Delete Untitled37.ipynb

Untitled37.ipynb

@@ -1,968 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "5eb63b43-0aff-4a50-9d64-d568bcd8f76a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import cv2\n",
-    "import os\n",
-    "from sklearn.preprocessing import LabelEncoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "2ec6794d-f9bd-4768-96c4-6605aba3dba8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.models import Sequential"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "bd92c9fa-2270-43b5-b09f-16b632b1cbe0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.layers import Dense,Dropout,Flatten,InputLayer,Conv2D,MaxPooling2D,Flatten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ee62cce2-debe-49e2-b81f-9e7e402f8290",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class_label=pd.read_json(\"C:\\\\Users\\\\DELL\\\\Desktop\\\\cnn-explainer-master\\\\cnn-explainer-master\\\\tiny-vgg\\\\data\\\\data\\\\class_dict_10.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e2dd374d-995b-4560-b0c5-40df1f3126bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>n03662601</th>\n",
-       "      <th>n02165456</th>\n",
-       "      <th>n07873807</th>\n",
-       "      <th>n07720875</th>\n",
-       "      <th>n04146614</th>\n",
-       "      <th>n01882714</th>\n",
-       "      <th>n07920052</th>\n",
-       "      <th>n02509815</th>\n",
-       "      <th>n07747607</th>\n",
-       "      <th>n04285008</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <td>lifeboat</td>\n",
-       "      <td>ladybug</td>\n",
-       "      <td>pizza, pizza pie</td>\n",
-       "      <td>bell pepper</td>\n",
-       "      <td>school bus</td>\n",
-       "      <td>koala</td>\n",
-       "      <td>espresso</td>\n",
-       "      <td>lesser panda</td>\n",
-       "      <td>orange</td>\n",
-       "      <td>sports car</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>index</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      n03662601 n02165456         n07873807    n07720875   n04146614  \\\n",
-       "class  lifeboat   ladybug  pizza, pizza pie  bell pepper  school bus   \n",
-       "index         0         1                 2            3           4   \n",
-       "\n",
-       "      n01882714 n07920052     n02509815 n07747607   n04285008  \n",
-       "class     koala  espresso  lesser panda    orange  sports car  \n",
-       "index         5         6             7         8           9  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "2a4e8cd0-94e6-446c-93c1-4b7c3c9984d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cv=[]\n",
-    "fv=[]\n",
-    "for file in os.listdir(r\"C:\\Users\\DELL\\Desktop\\cnn-explainer-master\\cnn-explainer-master\\tiny-vgg\\data\\data\\class_10_train\"):\n",
-    "    for img in os.listdir(r\"C:\\Users\\DELL\\Desktop\\cnn-explainer-master\\cnn-explainer-master\\tiny-vgg\\data\\data\\class_10_train\\{}\\images\".format(file)):\n",
-    "        img=cv2.imread(r\"C:\\Users\\DELL\\Desktop\\cnn-explainer-master\\cnn-explainer-master\\tiny-vgg\\data\\data\\class_10_train\\{}\\images\\{}\".format(file,img))\n",
-    "        fv.append(img)\n",
-    "        cv.append(class_label.loc[\"class\"][file])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "bf694bc9-5b4f-435f-a0ed-68f374554c2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5000"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(fv)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "25c14a98-9baf-4d23-a12c-50ae2f6a07a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_fv=np.asarray(fv)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "f163c2e6-0da1-4263-a7a6-37e17f0d39cf",
-   "metadata": {},
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-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_fv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "23bad3e7-6026-4ce1-a629-ec12d48e3c57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "le=LabelEncoder()\n",
-    "final_cv=le.fit_transform(cv)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "2f2abaff-b6fa-4a92-8ef9-c48aae150bf8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5000, 64, 64, 3)"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_fv.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "47b447df-9923-4229-b956-37a0cb93be0f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "model=Sequential()\n",
-    "model.add(InputLayer(input_shape=(64,64,3)))\n",
-    "\n",
-    "model.add(Conv2D(10,(3,3),strides=(1,1),padding='valid',activation='relu'))\n",
-    "\n",
-    "model.add(Conv2D(10,(3,3),strides=(1,1),padding='valid',activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2),strides=(2,2),padding=\"valid\"))\n",
-    "\n",
-    "model.add(Conv2D(10,(3,3),strides=(1,1),padding='valid',activation='relu'))\n",
-    "\n",
-    "model.add(Conv2D(10,(3,3),strides=(1,1),padding='valid',activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2),strides=(2,2),padding=\"valid\"))\n",
-    "\n",
-    "model.add(Flatten())\n",
-    "\n",
-    "model.add(Dense(10,activation=\"softmax\"))\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "60ac58fd-1da6-4ca2-bcb8-8e20af4fd654",
-   "metadata": {},
-   "outputs": [
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">280</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">910</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">910</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">910</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1690</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,910</span> │\n",
-       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
-       "</pre>\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",
-       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m10\u001b[0m)          │             \u001b[38;5;34m280\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m10\u001b[0m)          │             \u001b[38;5;34m910\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m10\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m10\u001b[0m)          │             \u001b[38;5;34m910\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m10\u001b[0m)          │             \u001b[38;5;34m910\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m10\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1690\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
-       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │          \u001b[38;5;34m16,910\u001b[0m │\n",
-       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19,920</span> (77.81 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,920\u001b[0m (77.81 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19,920</span> (77.81 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,920\u001b[0m (77.81 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "ab6f9579-a47d-4463-914f-5d2cebbb617f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19920"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "16900+(280)+(910*3)+10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "0be3eeba-a46b-4eea-bc96-f2c744a7bb7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer=\"adam\",loss=\"sparse_categorical_crossentropy\",metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "40e68fd9-bd57-4542-ab64-8ce9e1441f3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 23ms/step - accuracy: 0.2080 - loss: 4.6804 - val_accuracy: 0.0000e+00 - val_loss: 19.1830\n",
-      "Epoch 2/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.3782 - loss: 1.6260 - val_accuracy: 0.0000e+00 - val_loss: 26.0851\n",
-      "Epoch 3/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.5056 - loss: 1.3495 - val_accuracy: 0.0000e+00 - val_loss: 41.0336\n",
-      "Epoch 4/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.6231 - loss: 1.0457 - val_accuracy: 0.0000e+00 - val_loss: 40.3504\n",
-      "Epoch 5/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.6812 - loss: 0.9255 - val_accuracy: 0.0000e+00 - val_loss: 52.1076\n",
-      "Epoch 6/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 20ms/step - accuracy: 0.7157 - loss: 0.8099 - val_accuracy: 0.0000e+00 - val_loss: 50.8672\n",
-      "Epoch 7/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.7648 - loss: 0.6788 - val_accuracy: 0.0000e+00 - val_loss: 52.5935\n",
-      "Epoch 8/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.7743 - loss: 0.6353 - val_accuracy: 0.0000e+00 - val_loss: 59.4032\n",
-      "Epoch 9/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.8113 - loss: 0.5625 - val_accuracy: 0.0000e+00 - val_loss: 56.3095\n",
-      "Epoch 10/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.8503 - loss: 0.4576 - val_accuracy: 0.0000e+00 - val_loss: 60.5619\n",
-      "Epoch 11/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.8718 - loss: 0.4068 - val_accuracy: 0.0000e+00 - val_loss: 67.3940\n",
-      "Epoch 12/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.8908 - loss: 0.3511 - val_accuracy: 0.0000e+00 - val_loss: 73.8187\n",
-      "Epoch 13/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step - accuracy: 0.9020 - loss: 0.2951 - val_accuracy: 0.0000e+00 - val_loss: 72.3385\n",
-      "Epoch 14/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.9145 - loss: 0.2679 - val_accuracy: 0.0000e+00 - val_loss: 80.5521\n",
-      "Epoch 15/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.9059 - loss: 0.2770 - val_accuracy: 0.0000e+00 - val_loss: 90.8833\n",
-      "Epoch 16/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step - accuracy: 0.9445 - loss: 0.1843 - val_accuracy: 0.0000e+00 - val_loss: 95.8095\n",
-      "Epoch 17/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 20ms/step - accuracy: 0.9568 - loss: 0.1523 - val_accuracy: 0.0000e+00 - val_loss: 102.6003\n",
-      "Epoch 18/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step - accuracy: 0.9580 - loss: 0.1363 - val_accuracy: 0.0000e+00 - val_loss: 101.0709\n",
-      "Epoch 19/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.9502 - loss: 0.1436 - val_accuracy: 0.0000e+00 - val_loss: 100.0361\n",
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-      "Epoch 21/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.9640 - loss: 0.1122 - val_accuracy: 0.0000e+00 - val_loss: 101.3856\n",
-      "Epoch 22/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.9602 - loss: 0.1128 - val_accuracy: 0.0000e+00 - val_loss: 106.5760\n",
-      "Epoch 23/30\n",
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-      "Epoch 24/30\n",
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-      "Epoch 28/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.9643 - loss: 0.1042 - val_accuracy: 0.0000e+00 - val_loss: 115.8864\n",
-      "Epoch 29/30\n",
-      "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 21ms/step - accuracy: 0.9656 - loss: 0.1140 - val_accuracy: 0.0000e+00 - val_loss: 106.3280\n",
-      "Epoch 30/30\n",
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-    "img1=cv2.imread(r\"C:\\Users\\DELL\\Desktop\\cnn-explainer-master\\cnn-explainer-master\\tiny-vgg\\data\\data\\class_10_val\\val_images\\val_47.JPEG\")"
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