{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Liabraries " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2025-06-30T02:00:00.652189Z", "iopub.status.busy": "2025-06-30T02:00:00.651897Z", "iopub.status.idle": "2025-06-30T02:00:14.315724Z", "shell.execute_reply": "2025-06-30T02:00:14.314972Z", "shell.execute_reply.started": "2025-06-30T02:00:00.652168Z" }, "id": "9-X8JZL41VwJ", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-06-30 02:00:03.470880: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1751248803.660838 35 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1751248803.716097 35 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as ticker\n", "import textwrap\n", "import seaborn as sns\n", "import random\n", "import os\n", "import cv2\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, classification_report\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.optimizers import Adam, Adamax\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization,GlobalAveragePooling2D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T02:00:32.250073Z", "iopub.status.busy": "2025-06-30T02:00:32.249133Z", "iopub.status.idle": "2025-06-30T02:00:32.254331Z", "shell.execute_reply": "2025-06-30T02:00:32.253632Z", "shell.execute_reply.started": "2025-06-30T02:00:32.250048Z" }, "id": "M7wuQNSX1VwR", "trusted": true }, "outputs": [], "source": [ "def prepare_data(data_dir):\n", " filepaths=[]\n", " labels=[]\n", " folds=os.listdir(data_dir)\n", " for fold in folds:\n", " foldpath = os.path.join(data_dir, fold)\n", " files=os.listdir(foldpath)\n", " for file in files:\n", " file_path=os.path.join(foldpath,file)\n", " filepaths.append(file_path)\n", " labels.append(fold)\n", " df = pd.DataFrame(data={'filepaths':filepaths, 'labels':labels})\n", " return df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T02:00:36.491115Z", "iopub.status.busy": "2025-06-30T02:00:36.490438Z", "iopub.status.idle": "2025-06-30T02:00:47.244148Z", "shell.execute_reply": "2025-06-30T02:00:47.243363Z", "shell.execute_reply.started": "2025-06-30T02:00:36.491093Z" }, "id": "HHHd53pP1VwS", "trusted": true }, "outputs": [], "source": [ "train_df=prepare_data('/kaggle/input/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/train')\n", "valid_df=prepare_data('/kaggle/input/new-plant-diseases-dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid')\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "execution": { "iopub.execute_input": "2025-06-30T02:00:52.982056Z", "iopub.status.busy": "2025-06-30T02:00:52.981265Z", "iopub.status.idle": "2025-06-30T02:00:53.004943Z", "shell.execute_reply": "2025-06-30T02:00:53.004385Z", "shell.execute_reply.started": "2025-06-30T02:00:52.982023Z" }, "id": "jiUIYWs-1VwS", "outputId": "cf945b6e-5a2d-42bd-d1f7-bd751de0530f", "trusted": true }, "outputs": [ { "data": { "text/html": [ "
| \n", " | filepaths | \n", "labels | \n", "
|---|---|---|
| 0 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 1 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 2 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 3 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 4 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| ... | \n", "... | \n", "... | \n", "
| 70290 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 70291 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 70292 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 70293 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 70294 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
70295 rows × 2 columns
\n", "| \n", " | filepaths | \n", "labels | \n", "
|---|---|---|
| 0 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 1 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 2 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 3 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| 4 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Tomato___Late_blight | \n", "
| ... | \n", "... | \n", "... | \n", "
| 17567 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 17568 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 17569 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 17570 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
| 17571 | \n", "/kaggle/input/new-plant-diseases-dataset/New P... | \n", "Corn_(maize)___healthy | \n", "
17572 rows × 2 columns
\n", "Model: \"sequential_1\"\n",
"\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
"│ inception_resnet_v2 (Functional) │ (None, 8, 8, 1536) │ 54,336,736 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_409 │ (None, 8, 8, 1536) │ 6,144 │\n",
"│ (BatchNormalization) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ global_average_pooling2d_1 │ (None, 1536) │ 0 │\n",
"│ (GlobalAveragePooling2D) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_4 (Dense) │ (None, 256) │ 393,472 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_410 │ (None, 256) │ 1,024 │\n",
"│ (BatchNormalization) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_3 (Dropout) │ (None, 256) │ 0 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_5 (Dense) │ (None, 128) │ 32,896 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_411 │ (None, 128) │ 512 │\n",
"│ (BatchNormalization) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_4 (Dropout) │ (None, 128) │ 0 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_6 (Dense) │ (None, 64) │ 8,256 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_5 (Dropout) │ (None, 64) │ 0 │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_7 (Dense) │ (None, 38) │ 2,470 │\n",
"└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
"\n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
"│ inception_resnet_v2 (\u001b[38;5;33mFunctional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1536\u001b[0m) │ \u001b[38;5;34m54,336,736\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_409 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1536\u001b[0m) │ \u001b[38;5;34m6,144\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ global_average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1536\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m393,472\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_410 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,896\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ batch_normalization_411 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
"│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m38\u001b[0m) │ \u001b[38;5;34m2,470\u001b[0m │\n",
"└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Total params: 54,781,510 (208.97 MB)\n", "\n" ], "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m54,781,510\u001b[0m (208.97 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Trainable params: 54,717,126 (208.73 MB)\n", "\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m54,717,126\u001b[0m (208.73 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Non-trainable params: 64,384 (251.50 KB)\n", "\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m64,384\u001b[0m (251.50 KB)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "base_model = keras.applications.InceptionResNetV2(\n", " include_top=False,\n", " weights=\"imagenet\",\n", " input_shape=(299,299,3),\n", ")\n", "model=Sequential([\n", " base_model,\n", " BatchNormalization(),\n", " GlobalAveragePooling2D(),\n", " Dense(256,activation='relu'),\n", " BatchNormalization(),\n", " Dropout(0.2),\n", " Dense(128,activation='relu'),\n", " BatchNormalization(),\n", " Dropout(0.3),\n", " Dense(64,activation='relu'),\n", " Dropout(0.5),\n", " Dense(38,activation='softmax')\n", "])\n", "model.compile(optimizer=Adamax(learning_rate= 0.001), loss= 'categorical_crossentropy', metrics= ['accuracy'])\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-06-30T02:04:56.735027Z", "iopub.status.busy": "2025-06-30T02:04:56.734379Z", "iopub.status.idle": "2025-06-30T03:29:50.388793Z", "shell.execute_reply": "2025-06-30T03:29:50.388220Z", "shell.execute_reply.started": "2025-06-30T02:04:56.734995Z" }, "id": "AllSBJWT1VwX", "outputId": "89fc4199-bdc2-4d6a-e33b-0bf27131ee01", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1751249220.462055 97 service.cc:148] XLA service 0x7ed0e4003830 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1751249220.462757 97 service.cc:156] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n", "I0000 00:00:1751249231.872425 97 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", "I0000 00:00:1751249283.284014 97 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1495s\u001b[0m 596ms/step - accuracy: 0.6360 - loss: 1.4725 - val_accuracy: 0.9651 - val_loss: 0.3998\n", "Epoch 2/4\n", "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1200s\u001b[0m 546ms/step - accuracy: 0.9591 - loss: 0.1952 - val_accuracy: 0.9876 - val_loss: 0.0595\n", "Epoch 3/4\n", "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1200s\u001b[0m 546ms/step - accuracy: 0.9775 - loss: 0.1045 - val_accuracy: 0.9851 - val_loss: 0.0750\n", "Epoch 4/4\n", "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1198s\u001b[0m 545ms/step - accuracy: 0.9829 - loss: 0.0760 - val_accuracy: 0.9948 - val_loss: 0.0217\n" ] } ], "source": [ "hist=model.fit(train_gen,validation_data= valid_gen,epochs=4,batch_size=32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T03:29:58.661498Z", "iopub.status.busy": "2025-06-30T03:29:58.661214Z", "iopub.status.idle": "2025-06-30T03:35:43.098708Z", "shell.execute_reply": "2025-06-30T03:35:43.098144Z", "shell.execute_reply.started": "2025-06-30T03:29:58.661479Z" }, "id": "aIB42Ow71VwX", "outputId": "da778e1a-b111-4572-be15-39fea771c1c7", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m344s\u001b[0m 157ms/step - accuracy: 0.9978 - loss: 0.0082\n" ] }, { "data": { "text/plain": [ "[0.00814148597419262, 0.9978234767913818]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(train_gen)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T03:35:47.325886Z", "iopub.status.busy": "2025-06-30T03:35:47.325225Z", "iopub.status.idle": "2025-06-30T03:36:29.989095Z", "shell.execute_reply": "2025-06-30T03:36:29.988414Z", "shell.execute_reply.started": "2025-06-30T03:35:47.325867Z" }, "id": "_DT5NdfO1VwX", "outputId": "9b06c091-1538-4d91-b7f4-d034472f628d", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m275/275\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 154ms/step - accuracy: 0.9940 - loss: 0.0260\n" ] }, { "data": { "text/plain": [ "[0.021728582680225372, 0.9947643876075745]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(valid_gen)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T03:36:53.892282Z", "iopub.status.busy": "2025-06-30T03:36:53.892006Z", "iopub.status.idle": "2025-06-30T03:38:15.637294Z", "shell.execute_reply": "2025-06-30T03:38:15.636565Z", "shell.execute_reply.started": "2025-06-30T03:36:53.892262Z" }, "id": "CHdVGFVu1VwX", "outputId": "1468cd92-de8f-481b-91bd-94eeabd8b77e", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m275/275\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 295ms/step - accuracy: 0.9949 - loss: 0.0198\n" ] }, { "data": { "text/plain": [ "[0.022730236873030663, 0.9951058626174927]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(test_gen)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T03:38:18.191562Z", "iopub.status.busy": "2025-06-30T03:38:18.191037Z", "iopub.status.idle": "2025-06-30T03:38:18.195401Z", "shell.execute_reply": "2025-06-30T03:38:18.194647Z", "shell.execute_reply.started": "2025-06-30T03:38:18.191538Z" }, "id": "PabHGYso1VwY", "outputId": "a8921524-de48-494c-861e-e75c8e769654", "trusted": true }, "outputs": [], "source": [ "hist=pd.DataFrame(hist.history)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2025-06-30T03:38:21.439317Z", "iopub.status.busy": "2025-06-30T03:38:21.439044Z", "iopub.status.idle": "2025-06-30T03:38:21.830606Z", "shell.execute_reply": "2025-06-30T03:38:21.829814Z", "shell.execute_reply.started": "2025-06-30T03:38:21.439298Z" }, "id": "QgfTNrky1VwY", "outputId": "1c00ded0-6cb9-4230-ab62-cd9de108c841", "trusted": true }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "