training \n", + "Train your model for some epochs and plot the graph. Try and save your best model. Experiment with the parameters of `model.fit`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Gs2X14MBmu7W", + "outputId": "e0da6f0c-4064-45de-f3d0-b0999688ea45" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "226/226 [==============================] - 37s 99ms/step - loss: 0.2020 - accuracy: 0.9207 - val_loss: 0.1360 - val_accuracy: 0.9488\n", + "Epoch 2/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0782 - accuracy: 0.9725 - val_loss: 0.1400 - val_accuracy: 0.9494\n", + "Epoch 3/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0637 - accuracy: 0.9764 - val_loss: 0.0807 - val_accuracy: 0.9761\n", + "Epoch 4/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0285 - accuracy: 0.9901 - val_loss: 0.0719 - val_accuracy: 0.9794\n", + "Epoch 5/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0326 - accuracy: 0.9895 - val_loss: 0.0818 - val_accuracy: 0.9744\n", + "Epoch 6/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0232 - accuracy: 0.9915 - val_loss: 0.0610 - val_accuracy: 0.9828\n", + "Epoch 7/20\n", + "226/226 [==============================] - 21s 93ms/step - loss: 0.0181 - accuracy: 0.9942 - val_loss: 0.1116 - val_accuracy: 0.9655\n", + "Epoch 8/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0226 - accuracy: 0.9914 - val_loss: 0.1270 - val_accuracy: 0.9666\n", + "Epoch 9/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0186 - accuracy: 0.9936 - val_loss: 0.0973 - val_accuracy: 0.9716\n", + "Epoch 10/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0116 - accuracy: 0.9957 - val_loss: 0.1778 - val_accuracy: 0.9661\n", + "Epoch 11/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0269 - accuracy: 0.9917 - val_loss: 0.1012 - val_accuracy: 0.9805\n", + "Epoch 12/20\n", + "226/226 [==============================] - 21s 93ms/step - loss: 0.0151 - accuracy: 0.9961 - val_loss: 0.0823 - val_accuracy: 0.9811\n", + "Epoch 13/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0127 - accuracy: 0.9964 - val_loss: 0.0963 - val_accuracy: 0.9844\n", + "Epoch 14/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0130 - accuracy: 0.9956 - val_loss: 0.1010 - val_accuracy: 0.9744\n", + "Epoch 15/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 0.1270 - val_accuracy: 0.9839\n", + "Epoch 16/20\n", + "226/226 [==============================] - 20s 90ms/step - loss: 0.0146 - accuracy: 0.9951 - val_loss: 0.0619 - val_accuracy: 0.9822\n", + "Epoch 17/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1040 - val_accuracy: 0.9828\n", + "Epoch 18/20\n", + "226/226 [==============================] - 21s 92ms/step - loss: 0.0275 - accuracy: 0.9932 - val_loss: 0.1800 - val_accuracy: 0.9360\n", + "Epoch 19/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0206 - accuracy: 0.9928 - val_loss: 0.0863 - val_accuracy: 0.9794\n", + "Epoch 20/20\n", + "226/226 [==============================] - 20s 89ms/step - loss: 0.0068 - accuracy: 0.9982 - val_loss: 0.0864 - val_accuracy: 0.9811\n" + ] + } + ], + "source": [ + "from keras.callbacks import ModelCheckpoint\n", + "\n", + "r = model.fit(\n", + " train_dataset,\n", + " validation_data=validation_dataset,\n", + " epochs=20,\n", + " steps_per_epoch=len(train_dataset),\n", + " validation_steps=len(validation_dataset)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FTvRa1FXri4R" + }, + "source": [ + "### Evaluate the performance" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 530 + }, + "id": "cTH6flzcrck0", + "outputId": "40802b2c-99da-4bcf-9992-dc9b6ab62170" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot training & validation loss/accuracy values\n", + "print(r.history.keys())\n", + "# plot the loss\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(r.history['loss'], label='train loss')\n", + "plt.plot(r.history['val_loss'], label='val loss')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# plot the accuracy\n", + "plt.plot(r.history['accuracy'], label='train acc')\n", + "plt.plot(r.history['val_accuracy'], label='val acc')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fJ-ZtU84r66Z", + "outputId": "ce197787-0c01-42c9-fc37-ce7cfc18d28f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " without_mask 0.98 1.00 0.99 598\n", + "mask_weared_incorrect 0.98 0.97 0.98 598\n", + " with_mask 0.99 0.97 0.98 602\n", + "\n", + " accuracy 0.98 1798\n", + " macro avg 0.98 0.98 0.98 1798\n", + " weighted avg 0.98 0.98 0.98 1798\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report, confusion_matrix\n", + "import numpy as np\n", + "\n", + "import itertools\n", + "import numpy as np\n", + "\n", + "\n", + "validation_classes = []\n", + "validation_images = []\n", + "for i in range( -(-validation_dataset.samples // validation_dataset.batch_size)):\n", + " batch = validation_dataset.next()\n", + " expected = np.argmax(batch[1], axis=1) \n", + " validation_classes.extend(expected)\n", + " validation_images.extend(batch[0])\n", + "validation_classes = np.array(validation_classes)\n", + "validation_images = np.array(validation_images)\n", + "Y_pred = model.predict(validation_images)\n", + "y_pred = np.argmax(Y_pred, axis=1)\n", + "\n", + "\n", + "print(classification_report(validation_classes, y_pred, \n", + "\t\ttarget_names = ['without_mask', 'mask_weared_incorrect', 'with_mask']))\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "A4_200782.ipynb", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (windows store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "c0f7ff968abd5cdc646c3b0d79d8d9fba0fbc1faf12e0395fe0c5d95a57fad77" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Resources/README.md b/Resources/README.md new file mode 100644 index 0000000..bb6b641 --- /dev/null +++ b/Resources/README.md @@ -0,0 +1,219 @@ +Let’s start with the installation of Python’s IDE - Jupyter. Alternatively, you can use Google Colab for this purpose. You will not be needed to install any library on your local machine. Everything will work on Google’s server, and it’s best suited for Machine Learners. You can explore it out by googling, agar comfortable lage, then go with it. +Here’s a quick view of Google Colab - https://www.youtube.com/watch?v=oCngVVBSsmA + + +Installing Jupyter Notebook +Open the command prompt and install using the following command. + +To get comfortable with the Jupyter notebook, you can just skim through this article https://www.dataquest.io/blog/jupyter-notebook-tutorial/. +Also, you can use this Jupyter cheatsheet (given below) to get a hold of it. +https://www.datacamp.com/community/blog/jupyter-notebook-cheat-sheet + + +Getting started with Python +You need not have any prior programming knowledge to start using Python. We only expect from you is seriousness while learning a new language. Honestly, working with Python will seem intuitive, but don't ever SKIP anything. Also, try to implement every line of code on Jupyter/colab to get confidence in Python and get familiar with the Jupyter/Colab notebook’s working. +Start learning Python with https://www.w3schools.com/python/default.asp +Complete every topic serial-wise in the list, up to Python Modules. +Start learning libraries (next section) + + + +Python Libraries +Python is considered the best language for Machine Learning and many other domains because of the extensive support it offers through its libraries. Think of them as a piece of code that someone else wrote to ease your work, and you just need to call that code in a single line and get your job done. Isn’t that fun? +There are thousands of libraries to explore in Python depending upon your work and domain, but primarily we will be dealing with the three most commonly used ones. In the later part of your journey, learning a library will become your everyday task xD. +So let's get started! + + + +Numpy or Numerical Python (All about array manipulation) +To start learning and working with software or a library, it is a good habit to go through its official documentation first. Documentation is most accurate and would best guide you if you encounter bugs. To learn Numpy, it is highly recommended to go through its doc. +Documentation: https://numpy.org/devdocs/user/quickstart.html (Must do) +Tutorial: https://www.w3schools.com/python/numpy/default.asp (A quick watch) +Practice: https://www.machinelearningplus.com/python/101-numpy-exercises-python/ (Must do. Consider discussing these problems with your friends and in groups) + +Pandas (Python’s data analysis library) +Starter video to get the feel and motivation to learn pandas +https://www.youtube.com/watch?v=dcqPhpY7tWk (Quick watch) +Attached below is a notebook on Pandas which you must do completely to get comfortable with the library +https://drive.google.com/file/d/1E9BIQjJxVRiWTuPOe_AJsPRj3aQX3c0I/view?usp=sharing (Must do) +If you ever find difficulty or want to search for something specific in Pandas, as always, refer to their official documentation. +Documentation - https://pandas.pydata.org/docs/user_guide/index.html + +Matplotlib (All about graphs) +It is the most commonly used plotting library in python. Easy to use and offers a wide range of functions for better visualization of your data. We will only go through the most basic plots for now. Here's an excellent tutorial to get you started: +https://matplotlib.org/2.0.2/users/pyplot_tutorial.html (MUST DO). + +You can also watch these videos to get a good grasp https://youtube.com/playlist?list=PLeo1K3hjS3uu4Lr8_kro2AqaO6CFYgKOl. + + + +Machine Learning begins now! +First of all, a little bit of motivation to get started with this domain. It won't be long, but it will get you going at your best pace! + +Sundar Pichai’s short speech (2.5 minutes watch) +https://www.youtube.com/watch?v=5cFUZ03Sbhc +https://machinelearningmastery.com/why-get-into-machine-learning/ (5 min read) +Tadeo Corradi’s (researcher) TEDx video (10 minutes, can be watched at 1.5x) +https://www.youtube.com/watch?v=4qCzxo2wPCw + +So yea, let's get started with the journey. +In the workshop, we defined the basic workflow of a Machine learner, that is, firstly, we create at random, we iterate and iterate, and again iterate to find the best-suited results. So in those iterations, we come up with specific algorithms that ML enthusiasts commonly use. Let us go through them one by one. +At the end of this document, we will be providing resources to some relevant course materials and notes to start from scratch in a structured manner, but it is highly recommended to follow the steps as mentioned. + +Linear regression (firstly read the article carefully, then watch video on your pace) -https://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86 +-https://www.youtube.com/watch?v=1-OGRohmH2s + +Logistic regression (firstly read the article carefully, then watch video on your pace) +-https://www.youtube.com/watch?v=yIYKR4sgzI8 +-https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148 + +Decision tree classifier +The attached article is the best visualization you can have for a DT. +http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ (Must) + +To get more comfortable with the mechanism, consider watching this at your pace. +https://www.youtube.com/watch?v=7VeUPuFGJHk + +Random forest classifier +https://williamkoehrsen.medium.com/random-forest-simple-explanation-377895a60d2d +https://www.youtube.com/watch?v=v6VJ2RO66Ag + +KNN + -https://medium.com/swlh/k-nearest-neighbor-ca2593d7a3c4 (only up to ‘Brute force’) + -https://www.youtube.com/watch?v=HVXime0nQeI + +A visualization to Neural networks (deep learning) +https://www.youtube.com/watch?v=aircAruvnKk + + +[Advice] It's highly recommended to discuss all these algorithms with friends, in groups, and on the Discord server (if needed) to get the most out of the given material. + + +Listed below are some courses and materials to start your DS journey in a very structured way. All the prerequisites and basic understanding required to crack the given material below are already provided in this document. Make sure to follow everything in a step-wise manner. + + + +Lecture notes for Machine Learning (Stanford’s CS229) - For readers +https://sgfin.github.io/files/notes/CS229_Lecture_Notes.pdf + +Lecture videos for Machine Learning (Stanford’s CC229) - For watchers +https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU + +Book (for enthusiastic readers, this is a great book to get started) +https://drive.google.com/file/d/1hFPxorU1AMDXE_02HBwM0hAfcwsdOCeo/view?usp=sharing + +Andrew Ng’s Deep Learning Specialisation (Coursera course) +It is a highly recommended course series for Deep Learning (a set of 5 paid courses, but you can get them for free after applying for Financial Aid, regarding this you can contact any of the ICG secretaries). +You can get started with this course series if you have familiarity with python and a basic understanding of ML algorithms (the ones covered in this document). + +Course link: https://www.coursera.org/specializations/deep-learning + +[Advice] Consider avoiding Standford’s Machine Learning course by Andrew Ng (on Coursera) because it's outdated and uses Octave for programming (a pain). + + +That's it for now, we will update the Discord server with further steps to your ML journey. +Feel free to ping us for any doubts and keep the server active! +All the best, keep learning 👍 + +As decided, we'll be covering some basic stuff before we move on to Deep Learning. Here are resources for the same: + +Basic Python: https://www.w3schools.com/python/ (you might be familiar with most of it by now, but it might be handy to have something in case you forget anything) +Numpy: https://www.w3schools.com/python/numpy/default.asp (go only through the Basic part i.e skip Random & ufunc) +Pandas: https://www.w3schools.com/python/pandas/default.asp (go only through the Basic part, Cleaning Data won't be required as of now) +Matplotlib: https://www.w3schools.com/python/matplotlib_intro.asp + +You don't need to mug up the syntax. You just need to know what utilities exist and where you can use them. You'll get used to the important syntax as we proceed. + +Next, we move on to the basics of Machine Learning. The following article is a great introduction to it. It is quite exhaustive but not that elaborative. So, if you don't get something, feel free to ask here or Google it up. +Machine Learning —Fundamentals: https://towardsdatascience.com/machine-learning-basics-part-1-a36d38c7916 + +We'll be starting with Deep Learning next week. Since our aim is to classify images, we won't go deep into how we deal with traditional data. But if you want, you can always dig something that interests you, it may help 🙂 + +We hope you have gone through this week's reading material. + +Here is your first assignment: https://colab.research.google.com/drive/1lHTeY0ieI9TWcR88yhQV1EwsX7n5PSJq?usp=sharing + +You are supposed to make a copy of this notebook and work on that copy. All other details are in the notebook. The details on how to submit assignments would be conveyed to you soon. + +Deadline: 5th June 2022 + +In the second week, we will be learning about Deep Learning and TensorFlow. +These are a few short articles that will explain what Deep Learning is: +https://medium.com/free-code-camp/want-to-know-how-deep-learning-works-heres-a-quick-guide-for-everyone-1aedeca88076 + +https://towardsdatascience.com/an-introduction-to-deep-learning-af63448c122c + +https://www.analyticsvidhya.com/blog/2021/05/beginners-guide-to-artificial-neural-network/ + +https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0 + +https://medium.com/towards-data-science/overfitting-vs-underfitting-a-conceptual-explanation-d94ee20ca7f9 + +Kindly give these a thorough read. + + +Furthermore, to learn about TensorFlow, kindly refer to the following links: + +https://www.tensorflow.org/tutorials/keras/regression +https://www.tensorflow.org/tutorials +(Beginner Quickstart and Keras Basics) + +If you have finished Assignment 1, you can start with Assignment 2. +The deadline for Assgn2 is Sunday 12th June. +https://colab.research.google.com/drive/1ptX8WQ4CFpsuB3FySAo-Ofzinil-gfw_?usp=sharing + +We hope you are working on Assignment 2. Here are the resources for the next week. You could try to read them before tomorrow's meet. +We now dive into the heart of the project: CNNs. + +Simple Introduction to Convolutional Neural Networks: https://towardsdatascience.com/simple-introduction-to-convolutional-neural-networks-cdf8d3077bac +This article covers all the basic concepts related to CNNs. Go through it. As suggested earlier as well, google the concepts you don't get (as the article isn't that elaborative). You will find plenty of resources. And it goes without saying, if you have any specific doubts, you can ask us here. + +CNN in Tensorflow (intro): https://www.tensorflow.org/tutorials/images/cnn & https://www.tensorflow.org/tutorials/images/classification +This will help you in your next assignment. You can skim through it, and just know what utilities Tensorflow provides. You would get a better idea when you implement it through the assignment. + + few of you have reached out to me asking for other resources for TensorFlow. +https://youtu.be/tPYj3fFJGjk +You can follow this YouTube video. Refer to Module 3 and 4. You can refer to Module 1 and 2 as well, but that is related to the Week 1 topics. + +Refer to this link for help and try to understand the code: +https://www.tensorflow.org/tutorials/keras/regression + +If you have any doubts, you can contact us. + +https://keras.io/api/optimizers/ +Keras documentation: Optimizers +Image +Refer to this for Gradient Descent in TensorFlow + +Here is your next assignment: https://colab.research.google.com/drive/1asGptVW1kSOD-sm44G3PJr-Ad9lPwQFR?usp=sharing + +Before you start it, it is preferable to look at some of the popular CNN architectures to get an idea of what has worked for people over the years. Then, you are to design your own model. +Here is a video that will help: https://youtu.be/dZVkygnKh1M + +This week you'll be covering Transfer Learning and Data Augmentation. This will be crucial for our final task of classification. Here are the resources you need to go through: + +Introduction to Data Augmentation: https://youtu.be/JI8saFjK84o +Data Augmentation: https://www.tensorflow.org/tutorials/images/data_augmentation + +Introduction to Transfer Learning: https://youtu.be/FQM13HkEfBk +Transfer Learning and Fine Tuning: https://www.tensorflow.org/tutorials/images/transfer_learning + +Great work on the last assignment! +Now we move on to our final task: Classifying the dataset we decided upon. You'll be using Transfer Learning to do the same. + +Here is the link to the dataset: https://drive.google.com/drive/folders/11TgD1-ouxCP6bd4HWnVvezo5H6c1xS0a?usp=sharing +You are to Make a copy of the folder Mask_Dataset (the above link) + +Then, you are to use a copy of this notebook to write code: https://colab.research.google.com/drive/1-StHlJj6XIfC0fptgdCWJ_OT_11cDP2G?usp=sharing +The rest of the instructions are given in the notebook. Run the pre-written code as it is. If you have any doubts regarding it, feel free to post it here. + +Deadline: 13th July + +We know Y21s have their endsems coming up, therefore we have kept it after your exams. It shouldn't take you that much time. +We are thinking about keeping an optional assignment as well (Y20s could probably do it, more about that later). + +https://www.tensorflow.org/tutorials/load_data/images +TensorFlow +Load and preprocess images | TensorFlow Core +Load and preprocess images | TensorFlow Core +Refer to the above site on how to load and split this data \ No newline at end of file diff --git a/Resources/week1.pdf b/Resources/week1.pdf new file mode 100644 index 0000000..24568ac Binary files /dev/null and b/Resources/week1.pdf differ diff --git a/Resources/week3.pdf b/Resources/week3.pdf new file mode 100644 index 0000000..64bb5cb Binary files /dev/null and b/Resources/week3.pdf differ