From 9d19c4b423bc0b741ab490b5674bd54e31aed268 Mon Sep 17 00:00:00 2001 From: Sergey Potapov <88240890+SergeyPotapov01@users.noreply.github.com> Date: Wed, 19 Jan 2022 02:05:03 +0500 Subject: [PATCH] Add files via upload --- main.ipynb | 8142 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 8142 insertions(+) create mode 100644 main.ipynb diff --git a/main.ipynb b/main.ipynb new file mode 100644 index 0000000..69e87e6 --- /dev/null +++ b/main.ipynb @@ -0,0 +1,8142 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a60a443a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already up-to-date: imutils in /home/sergey/.local/lib/python3.8/site-packages (0.5.4)\r\n" + ] + } + ], + "source": [ + "pip install --upgrade imutils " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e9b85b1", + "metadata": {}, + "outputs": [], + "source": [ + "pip install opencv-python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2d47ff1", + "metadata": {}, + "outputs": [], + "source": [ + "pip install --upgrade scikit-learn " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91168743", + "metadata": {}, + "outputs": [], + "source": [ + "pip install --upgrade matplotlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df0125e9", + "metadata": {}, + "outputs": [], + "source": [ + "pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81a1820f", + "metadata": {}, + "outputs": [], + "source": [ + "pip install tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10939a6b", + "metadata": {}, + "outputs": [], + "source": [ + "pip install keras" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d5ff833a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-01-18 23:41:53.240426: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2022-01-18 23:41:53.240476: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "import matplotlib\n", + "matplotlib.use(\"Agg\")\n", + " \n", + "# подключаем необходимые пакеты\n", + "from sklearn.preprocessing import LabelBinarizer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report\n", + "from keras.models import Sequential\n", + "from keras.layers.core import Dense, Dropout\n", + "from imutils import paths\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import argparse\n", + "import random\n", + "import pickle\n", + "import cv2\n", + "import os \n", + "from keras.preprocessing.image import load_img, img_to_array\n", + "from keras.preprocessing import image\n", + "\n", + "from tensorflow.keras.preprocessing import image_dataset_from_directory\n", + "\n", + "train_dataset = image_dataset_from_directory('aaa/',\n", + " subset='training',\n", + " seed=42,\n", + " validation_split=0.1,\n", + " batch_size=56,\n", + " image_size=(52, 56))\n", + "\n", + "class_names = train_dataset.class_names\n", + "class_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "18b70812", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] loading images...\n", + "[106 106 106 ... 211 211 211]\n", + "[184 184 184 ... 148 148 148]\n", + "[222 222 222 ... 213 213 213]\n", + "[230 230 230 ... 229 229 229]\n", + "[221 221 221 ... 209 209 209]\n", + "[222 222 222 ... 193 193 193]\n", + "[203 203 203 ... 120 120 120]\n", + "[231 231 231 ... 221 221 221]\n", + "[124 31 198 ... 73 69 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231 ... 44 44 44]\n", + "[233 233 233 ... 85 85 85]\n", + "[155 155 155 ... 149 149 149]\n", + "[229 229 229 ... 79 79 79]\n", + "[179 179 179 ... 20 20 20]\n", + "[240 240 240 ... 233 233 233]\n", + "[218 218 218 ... 123 123 123]\n", + "[40 40 40 ... 41 41 41]\n", + "[227 227 227 ... 204 204 204]\n", + "[61 61 61 ... 30 30 30]\n", + "[236 236 236 ... 72 72 72]\n", + "[101 60 58 ... 99 49 42]\n", + "[239 239 239 ... 93 93 93]\n", + "[206 206 206 ... 197 197 197]\n", + "[235 235 235 ... 113 113 113]\n", + "[195 195 195 ... 185 185 185]\n", + "[197 197 197 ... 57 57 57]\n", + "[205 205 205 ... 175 175 175]\n", + "[209 209 209 ... 92 92 92]\n", + "[181 181 181 ... 99 99 99]\n", + "[231 231 231 ... 221 221 221]\n", + "[115 105 45 ... 84 74 22]\n", + "[95 95 95 ... 73 73 73]\n", + "[255 223 145 ... 70 101 154]\n", + "[201 201 201 ... 200 200 200]\n", + "[206 206 206 ... 105 105 105]\n", + "[209 192 180 ... 22 55 77]\n", + "[ 86 98 137 ... 243 218 175]\n", + "[32 27 24 ... 46 52 49]\n", + "[179 179 179 ... 96 96 96]\n", + "[235 235 235 ... 143 143 143]\n", + "[182 182 182 ... 200 200 200]\n", + "[196 196 196 ... 209 209 209]\n", + "[194 194 194 ... 221 221 221]\n", + "[141 141 141 ... 159 159 159]\n", + "[204 204 204 ... 100 100 100]\n", + "[182 182 182 ... 45 45 45]\n", + "[191 191 191 ... 183 183 183]\n", + "[155 155 155 ... 149 149 149]\n", + "[220 220 220 ... 239 239 239]\n", + "[240 240 240 ... 193 193 193]\n", + "[241 140 169 ... 170 44 135]\n", + "[235 235 235 ... 135 135 135]\n", + "[61 61 61 ... 56 56 56]\n", + "[ 87 203 121 ... 89 198 118]\n", + "[219 219 219 ... 156 156 156]\n", + "[181 181 181 ... 98 98 98]\n", + "[29 20 16 ... 55 58 56]\n", + "[177 177 177 ... 183 183 183]\n", + "[164 164 164 ... 193 193 193]\n", + "[203 203 203 ... 128 128 128]\n", + "[229 229 229 ... 182 182 182]\n", + "[239 239 239 ... 93 93 93]\n", + "[201 201 201 ... 129 129 129]\n", + "[222 222 222 ... 243 243 243]\n", + "[235 235 235 ... 143 143 143]\n", + "[186 186 186 ... 228 228 228]\n", + "[205 205 205 ... 175 175 175]\n", + "[203 203 203 ... 128 128 128]\n", + "[155 155 155 ... 149 149 149]\n", + "[186 186 186 ... 107 107 107]\n", + "[79 79 79 ... 40 40 40]\n" + ] + } + ], + "source": [ + "data = []\n", + "labels = []\n", + " \n", + "imagePaths = sorted(list(paths.list_images(\"aaa/\")))\n", + "random.seed(42)\n", + "random.shuffle(imagePaths)\n", + " \n", + "for imagePath in imagePaths:\n", + " image = cv2.imread(imagePath)\n", + " image = cv2.resize(image, (26, 28)).flatten()\n", + " data.append(image) \n", + " label = imagePath.split(os.path.sep)[-2]\n", + " labels.append(label) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2a96636", + "metadata": {}, + "outputs": [], + "source": [ + "data = np.array(data, dtype=\"float\") / 255.0\n", + "labels = np.array(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b4d63acf", + "metadata": {}, + "outputs": [], + "source": [ + "(trainX, testX, trainY, testY) = train_test_split(data,\n", + " labels, test_size=0.25, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f27747c", + "metadata": {}, + "outputs": [], + "source": [ + "lb = LabelBinarizer()\n", + "trainY = lb.fit_transform(trainY)\n", + "testY = lb.transform(testY)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "777c7c09", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-01-18 23:42:11.808773: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/sergey/.local/lib/python3.8/site-packages/cv2/../../lib64:\n", + "2022-01-18 23:42:11.809015: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2022-01-18 23:42:11.809100: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (sergey): /proc/driver/nvidia/version does not exist\n", + "2022-01-18 23:42:11.809685: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(2048, input_shape=(2184,), activation=\"sigmoid\"))\n", + "model.add(Dense(1024, activation=\"sigmoid\"))\n", + "model.add(Dense(len(lb.classes_), activation=\"softmax\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d852db23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] training network...\n" + ] + } + ], + "source": [ + "INIT_LR = 0.15\n", + " \n", + "model.compile(loss=\"categorical_crossentropy\", \n", + " metrics=[\"accuracy\"]) " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ccc18c01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.2011 - accuracy: 0.9487 - val_loss: 0.1904 - val_accuracy: 0.9657\n", + "Epoch 2/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.1680 - accuracy: 0.9549 - val_loss: 0.4426 - val_accuracy: 0.8992\n", + "Epoch 3/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.1833 - accuracy: 0.9524 - val_loss: 0.3038 - val_accuracy: 0.9336\n", + "Epoch 4/150\n", + "97/97 [==============================] - 16s 164ms/step - loss: 0.1450 - accuracy: 0.9620 - val_loss: 0.1528 - val_accuracy: 0.9779\n", + "Epoch 5/150\n", + "97/97 [==============================] - 16s 168ms/step - loss: 0.1490 - accuracy: 0.9634 - val_loss: 0.4453 - val_accuracy: 0.9059\n", + "Epoch 6/150\n", + "97/97 [==============================] - 16s 168ms/step - loss: 0.1536 - accuracy: 0.9601 - val_loss: 0.6912 - val_accuracy: 0.8699\n", + "Epoch 7/150\n", + "97/97 [==============================] - 16s 165ms/step - loss: 0.1308 - accuracy: 0.9647 - val_loss: 0.3200 - val_accuracy: 0.9358\n", + "Epoch 8/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.1249 - accuracy: 0.9679 - val_loss: 0.2648 - val_accuracy: 0.9424\n", + "Epoch 9/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.1205 - accuracy: 0.9716 - val_loss: 0.5843 - val_accuracy: 0.8738\n", + "Epoch 10/150\n", + "97/97 [==============================] - 17s 170ms/step - loss: 0.1235 - accuracy: 0.9688 - val_loss: 0.5006 - val_accuracy: 0.8931\n", + "Epoch 11/150\n", + "97/97 [==============================] - 16s 165ms/step - loss: 0.1366 - accuracy: 0.9662 - val_loss: 0.3024 - val_accuracy: 0.9441\n", + "Epoch 12/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.1168 - accuracy: 0.9706 - val_loss: 0.2561 - val_accuracy: 0.9563\n", + "Epoch 13/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.1065 - accuracy: 0.9742 - val_loss: 0.3547 - val_accuracy: 0.9252\n", + "Epoch 14/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0895 - accuracy: 0.9740 - val_loss: 0.1475 - val_accuracy: 0.9806\n", + "Epoch 15/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0962 - accuracy: 0.9764 - val_loss: 2.0319 - val_accuracy: 0.6268\n", + "Epoch 16/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.1093 - accuracy: 0.9701 - val_loss: 0.1790 - val_accuracy: 0.9751\n", + "Epoch 17/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.1012 - accuracy: 0.9790 - val_loss: 0.1761 - val_accuracy: 0.9751\n", + "Epoch 18/150\n", + "97/97 [==============================] - 17s 175ms/step - loss: 0.1061 - accuracy: 0.9754 - val_loss: 0.2059 - val_accuracy: 0.9707\n", + "Epoch 19/150\n", + "97/97 [==============================] - 17s 170ms/step - loss: 0.0864 - accuracy: 0.9795 - val_loss: 0.2232 - val_accuracy: 0.9646\n", + "Epoch 20/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.1074 - accuracy: 0.9758 - val_loss: 0.1986 - val_accuracy: 0.9712\n", + "Epoch 21/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.1012 - accuracy: 0.9799 - val_loss: 0.2514 - val_accuracy: 0.9618\n", + "Epoch 22/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0713 - accuracy: 0.9806 - val_loss: 0.2420 - val_accuracy: 0.9629\n", + "Epoch 23/150\n", + "97/97 [==============================] - 17s 176ms/step - loss: 0.0927 - accuracy: 0.9802 - val_loss: 0.1949 - val_accuracy: 0.9773\n", + "Epoch 24/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0824 - accuracy: 0.9830 - val_loss: 0.1705 - val_accuracy: 0.9817\n", + "Epoch 25/150\n", + "97/97 [==============================] - 18s 184ms/step - loss: 0.0772 - accuracy: 0.9841 - val_loss: 0.5639 - val_accuracy: 0.8953\n", + "Epoch 26/150\n", + "97/97 [==============================] - 19s 193ms/step - loss: 0.0703 - accuracy: 0.9832 - val_loss: 0.2125 - val_accuracy: 0.9695\n", + "Epoch 27/150\n", + "97/97 [==============================] - 17s 177ms/step - loss: 0.0733 - accuracy: 0.9847 - val_loss: 0.1818 - val_accuracy: 0.9839\n", + "Epoch 28/150\n", + "97/97 [==============================] - 19s 192ms/step - loss: 0.0738 - accuracy: 0.9847 - val_loss: 0.2019 - val_accuracy: 0.9779\n", + "Epoch 29/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0912 - accuracy: 0.9819 - val_loss: 0.3416 - val_accuracy: 0.9485\n", + "Epoch 30/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0811 - accuracy: 0.9815 - val_loss: 0.1731 - val_accuracy: 0.9817\n", + "Epoch 31/150\n", + "97/97 [==============================] - 17s 173ms/step - loss: 0.0658 - accuracy: 0.9849 - val_loss: 1.4386 - val_accuracy: 0.7746\n", + "Epoch 32/150\n", + "97/97 [==============================] - 19s 196ms/step - loss: 0.0715 - accuracy: 0.9828 - val_loss: 0.2007 - val_accuracy: 0.9773\n", + "Epoch 33/150\n", + "97/97 [==============================] - 17s 180ms/step - loss: 0.0668 - accuracy: 0.9858 - val_loss: 0.1730 - val_accuracy: 0.9795\n", + "Epoch 34/150\n", + "97/97 [==============================] - 17s 177ms/step - loss: 0.0652 - accuracy: 0.9836 - val_loss: 0.3145 - val_accuracy: 0.9596\n", + "Epoch 35/150\n", + "97/97 [==============================] - 18s 189ms/step - loss: 0.0537 - accuracy: 0.9878 - val_loss: 0.1908 - val_accuracy: 0.9767\n", + "Epoch 36/150\n", + "97/97 [==============================] - 19s 200ms/step - loss: 0.0682 - accuracy: 0.9843 - val_loss: 0.2198 - val_accuracy: 0.9690\n", + "Epoch 37/150\n", + "97/97 [==============================] - 17s 177ms/step - loss: 0.0599 - accuracy: 0.9856 - val_loss: 0.1987 - val_accuracy: 0.9745\n", + "Epoch 38/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0379 - accuracy: 0.9915 - val_loss: 0.1750 - val_accuracy: 0.9845\n", + "Epoch 39/150\n", + "97/97 [==============================] - 17s 175ms/step - loss: 0.0729 - accuracy: 0.9858 - val_loss: 0.2509 - val_accuracy: 0.9707\n", + "Epoch 40/150\n", + "97/97 [==============================] - 18s 190ms/step - loss: 0.0511 - accuracy: 0.9928 - val_loss: 0.8132 - val_accuracy: 0.8638\n", + "Epoch 41/150\n", + "97/97 [==============================] - 17s 181ms/step - loss: 0.0673 - accuracy: 0.9852 - val_loss: 0.2107 - val_accuracy: 0.9812\n", + "Epoch 42/150\n", + "97/97 [==============================] - 16s 163ms/step - loss: 0.0457 - accuracy: 0.9900 - val_loss: 0.1919 - val_accuracy: 0.9817\n", + "Epoch 43/150\n", + "97/97 [==============================] - 15s 155ms/step - loss: 0.0876 - accuracy: 0.9843 - val_loss: 0.4845 - val_accuracy: 0.9203\n", + "Epoch 44/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0494 - accuracy: 0.9904 - val_loss: 0.5559 - val_accuracy: 0.9142\n", + "Epoch 45/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0826 - accuracy: 0.9821 - val_loss: 0.2370 - val_accuracy: 0.9745\n", + "Epoch 46/150\n", + "97/97 [==============================] - 17s 178ms/step - loss: 0.0426 - accuracy: 0.9930 - val_loss: 0.2114 - val_accuracy: 0.9801\n", + "Epoch 47/150\n", + "97/97 [==============================] - 17s 175ms/step - loss: 0.0737 - accuracy: 0.9858 - val_loss: 0.1827 - val_accuracy: 0.9845\n", + "Epoch 48/150\n", + "97/97 [==============================] - 17s 175ms/step - loss: 0.0629 - accuracy: 0.9873 - val_loss: 0.2102 - val_accuracy: 0.9812\n", + "Epoch 49/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0525 - accuracy: 0.9869 - val_loss: 0.2490 - val_accuracy: 0.9640\n", + "Epoch 50/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0584 - accuracy: 0.9867 - val_loss: 0.1970 - val_accuracy: 0.9790\n", + "Epoch 51/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0314 - accuracy: 0.9935 - val_loss: 0.2058 - val_accuracy: 0.9790\n", + "Epoch 52/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0634 - accuracy: 0.9871 - val_loss: 0.2036 - val_accuracy: 0.9823\n", + "Epoch 53/150\n", + "97/97 [==============================] - 17s 177ms/step - loss: 0.0503 - accuracy: 0.9891 - val_loss: 0.4253 - val_accuracy: 0.9529\n", + "Epoch 54/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0394 - accuracy: 0.9908 - val_loss: 0.1881 - val_accuracy: 0.9823\n", + "Epoch 55/150\n", + "97/97 [==============================] - 17s 173ms/step - loss: 0.0695 - accuracy: 0.9900 - val_loss: 0.2516 - val_accuracy: 0.9767\n", + "Epoch 56/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0592 - accuracy: 0.9858 - val_loss: 0.2208 - val_accuracy: 0.9801\n", + "Epoch 57/150\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "97/97 [==============================] - 17s 171ms/step - loss: 0.0641 - accuracy: 0.9865 - val_loss: 0.2024 - val_accuracy: 0.9823\n", + "Epoch 58/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0654 - accuracy: 0.9836 - val_loss: 0.2111 - val_accuracy: 0.9817\n", + "Epoch 59/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0543 - accuracy: 0.9904 - val_loss: 0.2227 - val_accuracy: 0.9784\n", + "Epoch 60/150\n", + "97/97 [==============================] - 18s 186ms/step - loss: 0.0556 - accuracy: 0.9886 - val_loss: 0.2171 - val_accuracy: 0.9828\n", + "Epoch 61/150\n", + "97/97 [==============================] - 17s 177ms/step - loss: 0.0816 - accuracy: 0.9891 - val_loss: 0.2286 - val_accuracy: 0.9812\n", + "Epoch 62/150\n", + "97/97 [==============================] - 18s 184ms/step - loss: 0.0558 - accuracy: 0.9908 - val_loss: 0.2266 - val_accuracy: 0.9812\n", + "Epoch 63/150\n", + "97/97 [==============================] - 17s 173ms/step - loss: 0.0546 - accuracy: 0.9898 - val_loss: 0.2581 - val_accuracy: 0.9767\n", + "Epoch 64/150\n", + "97/97 [==============================] - 15s 157ms/step - loss: 0.0634 - accuracy: 0.9891 - val_loss: 0.2544 - val_accuracy: 0.9734\n", + "Epoch 65/150\n", + "97/97 [==============================] - 15s 158ms/step - loss: 0.0540 - accuracy: 0.9880 - val_loss: 0.2147 - val_accuracy: 0.9812\n", + "Epoch 66/150\n", + "97/97 [==============================] - 16s 162ms/step - loss: 0.0505 - accuracy: 0.9913 - val_loss: 0.2624 - val_accuracy: 0.9657\n", + "Epoch 67/150\n", + "97/97 [==============================] - 16s 166ms/step - loss: 0.0419 - accuracy: 0.9922 - val_loss: 0.2960 - val_accuracy: 0.9651\n", + "Epoch 68/150\n", + "97/97 [==============================] - 16s 161ms/step - loss: 0.0542 - accuracy: 0.9873 - val_loss: 0.3964 - val_accuracy: 0.9446\n", + "Epoch 69/150\n", + "97/97 [==============================] - 18s 184ms/step - loss: 0.0414 - accuracy: 0.9882 - val_loss: 0.2207 - val_accuracy: 0.9801\n", + "Epoch 70/150\n", + "97/97 [==============================] - 18s 183ms/step - loss: 0.0526 - accuracy: 0.9919 - val_loss: 0.2110 - val_accuracy: 0.9823\n", + "Epoch 71/150\n", + "97/97 [==============================] - 15s 156ms/step - loss: 0.0527 - accuracy: 0.9895 - val_loss: 0.2309 - val_accuracy: 0.9823\n", + "Epoch 72/150\n", + "97/97 [==============================] - 16s 161ms/step - loss: 0.0457 - accuracy: 0.9887 - val_loss: 0.6318 - val_accuracy: 0.9136\n", + "Epoch 73/150\n", + "97/97 [==============================] - 18s 183ms/step - loss: 0.0499 - accuracy: 0.9882 - val_loss: 0.2221 - val_accuracy: 0.9834\n", + "Epoch 74/150\n", + "97/97 [==============================] - 20s 205ms/step - loss: 0.0359 - accuracy: 0.9908 - val_loss: 0.2092 - val_accuracy: 0.9839\n", + "Epoch 75/150\n", + "97/97 [==============================] - 18s 185ms/step - loss: 0.0658 - accuracy: 0.9878 - val_loss: 0.3310 - val_accuracy: 0.9618\n", + "Epoch 76/150\n", + "97/97 [==============================] - 18s 186ms/step - loss: 0.0583 - accuracy: 0.9869 - val_loss: 0.2019 - val_accuracy: 0.9834\n", + "Epoch 77/150\n", + "97/97 [==============================] - 18s 185ms/step - loss: 0.0491 - accuracy: 0.9908 - val_loss: 0.2300 - val_accuracy: 0.9834\n", + "Epoch 78/150\n", + "97/97 [==============================] - 18s 186ms/step - loss: 0.0384 - accuracy: 0.9928 - val_loss: 0.2298 - val_accuracy: 0.9828\n", + "Epoch 79/150\n", + "97/97 [==============================] - 18s 189ms/step - loss: 0.0372 - accuracy: 0.9898 - val_loss: 0.2273 - val_accuracy: 0.9806\n", + "Epoch 80/150\n", + "97/97 [==============================] - 18s 185ms/step - loss: 0.0461 - accuracy: 0.9922 - val_loss: 0.2513 - val_accuracy: 0.9767\n", + "Epoch 81/150\n", + "97/97 [==============================] - 18s 183ms/step - loss: 0.0377 - accuracy: 0.9934 - val_loss: 0.2803 - val_accuracy: 0.9734\n", + "Epoch 82/150\n", + "97/97 [==============================] - 18s 188ms/step - loss: 0.0476 - accuracy: 0.9886 - val_loss: 0.2280 - val_accuracy: 0.9823\n", + "Epoch 83/150\n", + "97/97 [==============================] - 18s 190ms/step - loss: 0.0303 - accuracy: 0.9941 - val_loss: 0.2264 - val_accuracy: 0.9839\n", + "Epoch 84/150\n", + "97/97 [==============================] - 17s 179ms/step - loss: 0.0595 - accuracy: 0.9900 - val_loss: 0.2854 - val_accuracy: 0.9707\n", + "Epoch 85/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0478 - accuracy: 0.9917 - val_loss: 0.3647 - val_accuracy: 0.9535\n", + "Epoch 86/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.2326 - val_accuracy: 0.9828\n", + "Epoch 87/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.0490 - accuracy: 0.9908 - val_loss: 0.2234 - val_accuracy: 0.9801\n", + "Epoch 88/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0384 - accuracy: 0.9911 - val_loss: 0.2528 - val_accuracy: 0.9767\n", + "Epoch 89/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0487 - accuracy: 0.9874 - val_loss: 0.2282 - val_accuracy: 0.9817\n", + "Epoch 90/150\n", + "97/97 [==============================] - 17s 175ms/step - loss: 0.0653 - accuracy: 0.9869 - val_loss: 0.2493 - val_accuracy: 0.9745\n", + "Epoch 91/150\n", + "97/97 [==============================] - 18s 183ms/step - loss: 0.0209 - accuracy: 0.9956 - val_loss: 0.2417 - val_accuracy: 0.9828\n", + "Epoch 92/150\n", + "97/97 [==============================] - 18s 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val_loss: 0.2183 - val_accuracy: 0.9817\n", + "Epoch 99/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0457 - accuracy: 0.9906 - val_loss: 0.2644 - val_accuracy: 0.9762\n", + "Epoch 100/150\n", + "97/97 [==============================] - 17s 172ms/step - loss: 0.0360 - accuracy: 0.9924 - val_loss: 0.2349 - val_accuracy: 0.9795\n", + "Epoch 101/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0485 - accuracy: 0.9893 - val_loss: 0.2578 - val_accuracy: 0.9767\n", + "Epoch 102/150\n", + "97/97 [==============================] - 18s 190ms/step - loss: 0.0464 - accuracy: 0.9926 - val_loss: 0.2468 - val_accuracy: 0.9773\n", + "Epoch 103/150\n", + "97/97 [==============================] - 17s 178ms/step - loss: 0.0183 - accuracy: 0.9954 - val_loss: 0.1998 - val_accuracy: 0.9850\n", + "Epoch 104/150\n", + "97/97 [==============================] - 17s 175ms/step - loss: 0.0275 - accuracy: 0.9952 - val_loss: 0.2347 - val_accuracy: 0.9817\n", + "Epoch 105/150\n", + "97/97 [==============================] - 17s 174ms/step - loss: 0.0467 - accuracy: 0.9922 - val_loss: 0.2358 - val_accuracy: 0.9812\n", + "Epoch 106/150\n", + "97/97 [==============================] - 18s 182ms/step - loss: 0.0338 - accuracy: 0.9934 - val_loss: 0.2468 - val_accuracy: 0.9812\n", + "Epoch 107/150\n", + "97/97 [==============================] - 17s 170ms/step - loss: 0.0249 - accuracy: 0.9937 - val_loss: 0.2460 - val_accuracy: 0.9806\n", + "Epoch 108/150\n", + "97/97 [==============================] - 18s 181ms/step - loss: 0.0477 - accuracy: 0.9915 - val_loss: 0.2284 - val_accuracy: 0.9812\n", + "Epoch 109/150\n", + "97/97 [==============================] - 18s 188ms/step - loss: 0.0520 - accuracy: 0.9913 - val_loss: 0.3518 - val_accuracy: 0.9579\n", + "Epoch 110/150\n", + "97/97 [==============================] - 18s 185ms/step - loss: 0.0280 - accuracy: 0.9945 - val_loss: 0.2315 - val_accuracy: 0.9817\n", + "Epoch 111/150\n", + "97/97 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+ "Epoch 117/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0280 - accuracy: 0.9928 - val_loss: 0.3262 - val_accuracy: 0.9684\n", + "Epoch 118/150\n", + "97/97 [==============================] - 16s 170ms/step - loss: 0.0411 - accuracy: 0.9924 - val_loss: 0.2284 - val_accuracy: 0.9817\n", + "Epoch 119/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.0432 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9784\n", + "Epoch 120/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0174 - accuracy: 0.9963 - val_loss: 0.2371 - val_accuracy: 0.9828\n", + "Epoch 121/150\n", + "97/97 [==============================] - 16s 167ms/step - loss: 0.0292 - accuracy: 0.9954 - val_loss: 0.3957 - val_accuracy: 0.9540\n", + "Epoch 122/150\n", + "97/97 [==============================] - 18s 181ms/step - loss: 0.0388 - accuracy: 0.9921 - val_loss: 0.2309 - val_accuracy: 0.9828\n", + "Epoch 123/150\n", + "97/97 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167ms/step - loss: 0.0378 - accuracy: 0.9913 - val_loss: 0.2227 - val_accuracy: 0.9828\n", + "Epoch 130/150\n", + "97/97 [==============================] - 16s 170ms/step - loss: 0.0362 - accuracy: 0.9930 - val_loss: 0.2462 - val_accuracy: 0.9828\n", + "Epoch 131/150\n", + "97/97 [==============================] - 16s 162ms/step - loss: 0.0431 - accuracy: 0.9910 - val_loss: 0.2562 - val_accuracy: 0.9806\n", + "Epoch 132/150\n", + "97/97 [==============================] - 16s 165ms/step - loss: 0.0235 - accuracy: 0.9952 - val_loss: 0.2298 - val_accuracy: 0.9806\n", + "Epoch 133/150\n", + "97/97 [==============================] - 16s 161ms/step - loss: 0.0365 - accuracy: 0.9921 - val_loss: 0.2547 - val_accuracy: 0.9740\n", + "Epoch 134/150\n", + "97/97 [==============================] - 16s 160ms/step - loss: 0.0324 - accuracy: 0.9922 - val_loss: 0.2552 - val_accuracy: 0.9773\n", + "Epoch 135/150\n", + "97/97 [==============================] - 16s 163ms/step - loss: 0.0388 - accuracy: 0.9928 - val_loss: 0.2297 - val_accuracy: 0.9817\n", + "Epoch 136/150\n", + "97/97 [==============================] - 16s 164ms/step - loss: 0.0170 - accuracy: 0.9961 - val_loss: 0.2687 - val_accuracy: 0.9784\n", + "Epoch 137/150\n", + "97/97 [==============================] - 17s 177ms/step - loss: 0.0332 - accuracy: 0.9935 - val_loss: 0.2946 - val_accuracy: 0.9790\n", + "Epoch 138/150\n", + "97/97 [==============================] - 17s 171ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.2435 - val_accuracy: 0.9817\n", + "Epoch 139/150\n", + "97/97 [==============================] - 16s 164ms/step - loss: 0.0292 - accuracy: 0.9952 - val_loss: 0.4371 - val_accuracy: 0.9518\n", + "Epoch 140/150\n", + "97/97 [==============================] - 16s 164ms/step - loss: 0.0359 - accuracy: 0.9930 - val_loss: 0.2279 - val_accuracy: 0.9817\n", + "Epoch 141/150\n", + "97/97 [==============================] - 16s 166ms/step - loss: 0.0519 - accuracy: 0.9908 - val_loss: 0.2720 - val_accuracy: 0.9795\n", + "Epoch 142/150\n", + "97/97 [==============================] - 16s 163ms/step - loss: 0.0343 - accuracy: 0.9934 - val_loss: 0.2627 - val_accuracy: 0.9790\n", + "Epoch 143/150\n", + "97/97 [==============================] - 18s 181ms/step - loss: 0.0357 - accuracy: 0.9926 - val_loss: 0.2861 - val_accuracy: 0.9767\n", + "Epoch 144/150\n", + "97/97 [==============================] - 16s 164ms/step - loss: 0.0309 - accuracy: 0.9941 - val_loss: 0.2492 - val_accuracy: 0.9834\n", + "Epoch 145/150\n", + "97/97 [==============================] - 16s 169ms/step - loss: 0.0272 - accuracy: 0.9958 - val_loss: 0.3839 - val_accuracy: 0.9540\n", + "Epoch 146/150\n", + "97/97 [==============================] - 16s 163ms/step - loss: 0.0394 - accuracy: 0.9928 - val_loss: 0.5787 - val_accuracy: 0.9413\n", + "Epoch 147/150\n", + "97/97 [==============================] - 16s 163ms/step - loss: 0.0368 - accuracy: 0.9937 - val_loss: 0.2348 - val_accuracy: 0.9812\n", + "Epoch 148/150\n", + "97/97 [==============================] - 15s 160ms/step - loss: 0.0113 - accuracy: 0.9969 - val_loss: 0.2996 - val_accuracy: 0.9756\n", + "Epoch 149/150\n", + "97/97 [==============================] - 16s 162ms/step - loss: 0.0372 - accuracy: 0.9908 - val_loss: 0.2226 - val_accuracy: 0.9845\n", + "Epoch 150/150\n", + "97/97 [==============================] - 16s 162ms/step - loss: 0.0391 - accuracy: 0.9930 - val_loss: 0.2484 - val_accuracy: 0.9784\n" + ] + } + ], + "source": [ + "H = model.fit(trainX, trainY, validation_data=(testX, testY),\n", + " epochs=150, batch_size=56) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d7381dff", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "plt.plot(H.history['accuracy'], \n", + " label='Доля верных ответов на обучающем наборе')\n", + "plt.plot(H.history['val_accuracy'], \n", + " label='Доля верных ответов на проверочном наборе')\n", + "plt.xlabel('Эпоха обучения')\n", + "plt.ylabel('Доля верных ответов')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7cbf8815", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fireball Firecracker 30\n", + "104 ['Witch', 'Princess', 'Skeleton_Barrel', 'Tombstone', 'Royal_Hogs', 'Ram_Rider', 'Bowler', 'Lightnimg', 'Electro_Spirit', 'Mother_Witch', 'Freeze', 'Giant', 'Archers', 'Skeleton_Dragons', 'Executiomer', 'Zap', 'Electro_Giant', 'Night_Witch', 'Heal_Spirit', 'Minion_Horde', 'Archer_Queen', 'Mortar', 'The_Log', 'Spear_Goblins', 'Fire_Spirit', 'Rocket', 'Lava_Hound', 'Ice_Spirit', 'Earthquake', 'Royal_Delivery', 'Arrows', 'Prince', 'Goblin_Gang', 'Graveyard', 'Knight', 'Tornado', 'Rascals', 'Royal_Ghost', 'Furnace', 'Cannon_Cart', 'Bats', 'Magic_Archer', 'Rage', 'Elixir_Collector', 'Tesla', 'Fisherman', 'Musketeer', 'Elite_Barbarians', 'Poison', 'Goblin_Hut', 'Dart_Goblin', 'Sparky', 'Minions', 'Battle_Healer', 'Fireball', 'Inferno_Tower', 'Barbarian_Hut', 'Zappies', 'Skeleton_Army', 'Giant_Snowball', 'Clone', 'Barbarian_Barrel', 'X-Bow', 'Golem', 'Hog_Rider', 'Valkyrie', 'Mini_PEKKA', 'Ice_Golem', 'Electro_Dragon', 'Goblin_Drill', 'Bomber', 'Wizard', 'Wall_Breakers', 'Miner', 'Flying_Machine', 'Pekka', 'Mega_Knight', 'Royal_Giant', 'Guards', 'Ice_Wizard', 'Skeleton_King', 'Three_Musketeers', 'Goblin_Barrel', 'Elixir_Golem', 'Mega_Minion', 'Skeletons', 'Dark_Prince', 'Electro_Wizard', 'Battle_Ram', 'Goblin_Cage', 'Baby_Dragon', 'Gaint_Skeleton', 'Bomb_Tower', 'Bandit', 'Goblin_Giant', 'Inferno_Dragon', 'Golden_Knight', 'Royal_Recruits', 'Hunter', 'Goblins', 'Balloon', 'Lumberjack', 'Cannon', 'Barbarians']\n" + ] + } + ], + "source": [ + "z = 0\n", + "c = []\n", + "for i in os.listdir('aaa/'):\n", + " image = cv2.imread(f'aaa/{i}/{os.listdir(f\"aut/{i}/\")[4]}')\n", + " x = image\n", + " image = cv2.resize(image, (26, 28))\n", + " x = image.astype(\"float\") / 255.0\n", + "\n", + " x = x.reshape(1, 2184)\n", + " prediction = model.predict(x)\n", + " prediction = np.argmax(prediction)\n", + " if class_names[prediction]==i :\n", + " z+=1\n", + " c.append(class_names[prediction])\n", + " else:\n", + " print(class_names[prediction], i, prediction)\n", + "print(z, c)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "557bb8cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95 94 Witch Witch 100\n", + "141 139 Princess Princess 76\n", + "230 228 Skeleton_Barrel Skeleton_Barrel 87\n", + "258 256 Tombstone Tombstone 96\n", + "328 326 Royal_Hogs Royal_Hogs 84\n", + "420 418 Ram_Rider Ram_Rider 78\n", + "512 510 Bowler Bowler 14\n", + "622 620 Lightnimg Lightnimg 60\n", + "649 646 Electro_Spirit Electro_Spirit 23\n", + "682 679 Mother_Witch Mother_Witch 70\n", + "712 709 Freeze Freeze 34\n", + "782 779 Giant Giant 37\n", + "854 851 Archers Archers 1\n", + "931 928 Skeleton_Dragons Skeleton_Dragons 88\n", + "1041 1038 Executiomer Executiomer 28\n", + "1079 1076 Zap Zap 103\n", + "1157 1154 Electro_Giant Electro_Giant 22\n", + "1284 1280 Night_Witch Night_Witch 72\n", + "1340 1335 Heal_Spirit Heal_Spirit 50\n", + "1395 1389 Minion_Horde Minion_Horde 67\n", + "1469 1463 Archer_Queen Archer_Queen 0\n", + "1519 1513 Mortar Mortar 69\n", + "1548 1542 The_Log The_Log 94\n", + "1585 1578 Spear_Goblins Spear_Goblins 92\n", + "1608 1601 Fire_Spirit Fire_Spirit 29\n", + "1734 1727 Rocket Rocket 80\n", + "1817 1809 Lava_Hound Lava_Hound 59\n", + "1845 1836 Ice_Spirit Ice_Spirit 54\n", + "1869 1858 Earthquake Earthquake 20\n", + "1937 1926 Royal_Delivery Royal_Delivery 81\n", + "1986 1975 Arrows Arrows 2\n", + "2053 2042 Prince Prince 75\n", + "2120 2109 Goblin_Gang Goblin_Gang 42\n", + "2179 2168 Graveyard Graveyard 48\n", + "2264 2253 Knight Knight 58\n", + "2308 2297 Tornado Tornado 97\n", + "2382 2371 Rascals Rascals 79\n", + "2420 2409 Royal_Ghost Royal_Ghost 82\n", + "2450 2438 Furnace Furnace 35\n", + "2562 2549 Cannon_Cart Cannon_Cart 16\n", + "2656 2642 Bats Bats 9\n", + "2819 2805 Magic_Archer Magic_Archer 62\n", + "2883 2869 Rage Rage 77\n", + "2964 2950 Elixir_Collector Elixir_Collector 26\n", + "3017 3003 Tesla Tesla 93\n", + "3051 3037 Fisherman Fisherman 32\n", + "3132 3118 Musketeer Musketeer 71\n", + "3219 3205 Elite_Barbarians Elite_Barbarians 25\n", + "3282 3268 Poison Poison 74\n", + "3319 3303 Goblin_Hut Goblin_Hut 44\n", + "3386 3369 Dart_Goblin Dart_Goblin 19\n", + "3453 3436 Sparky Sparky 91\n", + "3518 3501 Minions Minions 68\n", + "3599 3581 Battle_Healer Battle_Healer 10\n", + "3675 3657 Fireball Fireball 30\n", + "3748 3730 Inferno_Tower Inferno_Tower 57\n", + "3840 3822 Barbarian_Hut Barbarian_Hut 7\n", + "3932 3914 Zappies Zappies 104\n", + "4005 3987 Skeleton_Army Skeleton_Army 86\n", + "4033 4015 Giant_Snowball Giant_Snowball 38\n", + "4076 4058 Clone Clone 17\n", + "4100 4082 Barbarian_Barrel Barbarian_Barrel 6\n", + "4148 4130 X-Bow X-Bow 102\n", + "4283 4265 Golem Golem 47\n", + "4355 4337 Hog_Rider Hog_Rider 51\n", + "4409 4390 Valkyrie Valkyrie 98\n", + "4514 4495 Mini_PEKKA Mini_PEKKA 66\n", + "4714 4694 Ice_Golem Ice_Golem 53\n", + "4792 4772 Electro_Dragon Electro_Dragon 21\n", + "4851 4829 Goblin_Drill Goblin_Drill 41\n", + "4900 4876 Bomber Bomber 13\n", + "4970 4945 Firecracker Firecracker 31\n", + "5020 4995 Wizard Wizard 101\n", + "5066 5041 Wall_Breakers Wall_Breakers 99\n", + "5104 5078 Miner Miner 65\n", + "5189 5163 Flying_Machine Flying_Machine 33\n", + "5274 5248 Pekka Pekka 73\n", + "5359 5333 Mega_Knight Mega_Knight 63\n", + "5445 5415 Royal_Giant Royal_Giant 83\n", + "5511 5481 Guards Guards 49\n", + "5605 5574 Ice_Wizard Ice_Wizard 55\n", + "5673 5642 Skeleton_King Skeleton_King 89\n", + "5762 5731 Three_Musketeers Three_Musketeers 95\n", + "5820 5788 Goblin_Barrel Goblin_Barrel 39\n", + "5892 5859 Elixir_Golem Elixir_Golem 27\n", + "6025 5992 Mega_Minion Mega_Minion 64\n", + "6043 6010 Skeletons Skeletons 90\n", + "6078 6045 Dark_Prince Dark_Prince 18\n", + "6149 6116 Electro_Wizard Electro_Wizard 24\n", + "6213 6180 Battle_Ram Battle_Ram 11\n", + "6276 6242 Goblin_Cage Goblin_Cage 40\n", + "6473 6439 Baby_Dragon Baby_Dragon 3\n", + "6545 6511 Gaint_Skeleton Gaint_Skeleton 36\n", + "6578 6543 Bomb_Tower Bomb_Tower 12\n", + "6610 6575 Bandit Bandit 5\n", + "6644 6609 Goblin_Giant Goblin_Giant 43\n", + "6695 6660 Inferno_Dragon Inferno_Dragon 56\n", + "6724 6689 Golden_Knight Golden_Knight 46\n", + "6774 6737 Royal_Recruits Royal_Recruits 85\n", + "6862 6819 Hunter Hunter 52\n", + "6950 6904 Goblins Goblins 45\n", + "7034 6988 Balloon Balloon 4\n", + "7106 7060 Lumberjack Lumberjack 61\n", + "7143 7097 Cannon Cannon 15\n", + "7222 7176 Barbarians Barbarians 8\n", + "7176 7222\n" + ] + } + ], + "source": [ + "z = 0\n", + "c = 0\n", + "for i in os.listdir('aaa/'):\n", + " for j in os.listdir('aaa/'+i):\n", + " image = cv2.imread(\"aaa/\"+i+'/'+j)\n", + " image = cv2.resize(image, (26, 28))\n", + " x = image.astype(\"float\") / 255.0\n", + " x = x.reshape(1, 2184)\n", + " prediction = model.predict(x)\n", + " prediction = np.argmax(prediction)\n", + " c += 1 \n", + " if class_names[prediction]==i :\n", + " z+=1\n", + " print(c, z, i, class_names[prediction], prediction)\n", + "print(z, c)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b31b0162", + "metadata": {}, + "outputs": [], + "source": [ + "model.save('CR.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edc3a9b5", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import load_model\n", + "\n", + "model = load_model('CR.h5')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": 5 +}