{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "skbKH8VC-g3b" }, "source": [ "[](http://edenlibrary.ai/)" ] }, { "cell_type": "markdown", "metadata": { "id": "INXIJHFS-g3p" }, "source": [ "# Weeds Identification-Transfer Learning-3" ] }, { "cell_type": "markdown", "metadata": { "id": "RP3itMRP-g3r" }, "source": [ "## Instructions\n", "1. Clone the repository.\n", "2. Download the necessary datasets from Eden Repository:\n", " 1. Black nightsade-22/MAY/2019-v1\n", " 2. Tomato-24/MAY/2019-v1\n", "3. Unzip dataset files and remove the zip files.\n", "4. Create a folder called 'eden_data' inside eden_library_notebooks directory.\n", "5. Move the unzipped datasets into this folder.\n", "6. The resulting directory structure should be:\n", " * eden_library_notebooks/\n", " * image_classification/\n", " * weeds_identification-transfer_learning-1.ipynb\n", " * eden_data/\n", " * Tomato-240519-Healthy-zz-V1-20210225103740\n", " * Black nightsade-220519-Weed-zz-V1-20210225102034\n", "7. Install notebook dependencies by running:\n", "
conda env create -f eden_transfer_learning.yml
\n", "8. Open the notebook: jupyter notebook\n", "9. Run the code\n", "\n", "**Note:** If you find any issues while executing the notebook, don't hesitate to open an issue on Github. We will reply you as soon as possible." ] }, { "cell_type": "markdown", "metadata": { "id": "Filuerm4-g3s" }, "source": [ "## Background\n", "\n", "In this notebook, we are going to cover a technique called **Transfer Learning**, which generally refers to a process where a machine learning model is trained on one problem, and afterwards, it is reused in some way on a second (probably) related problem (Bengio, 2012). Specifically, in **deep learning**, this technique is used by training only some layers of the pre-trained network. Its promise is that the training will be more efficient and in the best of the cases the performance will be better compared to a model trained from scratch.\n", "\n", "In **agriculture**, since **weeds** compete with crops in the domain of space, light and nutrients, they are an important problem that can lead to a poorer harvest by farmers. To avoid this, weeds should be removed at every step of the growth, but especially at the initial stages. For that reason, identifying weeds accurately by deep learning has arisen as an important objective. Related to this, we can find the disease detection problem, where transfer learning has also been used. Among the most relevant recent works, we can find:\n", "\n", "**Wang et al., (2017)** used transfer learning in order to obtain the best neural-based method for disease detection in plants. They extended the apple black rot images in the PlantVillage dataset, which were further annotated by botanists with four severity stages as ground truth. Then, they evaluated the performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning. Their best model was the **VGG16** architecture trained with transfer learning, which yielded an overall accuracy of 90.4% on the hold-out test set. In **Mehdipour-Ghazi et al., (2017)**, the authors used the plant datasets of LifeCLEF 2015. Three popular deep learning architectures were evaluated: **GoogLeNet, AlexNet, and VGGNet**. Their best combined system (a combination of GoogleNet and VGGNet) achieved an overall accuracy of 80% on the validation set and an overall inverse rank score of 0.752 on the official test set. In **Suh et al., (2018)**, the authors compared different transfer learning approaches in order to find a suitable approach for weed detection (volunteer potato). Their highest classification accuracy for **AlexNet** was 98.0%. Comparing different networks, their highest classification accuracy was 98.7%, which was obtained with **VGG-19**. Additionally, all scenarios and pre-trained networks were feasible for real-time applications (classification time < 0.1 s). Another relevant study has been performed by **Kounalakis et al., (2019)** where they evaluated transfer learning by a combination of CNN-based feature extraction and linear classifiers to recognize rumex under real-world conditions. Their best system (**Inception_v1**+L2regLogReg) achieved an accuracy of 96.13 with a false positive rate of 3.62. In **Too et al., (2019)**, the authors used transfer learning achieving a performance of 99.75% with the **DenseNet** architecture. Finally, in **Espejo-Garcia et al., (2020)**, authors used transfer learning using agricultural datasets for pre-training neural networks, and afterwards, they fine-tuned the networks for classifying 4 species extracted from the **Eden Platform**. Their maximum performance was 99.54% by using the **Xception** architecture.\n", "\n", "It is important to note that in this notebook, a technique integrating neural-network feature-extraction and \"traditional\" machine learning algorithms is used. This technique was used in **Kounalakis et al., (2019)** and **Espejo-Garcia et al., (2020)** and represents an extension over the previous Eden notebooks:\n", "1. https://github.com/Eden-Library-AI/eden_library_notebooks/blob/master/image_classification/weeds_identification-transfer_learning-1.ipynb\n", "2. https://github.com/Eden-Library-AI/eden_library_notebooks/blob/master/image_classification/weeds_identification-transfer_learning-2.ipynb" ] }, { "cell_type": "markdown", "metadata": { "id": "_ZThKRHkRYBH" }, "source": [ "**UPDATES**\n", "\n", "* ***06/04/2021*** Added learning Curves plots.\n", "* ***28/04/2021*** Changed the file structure, see instructions for details. ( **IMPORTANT** )\n", "* ***28/04/2021*** Upgraded read function to become OS agnostic. It now works for both Windows and Linux machines." ] }, { "cell_type": "markdown", "metadata": { "id": "pXIEhm3--g3v" }, "source": [ "#### Library Imports" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "-3hf2blc-g30" }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import cv2\n", "import os\n", "import csv\n", "import gc\n", "import random\n", "import matplotlib.pyplot as plt\n", "\n", "from tqdm import tqdm\n", "from glob import glob\n", "from pathlib import Path\n", "\n", "import tensorflow as tf \n", "from tensorflow.keras.utils import to_categorical\n", "from tensorflow.keras.applications import *\n", "from tensorflow.keras.layers import Flatten, Dense, Dropout\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint,ReduceLROnPlateau\n", "from tensorflow.keras.applications.xception import preprocess_input\n", "import tensorflow.keras.backend as K\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import f1_score" ] }, { "cell_type": "markdown", "metadata": { "id": "BJpCrgWz-g34" }, "source": [ "#### Auxiliar Functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "SYB9zrcv-g35" }, "outputs": [], "source": [ "def denormalise(values):\n", " # Some functions need 1-d arrays\n", " # This function transform n-dimensional y to 1-d y\n", " y_den = []\n", " for dist in values:\n", " y_den.append(np.argmax(dist))\n", " \n", " return y_den" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "j8yxm5BI-g36" }, "outputs": [], "source": [ "# Function for plotting images.\n", "def plot_sample(X):\n", " # Plotting 6 sample images\n", " nb_rows = 3\n", " nb_cols = 3\n", " fig, axs = plt.subplots(nb_rows, nb_cols, figsize=(6, 6))\n", "\n", " for i in range(0, nb_rows):\n", " for j in range(0, nb_cols):\n", " axs[i, j].xaxis.set_ticklabels([])\n", " axs[i, j].yaxis.set_ticklabels([])\n", " axs[i, j].imshow(X[random.randint(0, X.shape[0]-1)])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "BCAuU0Lc-g37" }, "outputs": [], "source": [ "def read_data(path_list, im_size=(224,224)):\n", " \n", " X = []\n", " y = []\n", " \n", " # Exctract the file-names of the datasets we read and create a label dictionary. \n", " tag2idx = {tag.split(os.path.sep)[-1]:i for i, tag in enumerate(path_list)}\n", " \n", " for path in path_list:\n", " for im_file in tqdm(glob(path + '*/*')): # Read all files in path\n", " try:\n", " # os.path.separator is OS agnostic (either '/' or '\\'),[-2] to grab folder name.\n", " label = im_file.split(os.path.sep)[-2] \n", " im = cv2.imread(im_file) \n", " # Resize to appropriate dimensions.You can try different interpolation methods.\n", " im = cv2.resize(im, im_size,interpolation=cv2.INTER_LINEAR)\n", " # By default OpenCV read with BGR format, return back to RGB.\n", " im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n", " X.append(im)\n", " y.append(tag2idx[label])# Append the label name to y \n", " except Exception as e:\n", " # In case annotations or metadata are found\n", " print(\"Not a picture\")\n", " \n", " X = np.array(X) # Convert list to numpy array.\n", " y = np.eye(len(np.unique(y)))[y].astype(np.uint8)\n", " \n", " return X, y" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "mYJK0CY8-g38" }, "outputs": [], "source": [ "# Callbacks are used for saving the best weights and\n", "# early stopping.\n", "def get_callbacks(weights_file, patience, lr_factor):\n", " return [\n", " # Only save the weights that correspond to the maximum validation accuracy. \n", " ModelCheckpoint(filepath= weights_file,\n", " monitor=\"val_accuracy\",\n", " mode=\"max\",\n", " save_best_only=True, \n", " save_weights_only=True),\n", " # If val_loss doesn't improve for a number of epochs set with 'patience' var \n", " # training will stop to avoid overfitting. \n", " EarlyStopping(monitor=\"val_loss\",\n", " mode=\"min\",\n", " patience = patience,\n", " verbose=1),\n", " # Learning rate is reduced by 'lr_factor' if val_loss stagnates\n", " # for a number of epochs set with 'patience' var. \n", " ReduceLROnPlateau(monitor=\"val_loss\", mode=\"min\",\n", " factor=lr_factor, min_lr=1e-6, patience=patience//2, verbose=1 )]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "P7aAWdPuAC3C" }, "outputs": [], "source": [ "#Plot learning curves for both validation accuracy & loss,\n", "#training accuracy & loss\n", "def plot_training_curves(history):\n", " #Defining the metrics we will plot.\n", " train_acc=history.history['accuracy']\n", " val_acc=history.history['val_accuracy']\n", " train_loss = history.history['loss']\n", " val_loss = history.history['val_loss']\n", " \n", " # Range for the X axis.\n", " epochs = range(len(train_loss))\n", " \n", " fig,axs =plt.subplots(1,2,figsize=(20,10))# Figure size w,h in inches \n", "\n", " # Plotting Loss figures.\n", " plt.rcParams.update({'font.size': 22}) # Configuring font size.\n", " fig = plt.subplot(1,2,1)\n", " plt.plot(epochs,train_loss,c=\"red\",label=\"Training Loss\") # plotting\n", " plt.plot(epochs,val_loss,c=\"blue\",label=\"Validation Loss\")\n", " plt.xlabel(\"Epochs\") # title for x axis\n", " plt.ylabel(\"Loss\") # title for y axis\n", " plt.legend()\n", "\n", " # Plotting Accuracy figures. \n", " fig = plt.subplot(1,2,2)\n", " plt.plot(epochs,train_acc,c=\"red\",label=\"Training Acc\") #plotting\n", " plt.plot(epochs,val_acc,c=\"blue\",label=\"Validation Acc\")\n", " plt.xlabel(\"Epochs\") # title for x axis\n", " plt.ylabel(\"Accuracy\") # title for y axis\n", " plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "id": "5ZZ1C6oH-g3_" }, "source": [ "#### Experimental Constants" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "AdfYlPLm-g4A" }, "outputs": [], "source": [ "INPUT_SHAPE = (224, 224, 3)\n", "IM_SIZE = (224, 224)\n", "EPOCHS = 50\n", "BATCH_SIZE = 48\n", "TEST_SPLIT = 0.15\n", "VAL_SPLIT = 0.15\n", "RANDOM_STATE = 2020\n", "WEIGHTS_FILE = \"weights.h5\"# File that stores updated weights\n", "# Datasets' paths we want to work on.\n", "PATH_LIST = ['eden_data/Tomato-240519-Healthy-zz-V1-20210225103740',\n", " 'eden_data/Black nightsade-220519-Weed-zz-V1-20210225102034']" ] }, { "cell_type": "markdown", "metadata": { "id": "c-lv33vM-g4C" }, "source": [ "#### Reading and showing pictures" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7rH6ROif-g4C", "outputId": "f08d1b19-58bf-47fa-891f-1625904f94a5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 200/200 [04:38<00:00, 1.39s/it]\n", "100%|██████████| 123/123 [00:32<00:00, 3.76it/s]\n" ] } ], "source": [ "i=0\n", "for path in PATH_LIST:\n", " #Define paths in an OS agnostic way.\n", " PATH_LIST[i] = str(Path(Path.cwd()).parents[0].joinpath(path)) \n", " i+=1\n", "X, y = read_data(PATH_LIST, IM_SIZE) " ] }, { "cell_type": "markdown", "metadata": { "id": "C81qc-iU-g4E" }, "source": [ "#### Displaying the samples" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 361 }, "id": "koYBPynx-g4F", "outputId": "89efc3ff-70f7-4aaf-8acb-884bf67b51c6" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_sample(X)" ] }, { "cell_type": "markdown", "metadata": { "id": "WzxjRyJz-g4J" }, "source": [ "#### Architecture Definition: Xception as a feature extractor" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "9gf7n4of-g4J" }, "outputs": [], "source": [ "def get_architecture(y):\n", " feature_extractor = Xception(\n", " weights=\"imagenet\", # Load weights pre-trained on ImageNet.\n", " include_top=False, # Do not include the ImageNet classifier at the top.\n", " input_shape=INPUT_SHAPE) \n", "\n", " # Freeze the base_model,we don't want to update initial weights. \n", " feature_extractor.trainable = False\n", "\n", " # Create new model on top.\n", " x = Dropout(0.2)(feature_extractor.output) # Regularize with dropout.\n", " x = Flatten(name=\"flatten\")(x) # Flattening layer. \n", " x = Dense(units=100, activation=\"relu\")(x) # Add a fully connected layer.\n", " x = Dropout(0.3)(x) # Regularize with dropout.\n", " # Create a Classifier with shape=number_of_training_classes.\n", " out = Dense(units=y.shape[1], \n", " activation=\"softmax\")(x)\n", " # This is the final model. \n", " model = Model(feature_extractor.input, out)\n", "\n", " # Defining a base learning rate for Adam optimizer.\n", " base_learning_rate =0.0001\n", " model.compile(loss=\"categorical_crossentropy\",\n", " optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),\n", " metrics=[\"accuracy\"])\n", " \n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "MGhCQd1n-g4K" }, "source": [ "#### Data preprocessing and dataset splitting among train-val-test sets" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "BOXkgmXg-g4L" }, "outputs": [], "source": [ "X_prep = preprocess_input(X)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X_prep, y,\n", " test_size=TEST_SPLIT, \n", " random_state = RANDOM_STATE)\n", "\n", "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, \n", " test_size=VAL_SPLIT, \n", " random_state = RANDOM_STATE)" ] }, { "cell_type": "markdown", "metadata": { "id": "V9uuRcY1-g4M" }, "source": [ "#### Loading Deep Neural Network and printing its architecture" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "HSxqW11Y-g4M" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"functional_1\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_1 (InputLayer) [(None, 224, 224, 3) 0 \n", "__________________________________________________________________________________________________\n", "block1_conv1 (Conv2D) (None, 111, 111, 32) 864 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "block1_conv1_bn (BatchNormaliza (None, 111, 111, 32) 128 block1_conv1[0][0] \n", "__________________________________________________________________________________________________\n", "block1_conv1_act (Activation) (None, 111, 111, 32) 0 block1_conv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block1_conv2 (Conv2D) (None, 109, 109, 64) 18432 block1_conv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block1_conv2_bn (BatchNormaliza (None, 109, 109, 64) 256 block1_conv2[0][0] \n", "__________________________________________________________________________________________________\n", "block1_conv2_act (Activation) (None, 109, 109, 64) 0 block1_conv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block2_sepconv1 (SeparableConv2 (None, 109, 109, 128 8768 block1_conv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block2_sepconv1_bn (BatchNormal (None, 109, 109, 128 512 block2_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block2_sepconv2_act (Activation (None, 109, 109, 128 0 block2_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block2_sepconv2 (SeparableConv2 (None, 109, 109, 128 17536 block2_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block2_sepconv2_bn (BatchNormal (None, 109, 109, 128 512 block2_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d (Conv2D) (None, 55, 55, 128) 8192 block1_conv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block2_pool (MaxPooling2D) (None, 55, 55, 128) 0 block2_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization (BatchNorma (None, 55, 55, 128) 512 conv2d[0][0] \n", "__________________________________________________________________________________________________\n", "add (Add) (None, 55, 55, 128) 0 block2_pool[0][0] \n", " batch_normalization[0][0] \n", "__________________________________________________________________________________________________\n", "block3_sepconv1_act (Activation (None, 55, 55, 128) 0 add[0][0] \n", "__________________________________________________________________________________________________\n", "block3_sepconv1 (SeparableConv2 (None, 55, 55, 256) 33920 block3_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block3_sepconv1_bn (BatchNormal (None, 55, 55, 256) 1024 block3_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block3_sepconv2_act (Activation (None, 55, 55, 256) 0 block3_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block3_sepconv2 (SeparableConv2 (None, 55, 55, 256) 67840 block3_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block3_sepconv2_bn (BatchNormal (None, 55, 55, 256) 1024 block3_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_1 (Conv2D) (None, 28, 28, 256) 32768 add[0][0] \n", "__________________________________________________________________________________________________\n", "block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 block3_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_1 (BatchNor (None, 28, 28, 256) 1024 conv2d_1[0][0] \n", "__________________________________________________________________________________________________\n", "add_1 (Add) (None, 28, 28, 256) 0 block3_pool[0][0] \n", " batch_normalization_1[0][0] \n", "__________________________________________________________________________________________________\n", "block4_sepconv1_act (Activation (None, 28, 28, 256) 0 add_1[0][0] \n", "__________________________________________________________________________________________________\n", "block4_sepconv1 (SeparableConv2 (None, 28, 28, 728) 188672 block4_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block4_sepconv1_bn (BatchNormal (None, 28, 28, 728) 2912 block4_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block4_sepconv2_act (Activation (None, 28, 28, 728) 0 block4_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block4_sepconv2 (SeparableConv2 (None, 28, 28, 728) 536536 block4_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block4_sepconv2_bn (BatchNormal (None, 28, 28, 728) 2912 block4_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 14, 14, 728) 186368 add_1[0][0] \n", "__________________________________________________________________________________________________\n", "block4_pool (MaxPooling2D) (None, 14, 14, 728) 0 block4_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_2 (BatchNor (None, 14, 14, 728) 2912 conv2d_2[0][0] \n", "__________________________________________________________________________________________________\n", "add_2 (Add) (None, 14, 14, 728) 0 block4_pool[0][0] \n", " batch_normalization_2[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv1_act (Activation (None, 14, 14, 728) 0 add_2[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv1 (SeparableConv2 (None, 14, 14, 728) 536536 block5_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv1_bn (BatchNormal (None, 14, 14, 728) 2912 block5_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv2_act (Activation (None, 14, 14, 728) 0 block5_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv2 (SeparableConv2 (None, 14, 14, 728) 536536 block5_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv2_bn (BatchNormal (None, 14, 14, 728) 2912 block5_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv3_act (Activation (None, 14, 14, 728) 0 block5_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv3 (SeparableConv2 (None, 14, 14, 728) 536536 block5_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block5_sepconv3_bn (BatchNormal (None, 14, 14, 728) 2912 block5_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_3 (Add) (None, 14, 14, 728) 0 block5_sepconv3_bn[0][0] \n", " add_2[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv1_act (Activation (None, 14, 14, 728) 0 add_3[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv1 (SeparableConv2 (None, 14, 14, 728) 536536 block6_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv1_bn (BatchNormal (None, 14, 14, 728) 2912 block6_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv2_act (Activation (None, 14, 14, 728) 0 block6_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv2 (SeparableConv2 (None, 14, 14, 728) 536536 block6_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv2_bn (BatchNormal (None, 14, 14, 728) 2912 block6_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv3_act (Activation (None, 14, 14, 728) 0 block6_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv3 (SeparableConv2 (None, 14, 14, 728) 536536 block6_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block6_sepconv3_bn (BatchNormal (None, 14, 14, 728) 2912 block6_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_4 (Add) (None, 14, 14, 728) 0 block6_sepconv3_bn[0][0] \n", " add_3[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv1_act (Activation (None, 14, 14, 728) 0 add_4[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv1 (SeparableConv2 (None, 14, 14, 728) 536536 block7_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv1_bn (BatchNormal (None, 14, 14, 728) 2912 block7_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv2_act (Activation (None, 14, 14, 728) 0 block7_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv2 (SeparableConv2 (None, 14, 14, 728) 536536 block7_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv2_bn (BatchNormal (None, 14, 14, 728) 2912 block7_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv3_act (Activation (None, 14, 14, 728) 0 block7_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv3 (SeparableConv2 (None, 14, 14, 728) 536536 block7_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block7_sepconv3_bn (BatchNormal (None, 14, 14, 728) 2912 block7_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_5 (Add) (None, 14, 14, 728) 0 block7_sepconv3_bn[0][0] \n", " add_4[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv1_act (Activation (None, 14, 14, 728) 0 add_5[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv1 (SeparableConv2 (None, 14, 14, 728) 536536 block8_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv1_bn (BatchNormal (None, 14, 14, 728) 2912 block8_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv2_act (Activation (None, 14, 14, 728) 0 block8_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv2 (SeparableConv2 (None, 14, 14, 728) 536536 block8_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv2_bn (BatchNormal (None, 14, 14, 728) 2912 block8_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv3_act (Activation (None, 14, 14, 728) 0 block8_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv3 (SeparableConv2 (None, 14, 14, 728) 536536 block8_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block8_sepconv3_bn (BatchNormal (None, 14, 14, 728) 2912 block8_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_6 (Add) (None, 14, 14, 728) 0 block8_sepconv3_bn[0][0] \n", " add_5[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv1_act (Activation (None, 14, 14, 728) 0 add_6[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv1 (SeparableConv2 (None, 14, 14, 728) 536536 block9_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv1_bn (BatchNormal (None, 14, 14, 728) 2912 block9_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv2_act (Activation (None, 14, 14, 728) 0 block9_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv2 (SeparableConv2 (None, 14, 14, 728) 536536 block9_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv2_bn (BatchNormal (None, 14, 14, 728) 2912 block9_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv3_act (Activation (None, 14, 14, 728) 0 block9_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv3 (SeparableConv2 (None, 14, 14, 728) 536536 block9_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block9_sepconv3_bn (BatchNormal (None, 14, 14, 728) 2912 block9_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_7 (Add) (None, 14, 14, 728) 0 block9_sepconv3_bn[0][0] \n", " add_6[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv1_act (Activatio (None, 14, 14, 728) 0 add_7[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 block10_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 block10_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv2_act (Activatio (None, 14, 14, 728) 0 block10_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 block10_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 block10_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv3_act (Activatio (None, 14, 14, 728) 0 block10_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 block10_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block10_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 block10_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_8 (Add) (None, 14, 14, 728) 0 block10_sepconv3_bn[0][0] \n", " add_7[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv1_act (Activatio (None, 14, 14, 728) 0 add_8[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 block11_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 block11_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv2_act (Activatio (None, 14, 14, 728) 0 block11_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 block11_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 block11_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv3_act (Activatio (None, 14, 14, 728) 0 block11_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 block11_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block11_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 block11_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_9 (Add) (None, 14, 14, 728) 0 block11_sepconv3_bn[0][0] \n", " add_8[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv1_act (Activatio (None, 14, 14, 728) 0 add_9[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 block12_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 block12_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv2_act (Activatio (None, 14, 14, 728) 0 block12_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 block12_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 block12_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv3_act (Activatio (None, 14, 14, 728) 0 block12_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 block12_sepconv3_act[0][0] \n", "__________________________________________________________________________________________________\n", "block12_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 block12_sepconv3[0][0] \n", "__________________________________________________________________________________________________\n", "add_10 (Add) (None, 14, 14, 728) 0 block12_sepconv3_bn[0][0] \n", " add_9[0][0] \n", "__________________________________________________________________________________________________\n", "block13_sepconv1_act (Activatio (None, 14, 14, 728) 0 add_10[0][0] \n", "__________________________________________________________________________________________________\n", "block13_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 block13_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block13_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 block13_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block13_sepconv2_act (Activatio (None, 14, 14, 728) 0 block13_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block13_sepconv2 (SeparableConv (None, 14, 14, 1024) 752024 block13_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "block13_sepconv2_bn (BatchNorma (None, 14, 14, 1024) 4096 block13_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 add_10[0][0] \n", "__________________________________________________________________________________________________\n", "block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 block13_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_3 (BatchNor (None, 7, 7, 1024) 4096 conv2d_3[0][0] \n", "__________________________________________________________________________________________________\n", "add_11 (Add) (None, 7, 7, 1024) 0 block13_pool[0][0] \n", " batch_normalization_3[0][0] \n", "__________________________________________________________________________________________________\n", "block14_sepconv1 (SeparableConv (None, 7, 7, 1536) 1582080 add_11[0][0] \n", "__________________________________________________________________________________________________\n", "block14_sepconv1_bn (BatchNorma (None, 7, 7, 1536) 6144 block14_sepconv1[0][0] \n", "__________________________________________________________________________________________________\n", "block14_sepconv1_act (Activatio (None, 7, 7, 1536) 0 block14_sepconv1_bn[0][0] \n", "__________________________________________________________________________________________________\n", "block14_sepconv2 (SeparableConv (None, 7, 7, 2048) 3159552 block14_sepconv1_act[0][0] \n", "__________________________________________________________________________________________________\n", "block14_sepconv2_bn (BatchNorma (None, 7, 7, 2048) 8192 block14_sepconv2[0][0] \n", "__________________________________________________________________________________________________\n", "block14_sepconv2_act (Activatio (None, 7, 7, 2048) 0 block14_sepconv2_bn[0][0] \n", "__________________________________________________________________________________________________\n", "dropout (Dropout) (None, 7, 7, 2048) 0 block14_sepconv2_act[0][0] \n", "__________________________________________________________________________________________________\n", "flatten (Flatten) (None, 100352) 0 dropout[0][0] \n", "__________________________________________________________________________________________________\n", "dense (Dense) (None, 100) 10035300 flatten[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 100) 0 dense[0][0] \n", "__________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 2) 202 dropout_1[0][0] \n", "==================================================================================================\n", "Total params: 30,896,982\n", "Trainable params: 10,035,502\n", "Non-trainable params: 20,861,480\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "model = get_architecture(y)\n", "# Print the whole model Architecture with info about layers.\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "H8S2xoLP-g4N" }, "source": [ "#### Training" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jOWx0BvK-g4O", "outputId": "6ac56d34-092b-4b0e-fbc0-e5c73015bc4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "5/5 [==============================] - 21s 4s/step - loss: 1.0794 - accuracy: 0.6466 - val_loss: 0.6318 - val_accuracy: 0.6190\n", "Epoch 2/50\n", "5/5 [==============================] - 22s 4s/step - loss: 0.3631 - accuracy: 0.8405 - val_loss: 0.1812 - val_accuracy: 0.9524\n", "Epoch 3/50\n", "5/5 [==============================] - 22s 4s/step - loss: 0.1080 - accuracy: 0.9483 - val_loss: 0.0650 - val_accuracy: 0.9762\n", "Epoch 4/50\n", "5/5 [==============================] - 23s 5s/step - loss: 0.0302 - accuracy: 0.9871 - val_loss: 0.0397 - val_accuracy: 1.0000\n", "Epoch 5/50\n", "5/5 [==============================] - 22s 4s/step - loss: 0.0309 - accuracy: 0.9914 - val_loss: 0.0325 - val_accuracy: 0.9762\n", "Epoch 6/50\n", "5/5 [==============================] - 19s 4s/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.0464 - val_accuracy: 0.9762\n", "Epoch 7/50\n", "5/5 [==============================] - 18s 4s/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0614 - val_accuracy: 0.9762\n", "Epoch 8/50\n", "5/5 [==============================] - 19s 4s/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.0598 - val_accuracy: 0.9762\n", "Epoch 9/50\n", "5/5 [==============================] - 19s 4s/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0510 - val_accuracy: 0.9762\n", "Epoch 10/50\n", "5/5 [==============================] - ETA: 0s - loss: 0.0016 - accuracy: 1.0000 \n", "Epoch 00010: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n", "5/5 [==============================] - 25s 5s/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0444 - val_accuracy: 0.9762\n", "Epoch 11/50\n", "5/5 [==============================] - 24s 5s/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0435 - val_accuracy: 0.9762\n", "Epoch 12/50\n", "5/5 [==============================] - 26s 5s/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0437 - val_accuracy: 0.9762\n", "Epoch 13/50\n", "5/5 [==============================] - 27s 5s/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0446 - val_accuracy: 0.9762\n", "Epoch 14/50\n", "5/5 [==============================] - 20s 4s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0463 - val_accuracy: 0.9762\n", "Epoch 15/50\n", "5/5 [==============================] - ETA: 0s - loss: 0.0017 - accuracy: 1.0000\n", "Epoch 00015: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n", "5/5 [==============================] - 20s 4s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0476 - val_accuracy: 0.9762\n", "Epoch 00015: early stopping\n", "Wall time: 6min 38s\n" ] } ], "source": [ "%%time\n", "history=model.fit(X_train, # train data\n", " y_train, # labels\n", " batch_size=BATCH_SIZE,\n", " epochs=EPOCHS,\n", " validation_data=(X_val, y_val),\n", " callbacks=get_callbacks(WEIGHTS_FILE, \n", " EPOCHS//5, \n", " 0.25))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 626 }, "id": "oqLAlxXuBONL", "outputId": "46fa2831-45d4-4e6f-d659-06ef17ae39d1" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Plotting learning curves.\n", "plot_training_curves(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "Nfc6b9Xg-g4Q" }, "source": [ "#### Final Fully-connected approach performance" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Po0h8YmE-g4R", "outputId": "89b293fb-54a5-44fd-92f3-69637fb4b219" }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load optimal weights computed during training.\n", "model.load_weights(WEIGHTS_FILE)\n", "# Make precictions on test set and print accuracy.\n", "f1_score(denormalise(y_test), \n", " denormalise(model.predict(X_test)),\n", " average='micro')" ] }, { "cell_type": "markdown", "metadata": { "id": "A-MCn3D2-g4S" }, "source": [ "#### Classifier Replacement -> SVM" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "auNTC0-Y-g4T" }, "outputs": [], "source": [ "# Extracting the features with the deep network\n", "inp = model.input\n", "out = model.get_layer('flatten').output\n", "feature_extractor = Model(inp, out)\n", "train_features = feature_extractor.predict(X_train)\n", "test_features = feature_extractor.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "xo8FW-Rg-g4U" }, "outputs": [], "source": [ "# Defining hyper-parameter search for SVM\n", "hyper_parameters = {'kernel':('linear', 'rbf'), \n", " 'C':[0.1, 1, 10]}\n", "# More info about metrics: sklearn.metrics.SCORERS.keys()\n", "svm = GridSearchCV(SVC(), \n", " hyper_parameters,\n", " refit=True,\n", " n_jobs=-1,\n", " scoring=\"f1_micro\")" ] }, { "cell_type": "markdown", "metadata": { "id": "g9P6-Ni3-g4V" }, "source": [ "#### Performing Hyper-parameter tuning" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R5Aapg9R-g4W", "outputId": "0de91c18-59f8-416e-f1a9-4e5568553c40" }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(estimator=SVC(), n_jobs=-1,\n", " param_grid={'C': [0.1, 1, 10], 'kernel': ('linear', 'rbf')},\n", " scoring='f1_micro')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svm.fit(train_features, denormalise(y_train))" ] }, { "cell_type": "markdown", "metadata": { "id": "jiWcVjqJ-g4X" }, "source": [ "#### Final SVM performance" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z9tiyxGp-g4Y", "outputId": "c6a434cf-5cf1-4dc0-e607-096d1ee825ee" }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1_score(denormalise(y_test), \n", " svm.predict(test_features),\n", " average='micro')" ] }, { "cell_type": "markdown", "metadata": { "id": "uECLrIHS-g4Z" }, "source": [ "#### Classifier Replacement -> Logistic Regression" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "tqnRvv2W-g4a" }, "outputs": [], "source": [ "# Defining hyper-parameter search for Logistic Regression\n", "hyper_parameters = {'penalty':('l1', 'l2'), \n", " 'C':[0.1, 1, 10]}\n", "log = GridSearchCV(LogisticRegression(solver=\"saga\",\n", " penalty=\"elasticnet\",\n", " l1_ratio = 0.5,\n", " multi_class=\"multinomial\"),\n", " hyper_parameters,\n", " refit=True,\n", " n_jobs = -1,\n", " scoring=\"f1_micro\")" ] }, { "cell_type": "markdown", "metadata": { "id": "rv9DnSux-g4a" }, "source": [ "#### Performing Hyper-parameter tuning" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_j8HoWcg-g4b", "outputId": "1fad2a7f-271d-4427-b98f-c4bf049be70d" }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(estimator=LogisticRegression(l1_ratio=0.5,\n", " multi_class='multinomial',\n", " penalty='elasticnet', solver='saga'),\n", " n_jobs=-1, param_grid={'C': [0.1, 1, 10], 'penalty': ('l1', 'l2')},\n", " scoring='f1_micro')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log.fit(train_features, denormalise(y_train))" ] }, { "cell_type": "markdown", "metadata": { "id": "1OHX5sxi-g4c" }, "source": [ "#### Final Logistic performance" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fgui1HNt-g4d", "outputId": "e7b3f609-dc3e-4fa5-ace3-daa5ef30cddc" }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1_score(denormalise(y_test), \n", " log.predict(test_features),\n", " average='micro')" ] }, { "cell_type": "markdown", "metadata": { "id": "OcOk3pYR-g4e" }, "source": [ "## Possible Extensions\n", "1. Use a different pre-trained network (Xception in this notebook).\n", "2. Try a different transfer learning schema instead of a feature extractor approach.\n", "3. Evaluate the model with other metrics.\n", "4. Try different dropout rates and neurons in the fully-connected part of the network.\n", "5. Try different epochs and batch sizes." ] }, { "cell_type": "markdown", "metadata": { "id": "ZmfylocJ-g4e" }, "source": [ "## Bibliography\n", "Bengio, Y., 2012. Deep Learning of Representations for Unsupervised and Transfer Learning. In: Journal of Machine Learning Research; 17–37.\n", "\n", "Wang, G., Sun, Y., Wang, J., (2017). Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience; 2017:8.\n", "\n", "Mehdipour-Ghazi, M., Yanikoglu, B.A., & Aptoula, E. (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 235, 228-235.\n", "\n", "Suh, H.K., IJsselmuiden, J., Hofstee, J.W., van Henten, E.J., (2018). Transfer learning for the classification of sugar beet and volunteer potato under field conditions. Biosystems Engineering; 174:50–65.\n", "\n", "Kounalakis T., Triantafyllidis G. A., Nalpantidis L., (2019). Deep learning-based visual recognition of rumex for robotic precision farming. Computers and Electronics in Agriculture.\n", "\n", "Too, E.C., Yujian, L., Njuki, S., & Ying-chun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric., 161, 272-279.\n", "\n", "Espejo-Garcia, B., Mylonas, N., Athanasakos, L., & Fountas, S., (2020). Improving\n", "Weeds Identification with a Repository of Agricultural Pre-trained Deep Neural\n", "Networks. Computers and Electronics in Agriculture; 175 (August)." ] } ], "metadata": { "accelerator": "TPU", "colab": { "collapsed_sections": [], "name": "weeds_identification-transfer_learning-3.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 1 }