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22_23-J 65
22_23-J 65
Commit b093e4e1 authored by Warnasooriya M.D.S.'s avatar Warnasooriya M.D.S.
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Models evaluated

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Backend/SF__Dinuka_Pest_Damages_identification_V01.ipynb
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......@@ -9,6 +9,456 @@
"# 🟢 Getting the Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KG86cI4E5fi0"
},
"source": [
"# 🟢 Visualize the Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r6jZtph2HCDv"
},
"source": [
"# Model - 01"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e_l--HlzDmE_"
},
"source": [
"# 🟢 ResNetModel"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XfPJz4PK_czR"
},
"source": [
"# 🟢 Evaluating Models "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kwgfR8nzq50J"
},
"outputs": [],
"source": [
"#Plot the validation and training curves separately\n",
"def plot_loss_curve(history):\n",
" '''\n",
" Return separate loss curves for training and validation metrics\n",
" '''\n",
" loss = history.history[\"loss\"]\n",
" val_loss = history.history[\"val_loss\"]\n",
"\n",
" accuracy = history.history[\"accuracy\"]\n",
" val_accuracy = history.history[\"val_accuracy\"]\n",
"\n",
" #get the number of epochs that we run for\n",
" epochs = range(len(history.history[\"loss\"]))\n",
"\n",
" #Plot the lost\n",
" plt.plot(epochs , loss , label=\"Training Loss\")\n",
" plt.plot(epochs , val_loss , label=\"Validation Loss\")\n",
" plt.title(\"Loss\")\n",
" plt.xlabel(\"Epochs\")\n",
" plt.legend()\n",
"\n",
" #Plot the accuracy\n",
" plt.figure()\n",
" plt.plot(epochs , accuracy , label=\"Training accuracy\")\n",
" plt.plot(epochs , val_accuracy , label=\"Validation accuracy\")\n",
" plt.title(\"accuracy\")\n",
" plt.xlabel(\"Epochs\")\n",
" plt.legend()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 573
},
"id": "8WMvE6vEq-n8",
"outputId": "56adfdbb-d619-4501-96fd-755f64e25a25"
},
"outputs": [],
"source": [
"plot_loss_curve(resnet_hist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6fAPtZLRqmaR"
},
"outputs": [],
"source": [
"#Import Dependencies\n",
"import tensorflow as tf\n",
"import tensorflow_hub as hub\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.layers import Conv2D , MaxPool2D , Dense , Flatten"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mEfKr1tLqqiz"
},
"outputs": [],
"source": [
"def create_model(model_url , num_classes=3):\n",
"\n",
" feature_extractor_layer = hub.KerasLayer(model_url,\n",
" trainable = False, #freeze the already learned patterns \n",
" name = \"feature_extraction_layer\",\n",
" input_shape = (448, 448,3)) \n",
" #Create our own model\n",
" model = tf.keras.Sequential([\n",
" feature_extractor_layer,\n",
" \n",
" layers.Dense(num_classes , activation=\"softmax\" , name=\"output_layer\")\n",
" ])\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CF7QWBeMxXo6"
},
"outputs": [],
"source": [
"resnet_url = \"https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QSnaA4Qbqt2b",
"outputId": "cfdfc8ab-acc9-4811-f8c7-22b08d11c22b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n",
"69/69 [==============================] - 62s 827ms/step - loss: 0.7628 - accuracy: 0.6727 - val_loss: 0.8452 - val_accuracy: 0.6351\n",
"Epoch 2/15\n",
"69/69 [==============================] - 53s 772ms/step - loss: 0.5198 - accuracy: 0.7794 - val_loss: 0.8006 - val_accuracy: 0.6419\n",
"Epoch 3/15\n",
"69/69 [==============================] - 53s 767ms/step - loss: 0.4339 - accuracy: 0.8332 - val_loss: 0.6867 - val_accuracy: 0.7095\n",
"Epoch 4/15\n",
"69/69 [==============================] - 53s 769ms/step - loss: 0.3879 - accuracy: 0.8578 - val_loss: 0.7505 - val_accuracy: 0.6892\n",
"Epoch 5/15\n",
"69/69 [==============================] - 57s 824ms/step - loss: 0.3395 - accuracy: 0.8760 - val_loss: 0.6813 - val_accuracy: 0.7365\n",
"Epoch 6/15\n",
"69/69 [==============================] - 54s 784ms/step - loss: 0.3365 - accuracy: 0.8715 - val_loss: 0.7276 - val_accuracy: 0.7095\n",
"Epoch 7/15\n",
"69/69 [==============================] - 54s 780ms/step - loss: 0.3101 - accuracy: 0.8888 - val_loss: 0.6131 - val_accuracy: 0.7703\n",
"Epoch 8/15\n",
"69/69 [==============================] - 53s 769ms/step - loss: 0.2798 - accuracy: 0.9061 - val_loss: 0.7144 - val_accuracy: 0.7297\n",
"Epoch 9/15\n",
"69/69 [==============================] - 54s 774ms/step - loss: 0.2568 - accuracy: 0.9088 - val_loss: 0.6302 - val_accuracy: 0.7838\n",
"Epoch 10/15\n",
"69/69 [==============================] - 57s 822ms/step - loss: 0.2506 - accuracy: 0.9180 - val_loss: 0.6371 - val_accuracy: 0.7770\n",
"Epoch 11/15\n",
"69/69 [==============================] - 54s 778ms/step - loss: 0.2318 - accuracy: 0.9216 - val_loss: 0.7219 - val_accuracy: 0.7432\n",
"Epoch 12/15\n",
"69/69 [==============================] - 53s 771ms/step - loss: 0.2210 - accuracy: 0.9289 - val_loss: 0.6702 - val_accuracy: 0.7770\n",
"Epoch 13/15\n",
"69/69 [==============================] - 54s 774ms/step - loss: 0.2247 - accuracy: 0.9307 - val_loss: 0.7111 - val_accuracy: 0.7703\n",
"Epoch 14/15\n",
"69/69 [==============================] - 57s 824ms/step - loss: 0.2071 - accuracy: 0.9417 - val_loss: 0.6763 - val_accuracy: 0.7770\n",
"Epoch 15/15\n",
"69/69 [==============================] - 54s 780ms/step - loss: 0.1968 - accuracy: 0.9362 - val_loss: 0.6465 - val_accuracy: 0.7973\n"
]
}
],
"source": [
"#Create Resnet Model\n",
"resnet_model = create_model(resnet_url , \n",
" num_classes = 3)\n",
"#Compile our resnet model\n",
"resnet_model.compile(loss='categorical_crossentropy',\n",
" optimizer = tf.keras.optimizers.Adam(),\n",
" metrics=[\"accuracy\"])\n",
"#Fitting the model\n",
"resnet_hist = resnet_model.fit(train_data,\n",
" epochs=15,\n",
" steps_per_epoch=len(train_data),\n",
" validation_data = test_data,\n",
" validation_steps = len(test_data)\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B-TvvNxe6A5l"
},
"source": [
"## 🟢 Pre-process Data "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "anWDmrMLHlHm",
"outputId": "fc45713a-ab85-4dc8-a9c3-2fd473e991b7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1097 images belonging to 3 classes.\n",
"Found 148 images belonging to 3 classes.\n"
]
}
],
"source": [
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"train_dir = \"Damage Data/Traning\"\n",
"test_dir = \"Damage Data/Testing\"\n",
"\n",
"#Normalize the data \n",
"train_datagen = ImageDataGenerator(rescale = 1/255.,\n",
" rotation_range = 0.2, #how much do you want to rotate an image\n",
" shear_range = 0.3,\n",
" zoom_range = 0.3, \n",
" width_shift_range=0.25,\n",
" height_shift_range=0.25,\n",
" horizontal_flip=True) \n",
"test_datagen = ImageDataGenerator(rescale = 1/255.)\n",
"#Loading the images from the directories\n",
"train_data = train_datagen.flow_from_directory(directory=train_dir,\n",
" target_size = (448, 448),\n",
" class_mode='categorical',\n",
" batch_size = 16,\n",
" shuffle=True)\n",
"\n",
"test_data = test_datagen.flow_from_directory(directory=test_dir,\n",
" target_size = (448, 448),\n",
" batch_size = 16,\n",
" class_mode = \"categorical\",\n",
" shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "V1riWehswsza",
"outputId": "662c175c-5f7a-45cc-fd7b-6569f5041a50"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.8617 - accuracy: 0.4622\n",
"Epoch 1: loss improved from inf to 1.86165, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 69s 958ms/step - loss: 1.8617 - accuracy: 0.4622 - val_loss: 3.9329 - val_accuracy: 0.3851\n",
"Epoch 2/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.5739 - accuracy: 0.5059\n",
"Epoch 2: loss improved from 1.86165 to 1.57390, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 65s 943ms/step - loss: 1.5739 - accuracy: 0.5059 - val_loss: 2.1447 - val_accuracy: 0.4324\n",
"Epoch 3/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.3094 - accuracy: 0.5397\n",
"Epoch 3: loss improved from 1.57390 to 1.30940, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 63s 907ms/step - loss: 1.3094 - accuracy: 0.5397 - val_loss: 1.2881 - val_accuracy: 0.3514\n",
"Epoch 4/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.2277 - accuracy: 0.5624\n",
"Epoch 4: loss improved from 1.30940 to 1.22773, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 64s 920ms/step - loss: 1.2277 - accuracy: 0.5624 - val_loss: 1.0698 - val_accuracy: 0.4730\n",
"Epoch 5/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.1769 - accuracy: 0.5469\n",
"Epoch 5: loss improved from 1.22773 to 1.17689, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 65s 936ms/step - loss: 1.1769 - accuracy: 0.5469 - val_loss: 1.5349 - val_accuracy: 0.3446\n",
"Epoch 6/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.0909 - accuracy: 0.5606\n",
"Epoch 6: loss improved from 1.17689 to 1.09090, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 63s 919ms/step - loss: 1.0909 - accuracy: 0.5606 - val_loss: 1.1054 - val_accuracy: 0.6014\n",
"Epoch 7/10\n",
"69/69 [==============================] - ETA: 0s - loss: 1.0655 - accuracy: 0.5679\n",
"Epoch 7: loss improved from 1.09090 to 1.06552, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 66s 962ms/step - loss: 1.0655 - accuracy: 0.5679 - val_loss: 0.9427 - val_accuracy: 0.5743\n",
"Epoch 8/10\n",
"69/69 [==============================] - ETA: 0s - loss: 0.9755 - accuracy: 0.6053\n",
"Epoch 8: loss improved from 1.06552 to 0.97551, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 64s 924ms/step - loss: 0.9755 - accuracy: 0.6053 - val_loss: 1.6016 - val_accuracy: 0.3649\n",
"Epoch 9/10\n",
"69/69 [==============================] - ETA: 0s - loss: 0.9616 - accuracy: 0.6080\n",
"Epoch 9: loss improved from 0.97551 to 0.96164, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 64s 928ms/step - loss: 0.9616 - accuracy: 0.6080 - val_loss: 1.2331 - val_accuracy: 0.6216\n",
"Epoch 10/10\n",
"69/69 [==============================] - ETA: 0s - loss: 0.9172 - accuracy: 0.6427\n",
"Epoch 10: loss improved from 0.96164 to 0.91717, saving model to /content/saved_models/pest_damages.h5\n",
"69/69 [==============================] - 67s 964ms/step - loss: 0.9172 - accuracy: 0.6427 - val_loss: 0.9896 - val_accuracy: 0.5338\n"
]
}
],
"source": [
"height = 448\n",
"width = 448\n",
"depth = 3\n",
"n_classes = 3\n",
"\n",
"\n",
"#Create the model\n",
"model = Sequential()\n",
"inputShape = (height, width, depth)\n",
"chanDim = -1\n",
"if K.image_data_format() == \"channels_first\":\n",
" inputShape = (depth, height, width)\n",
" chanDim = 1\n",
"model.add(Conv2D(32, (3, 3), padding=\"same\",input_shape=inputShape))\n",
"model.add(Activation(\"relu\"))\n",
"model.add(BatchNormalization(axis=chanDim))\n",
"model.add(MaxPooling2D(pool_size=(3, 3)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
"model.add(Activation(\"relu\"))\n",
"model.add(BatchNormalization(axis=chanDim))\n",
"model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
"model.add(Activation(\"relu\"))\n",
"model.add(BatchNormalization(axis=chanDim))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
"model.add(Activation(\"relu\"))\n",
"model.add(BatchNormalization(axis=chanDim))\n",
"model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
"model.add(Activation(\"relu\"))\n",
"model.add(BatchNormalization(axis=chanDim))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"model.add(Flatten())\n",
"model.add(Dense(1024))\n",
"model.add(Activation(\"relu\"))\n",
"model.add(BatchNormalization())\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(n_classes))\n",
"model.add(Activation(\"softmax\"))\n",
"\n",
"#Compile the model\n",
"model.compile(loss = tf.keras.losses.CategoricalCrossentropy(),\n",
" optimizer = tf.keras.optimizers.Adam(),\n",
" metrics = ['accuracy'])\n",
"fle_s = '/content/saved_models/pest_damages.h5'\n",
"checkpointer = ModelCheckpoint(fle_s, monitor='loss', verbose=1, save_best_only=True,\n",
" save_weights_only=False, mode='auto', save_freq='epoch')\n",
"callback_list = [checkpointer]\n",
"\n",
"model_history = model.fit(train_data,\n",
" epochs=10,\n",
" batch_size=16,\n",
" steps_per_epoch=len(train_data),\n",
" validation_data = test_data,\n",
" validation_steps = len(test_data),\n",
" callbacks=[callback_list]\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7zzt0QSS5h7u"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import random\n",
"import os\n",
"\n",
"def view_random_image(target_dir , target_class):\n",
" #Setup the target directory (we will view images from here)\n",
" target_folder = target_dir+\"/\"+target_class\n",
"\n",
" #Get a random image path\n",
" random_image = random.sample(os.listdir(target_folder) ,1)\n",
"\n",
" #Read in the image and plotted using the matplotlib\n",
" img = mpimg.imread(target_folder+\"/\"+random_image[0])\n",
" plt.imshow(img)\n",
" plt.title(target_class)\n",
" plt.axis(\"off\")\n",
"\n",
" print(f\"Image Shape:{img.shape}\") #Show the shape of the image\n",
"\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "2iCuwzRz5n7O",
"outputId": "b6a48458-365f-489c-beea-d17fde5496ae"
},
"outputs": [],
"source": [
"img = view_random_image(target_dir=\"Damage Data/Traning\" , target_class=\"Thrips\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "mTkn0s0t7cPb",
"outputId": "9861f6d0-0265-46e4-8ef9-f7f4f37ec186"
},
"outputs": [],
"source": [
"#Visualize Data\n",
"plt.figure()\n",
"plt.subplot(1 , 2 ,1)\n",
"img = view_random_image(target_dir=\"Damage Data/Traning\" , target_class=\"Thrips\")\n",
"plt.subplot(1 , 2, 2)\n",
"img = view_random_image(target_dir=\"Damage Data/Traning\" , target_class=\"Mealybug\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
......@@ -44,15 +494,6 @@
"from keras import backend as K"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KG86cI4E5fi0"
},
"source": [
"# 🟢 Visualize the Data"
]
},
{
"cell_type": "code",
"execution_count": 13,
......@@ -86,7 +527,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
......@@ -95,28 +536,7 @@
"id": "mTkn0s0t7cPb",
"outputId": "9861f6d0-0265-46e4-8ef9-f7f4f37ec186"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image Shape:(513, 767, 3)\n",
"Image Shape:(349, 489, 3)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"#Visualize Data\n",
"plt.figure()\n",
......@@ -126,24 +546,6 @@
"img = view_random_image(target_dir=\"Damage Data/Traning\" , target_class=\"Mealybug\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Model - 01"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"🟢 Pre-process Data"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -318,6 +720,60 @@
" validation_steps = len(test_data)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kwgfR8nzq50J"
},
"outputs": [],
"source": [
"#Plot the validation and training curves separately\n",
"def plot_loss_curve(history):\n",
" '''\n",
" Return separate loss curves for training and validation metrics\n",
" '''\n",
" loss = history.history[\"loss\"]\n",
" val_loss = history.history[\"val_loss\"]\n",
"\n",
" accuracy = history.history[\"accuracy\"]\n",
" val_accuracy = history.history[\"val_accuracy\"]\n",
"\n",
" #get the number of epochs that we run for\n",
" epochs = range(len(history.history[\"loss\"]))\n",
"\n",
" #Plot the lost\n",
" plt.plot(epochs , loss , label=\"Training Loss\")\n",
" plt.plot(epochs , val_loss , label=\"Validation Loss\")\n",
" plt.title(\"Loss\")\n",
" plt.xlabel(\"Epochs\")\n",
" plt.legend()\n",
"\n",
" #Plot the accuracy\n",
" plt.figure()\n",
" plt.plot(epochs , accuracy , label=\"Training accuracy\")\n",
" plt.plot(epochs , val_accuracy , label=\"Validation accuracy\")\n",
" plt.title(\"accuracy\")\n",
" plt.xlabel(\"Epochs\")\n",
" plt.legend()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 573
},
"id": "8WMvE6vEq-n8",
"outputId": "56adfdbb-d619-4501-96fd-755f64e25a25"
},
"outputs": [],
"source": [
"plot_loss_curve(resnet_hist)"
]
}
],
"metadata": {
......
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