From 8e4927d3ea73dfdf830216425a5885a893d4c45b Mon Sep 17 00:00:00 2001
From: s444519 <mardru@st.amu.edu.pl>
Date: Tue, 7 Jun 2022 22:42:35 +0200
Subject: [PATCH] ok

---
 Untitled.ipynb    |  80 +++++++++++-----------
 Untitled1.ipynb   |  33 ---------
 keras_class.ipynb | 170 ++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 210 insertions(+), 73 deletions(-)
 delete mode 100644 Untitled1.ipynb
 create mode 100644 keras_class.ipynb

diff --git a/Untitled.ipynb b/Untitled.ipynb
index 96a14ca..acc0a3e 100644
--- a/Untitled.ipynb
+++ b/Untitled.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 54,
    "id": "f902472d",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 55,
    "id": "2324a8dd",
    "metadata": {},
    "outputs": [],
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 56,
    "id": "e4ba4b52",
    "metadata": {},
    "outputs": [
@@ -144,7 +144,7 @@
        "9  2017.005479  20170103  Sinn Féin warns Stormont may collapse over 'ca..."
       ]
      },
-     "execution_count": 30,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 57,
    "id": "d4a64cb8",
    "metadata": {},
    "outputs": [
@@ -264,7 +264,7 @@
        "9  2012.791781  20121016  UK investigation into Icelandic bank fraud aba..."
       ]
      },
-     "execution_count": 31,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 58,
    "id": "1221baee",
    "metadata": {},
    "outputs": [
@@ -384,7 +384,7 @@
        "9  2005.569863  20050728     IRA must hand over criminal assets - McDowell"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 59,
    "id": "58cb7b89",
    "metadata": {},
    "outputs": [
@@ -482,7 +482,7 @@
        "9   news"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -493,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 60,
    "id": "cfb113b6",
    "metadata": {},
    "outputs": [],
@@ -506,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 61,
    "id": "046f00be",
    "metadata": {},
    "outputs": [
@@ -516,7 +516,7 @@
        "'Sudan claims it is disarming militias'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 62,
    "id": "9d36394d",
    "metadata": {},
    "outputs": [
@@ -548,7 +548,7 @@
        "Name: 2, Length: 1186898, dtype: object"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -559,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 63,
    "id": "58d6e666",
    "metadata": {},
    "outputs": [
@@ -570,7 +570,7 @@
        "       'removed'], dtype=object)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 64,
    "id": "86d6f712",
    "metadata": {},
    "outputs": [],
@@ -594,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 65,
    "id": "4491cae8",
    "metadata": {},
    "outputs": [
@@ -651,7 +651,7 @@
        "3  3"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -662,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 66,
    "id": "6eccbc39",
    "metadata": {},
    "outputs": [],
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 67,
    "id": "e09e6a3f",
    "metadata": {},
    "outputs": [],
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 68,
    "id": "f0e4b5fc",
    "metadata": {},
    "outputs": [
@@ -692,7 +692,7 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 69,
    "id": "7662ca93",
    "metadata": {},
    "outputs": [
@@ -713,7 +713,7 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -724,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 70,
    "id": "a1838cd6",
    "metadata": {},
    "outputs": [
@@ -1734,7 +1734,7 @@
        " ...]"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1745,7 +1745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 71,
    "id": "3eedae48",
    "metadata": {},
    "outputs": [],
@@ -1756,7 +1756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 72,
    "id": "adc7bcd0",
    "metadata": {},
    "outputs": [],
@@ -1769,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 73,
    "id": "2b9ce936",
    "metadata": {},
    "outputs": [],
@@ -1781,7 +1781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 74,
    "id": "cef5f0c2",
    "metadata": {},
    "outputs": [
@@ -1800,7 +1800,7 @@
        " '| headline: UK investigation into Icelandic bank fraud abandoned']"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1811,7 +1811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 75,
    "id": "062f0bd1",
    "metadata": {},
    "outputs": [
@@ -1824,7 +1824,7 @@
        " \"| headline: Those who can't\"]"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1835,7 +1835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 76,
    "id": "f20d5d1d",
    "metadata": {},
    "outputs": [],
@@ -1854,7 +1854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 77,
    "id": "4c68c041",
    "metadata": {},
    "outputs": [],
@@ -1864,7 +1864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 78,
    "id": "9da03434",
    "metadata": {},
    "outputs": [],
@@ -1874,7 +1874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 79,
    "id": "f8d5471d",
    "metadata": {},
    "outputs": [],
@@ -1885,7 +1885,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "948f6088",
+   "id": "37353752",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/Untitled1.ipynb b/Untitled1.ipynb
deleted file mode 100644
index 298ffbf..0000000
--- a/Untitled1.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "977d76ed",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/keras_class.ipynb b/keras_class.ipynb
new file mode 100644
index 0000000..083152d
--- /dev/null
+++ b/keras_class.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e3e0aed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv2D, MaxPooling2D\n",
+    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
+    "from keras import backend as K\n",
+    " \n",
+    "img_width, img_height = 224, 224"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "976c0dc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data_dir = 'v_data/train'\n",
+    "validation_data_dir = 'v_data/test'\n",
+    "nb_train_samples =400\n",
+    "nb_validation_samples = 100\n",
+    "epochs = 10\n",
+    "batch_size = 16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9bf78481",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check format\n",
+    "\n",
+    "\n",
+    "if K.image_data_format() == 'channels_first':\n",
+    "    input_shape = (3, img_width, img_height)\n",
+    "else:\n",
+    "    input_shape = (img_width, img_height, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a0663374",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, (2, 2), input_shape=input_shape))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    " \n",
+    "model.add(Conv2D(32, (2, 2)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    " \n",
+    "model.add(Conv2D(64, (2, 2)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    " \n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(64))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(1))\n",
+    "model.add(Activation('sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6fadd7e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0bb1a7ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[WinError 3] The system cannot find the path specified: 'v_data/train'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m train_datagen \u001b[38;5;241m=\u001b[39m ImageDataGenerator(\n\u001b[0;32m      2\u001b[0m     rescale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m,\n\u001b[0;32m      3\u001b[0m     shear_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m      4\u001b[0m     zoom_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m      5\u001b[0m     horizontal_flip\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      7\u001b[0m test_datagen \u001b[38;5;241m=\u001b[39m ImageDataGenerator(rescale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m train_generator \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_datagen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflow_from_directory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_data_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mimg_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg_height\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbinary\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     15\u001b[0m validation_generator \u001b[38;5;241m=\u001b[39m test_datagen\u001b[38;5;241m.\u001b[39mflow_from_directory(\n\u001b[0;32m     16\u001b[0m     validation_data_dir,\n\u001b[0;32m     17\u001b[0m     target_size\u001b[38;5;241m=\u001b[39m(img_width, img_height),\n\u001b[0;32m     18\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[0;32m     19\u001b[0m     class_mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     21\u001b[0m model\u001b[38;5;241m.\u001b[39mfit_generator(\n\u001b[0;32m     22\u001b[0m     train_generator,\n\u001b[0;32m     23\u001b[0m     steps_per_epoch\u001b[38;5;241m=\u001b[39mnb_train_samples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m batch_size,\n\u001b[0;32m     24\u001b[0m     epochs\u001b[38;5;241m=\u001b[39mepochs,\n\u001b[0;32m     25\u001b[0m     validation_data\u001b[38;5;241m=\u001b[39mvalidation_generator,\n\u001b[0;32m     26\u001b[0m     validation_steps\u001b[38;5;241m=\u001b[39mnb_validation_samples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m batch_size)\n",
+      "File \u001b[1;32m~\\.conda\\envs\\py\\lib\\site-packages\\keras\\preprocessing\\image.py:1469\u001b[0m, in \u001b[0;36mImageDataGenerator.flow_from_directory\u001b[1;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio)\u001b[0m\n\u001b[0;32m   1386\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mflow_from_directory\u001b[39m(\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1387\u001b[0m                         directory,\n\u001b[0;32m   1388\u001b[0m                         target_size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m256\u001b[39m, \u001b[38;5;241m256\u001b[39m),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1400\u001b[0m                         interpolation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnearest\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1401\u001b[0m                         keep_aspect_ratio\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m   1402\u001b[0m   \u001b[38;5;124;03m\"\"\"Takes the path to a directory & generates batches of augmented data.\u001b[39;00m\n\u001b[0;32m   1403\u001b[0m \n\u001b[0;32m   1404\u001b[0m \u001b[38;5;124;03m  Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1467\u001b[0m \u001b[38;5;124;03m          and `y` is a numpy array of corresponding labels.\u001b[39;00m\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;124;03m  \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1469\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDirectoryIterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1470\u001b[0m \u001b[43m      \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1471\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1472\u001b[0m \u001b[43m      \u001b[49m\u001b[43mtarget_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1473\u001b[0m \u001b[43m      \u001b[49m\u001b[43mcolor_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1474\u001b[0m \u001b[43m      \u001b[49m\u001b[43mkeep_aspect_ratio\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_aspect_ratio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1475\u001b[0m \u001b[43m      \u001b[49m\u001b[43mclasses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclasses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1476\u001b[0m \u001b[43m      \u001b[49m\u001b[43mclass_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1477\u001b[0m \u001b[43m      \u001b[49m\u001b[43mdata_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1478\u001b[0m \u001b[43m      \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1479\u001b[0m \u001b[43m      \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1480\u001b[0m \u001b[43m      \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1481\u001b[0m \u001b[43m      \u001b[49m\u001b[43msave_to_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_to_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1482\u001b[0m \u001b[43m      \u001b[49m\u001b[43msave_prefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_prefix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1483\u001b[0m \u001b[43m      \u001b[49m\u001b[43msave_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1484\u001b[0m \u001b[43m      \u001b[49m\u001b[43mfollow_links\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_links\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1485\u001b[0m \u001b[43m      \u001b[49m\u001b[43msubset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1486\u001b[0m \u001b[43m      \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1487\u001b[0m \u001b[43m      \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\.conda\\envs\\py\\lib\\site-packages\\keras\\preprocessing\\image.py:507\u001b[0m, in \u001b[0;36mDirectoryIterator.__init__\u001b[1;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio, dtype)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m classes:\n\u001b[0;32m    506\u001b[0m   classes \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 507\u001b[0m   \u001b[38;5;28;01mfor\u001b[39;00m subdir \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[0;32m    508\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(directory, subdir)):\n\u001b[0;32m    509\u001b[0m       classes\u001b[38;5;241m.\u001b[39mappend(subdir)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'v_data/train'"
+     ]
+    }
+   ],
+   "source": [
+    "train_datagen = ImageDataGenerator(\n",
+    "    rescale=1. / 255,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True)\n",
+    " \n",
+    "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
+    " \n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "    train_data_dir,\n",
+    "    target_size=(img_width, img_height),\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='binary')\n",
+    " \n",
+    "validation_generator = test_datagen.flow_from_directory(\n",
+    "    validation_data_dir,\n",
+    "    target_size=(img_width, img_height),\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='binary')\n",
+    " \n",
+    "model.fit_generator(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch=nb_train_samples // batch_size,\n",
+    "    epochs=epochs,\n",
+    "    validation_data=validation_generator,\n",
+    "    validation_steps=nb_validation_samples // batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "623ec03f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py",
+   "language": "python",
+   "name": "py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}