From 440199283bad595de6949b1d1237f31690f8138a Mon Sep 17 00:00:00 2001 From: Igor Volochay <44619012+IgorVolochay@users.noreply.github.com> Date: Sat, 13 Aug 2022 15:16:58 +0300 Subject: [PATCH] =?UTF-8?q?=F0=9F=96=BC=EF=B8=8F=20Update=20image=20instal?= =?UTF-8?q?ler?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Face recognition/Face recognition.ipynb | 1306 +++++++++++------------ 1 file changed, 647 insertions(+), 659 deletions(-) diff --git a/Face recognition/Face recognition.ipynb b/Face recognition/Face recognition.ipynb index c360662..3840d0b 100644 --- a/Face recognition/Face recognition.ipynb +++ b/Face recognition/Face recognition.ipynb @@ -33,6 +33,7 @@ "outputs": [], "source": [ "import random\n", + "import os\n", "\n", "import dlib\n", "\n", @@ -73,7 +74,7 @@ "execution_count": 3, "id": "3927fdf2", "metadata": { - "collapsed": true + "scrolled": true }, "outputs": [ { @@ -132,7 +133,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "6a3ed10c", + "id": "b88e6394", "metadata": { "scrolled": true }, @@ -141,104 +142,109 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 Men1/Men1_\n", - "10 Men1/Men1_\n", - "20 Men1/Men1_\n", - "30 Men1/Men1_\n", - "40 Men1/Men1_\n", - "50 Men1/Men1_\n", - "60 Men1/Men1_\n", - "70 Men1/Men1_\n", - "80 Men1/Men1_\n", - "90 Men1/Men1_\n", - "100 Men1/Men1_\n", - "110 Men1/Men1_\n", - "120 Men1/Men1_\n", - "130 Men1/Men1_\n", - "140 Men1/Men1_\n", - "150 Men1/Men1_\n", - "160 Men1/Men1_\n", - "170 Men1/Men1_\n", - "Done!\n", - "0 Men2/Men2_\n", - "10 Men2/Men2_\n", - "20 Men2/Men2_\n", - "30 Men2/Men2_\n", - "40 Men2/Men2_\n", - "50 Men2/Men2_\n", - "60 Men2/Men2_\n", - "70 Men2/Men2_\n", - "80 Men2/Men2_\n", - "90 Men2/Men2_\n", - "100 Men2/Men2_\n", - "110 Men2/Men2_\n", - "120 Men2/Men2_\n", - "130 Men2/Men2_\n", - "140 Men2/Men2_\n", - "150 Men2/Men2_\n", - "160 Men2/Men2_\n", - "170 Men2/Men2_\n", - "180 Men2/Men2_\n", - "Done!\n", - "0 Men3/Men3_\n", - "10 Men3/Men3_\n", - "20 Men3/Men3_\n", - "30 Men3/Men3_\n", - "40 Men3/Men3_\n", - "50 Men3/Men3_\n", - "60 Men3/Men3_\n", - "70 Men3/Men3_\n", - "80 Men3/Men3_\n", - "90 Men3/Men3_\n", - "100 Men3/Men3_\n", - "110 Men3/Men3_\n", - "120 Men3/Men3_\n", - "130 Men3/Men3_\n", - "140 Men3/Men3_\n", - "150 Men3/Men3_\n", - "160 Men3/Men3_\n", - "170 Men3/Men3_\n", - "180 Men3/Men3_\n", - "190 Men3/Men3_\n", - "200 Men3/Men3_\n", - "210 Men3/Men3_\n", - "220 Men3/Men3_\n", - "230 Men3/Men3_\n", - "Done!\n", - "0 Female1/Female1_\n", - "10 Female1/Female1_\n", - "20 Female1/Female1_\n", - "30 Female1/Female1_\n", - "40 Female1/Female1_\n", - "50 Female1/Female1_\n", - "60 Female1/Female1_\n", - "70 Female1/Female1_\n", - "80 Female1/Female1_\n", - "90 Female1/Female1_\n", - "100 Female1/Female1_\n", - "110 Female1/Female1_\n", - "120 Female1/Female1_\n", - "130 Female1/Female1_\n", - "140 Female1/Female1_\n", - "150 Female1/Female1_\n", - "160 Female1/Female1_\n", - "170 Female1/Female1_\n", - "180 Female1/Female1_\n", - "190 Female1/Female1_\n", - "200 Female1/Female1_\n", - "Done!\n", - "0 SomePerson/SomePerson_\n", - "10 SomePerson/SomePerson_\n", - "20 SomePerson/SomePerson_\n", - "30 SomePerson/SomePerson_\n", - "40 SomePerson/SomePerson_\n", - "50 SomePerson/SomePerson_\n", - "60 SomePerson/SomePerson_\n", - "70 SomePerson/SomePerson_\n", - "80 SomePerson/SomePerson_\n", - "90 SomePerson/SomePerson_\n", - "100 SomePerson/SomePerson_\n", - "Done!\n" + "Name: Men1\n", + "Real prediction: [1, 0, 0, 0]\n", + "Files on dir: 178\n", + "Files done: 10/178\n", + "Files done: 20/178\n", + "Files done: 30/178\n", + "Files done: 40/178\n", + "Files done: 50/178\n", + "Files done: 60/178\n", + "Files done: 70/178\n", + "Files done: 80/178\n", + "Files done: 90/178\n", + "Files done: 100/178\n", + "Files done: 110/178\n", + "Files done: 120/178\n", + "Files done: 130/178\n", + "Files done: 140/178\n", + "Files done: 150/178\n", + "Files done: 160/178\n", + "Files done: 170/178\n", + "Name: Men2\n", + "Real prediction: [0, 1, 0, 0]\n", + "Files on dir: 186\n", + "Files done: 10/186\n", + "Files done: 20/186\n", + "Files done: 30/186\n", + "Files done: 40/186\n", + "Files done: 50/186\n", + "Files done: 60/186\n", + "Files done: 70/186\n", + "Files done: 80/186\n", + "Files done: 90/186\n", + "Files done: 100/186\n", + "Files done: 110/186\n", + "Files done: 120/186\n", + "Files done: 130/186\n", + "Files done: 140/186\n", + "Files done: 150/186\n", + "Files done: 160/186\n", + "Files done: 170/186\n", + "Files done: 180/186\n", + "Name: Men3\n", + "Real prediction: [0, 0, 1, 0]\n", + "Files on dir: 234\n", + "Files done: 10/234\n", + "Files done: 20/234\n", + "Files done: 30/234\n", + "Files done: 40/234\n", + "Files done: 50/234\n", + "Files done: 60/234\n", + "Files done: 70/234\n", + "Files done: 80/234\n", + "Files done: 90/234\n", + "Files done: 100/234\n", + "Files done: 110/234\n", + "Files done: 120/234\n", + "Files done: 130/234\n", + "Files done: 140/234\n", + "Files done: 150/234\n", + "Files done: 160/234\n", + "Files done: 170/234\n", + "Files done: 180/234\n", + "Files done: 190/234\n", + "Files done: 200/234\n", + "Files done: 210/234\n", + "Files done: 220/234\n", + "Files done: 230/234\n", + "Name: Female1\n", + "Real prediction: [0, 0, 0, 1]\n", + "Files on dir: 203\n", + "Files done: 10/203\n", + "Files done: 20/203\n", + "Files done: 30/203\n", + "Files done: 40/203\n", + "Files done: 50/203\n", + "Files done: 60/203\n", + "Files done: 70/203\n", + "Files done: 80/203\n", + "Files done: 90/203\n", + "Files done: 100/203\n", + "Files done: 110/203\n", + "Files done: 120/203\n", + "Files done: 130/203\n", + "Files done: 140/203\n", + "Files done: 150/203\n", + "Files done: 160/203\n", + "Files done: 170/203\n", + "Files done: 180/203\n", + "Files done: 190/203\n", + "Files done: 200/203\n", + "Name: SomePerson\n", + "Real prediction: [0, 0, 0, 0]\n", + "Files on dir: 101\n", + "Files done: 10/101\n", + "Files done: 20/101\n", + "Files done: 30/101\n", + "Files done: 40/101\n", + "Files done: 50/101\n", + "Files done: 60/101\n", + "Files done: 70/101\n", + "Files done: 80/101\n", + "Files done: 90/101\n", + "Files done: 100/101\n" ] } ], @@ -246,53 +252,35 @@ "li_train = list()\n", "li_test = list()\n", "\n", - "for i in range(5):\n", - " if i == 0:\n", - " file = \"Men1/Men1_\"\n", - " pred = [1, 0, 0, 0]\n", - " elif i == 1:\n", - " file = \"Men2/Men2_\"\n", - " pred = [0, 1, 0, 0]\n", - " elif i == 2:\n", - " file = \"Men3/Men3_\"\n", - " pred = [0, 0, 1, 0]\n", - " elif i == 3:\n", - " file = \"Female1/Female1_\"\n", - " pred = [0, 0, 0, 1]\n", - " else:\n", - " file = \"SomePerson/SomePerson_\"\n", - " pred = [0, 0, 0, 0]\n", - " \n", - " li = list() \n", - " counter = 0\n", - " while True:\n", - " try:\n", - " if counter % 10 == 0:\n", - " print(counter, file)\n", - " if len(str(counter)) == 1:\n", - " c = \"00\" + str(counter)\n", - " elif len(str(counter)) == 2:\n", - " c = \"0\" + str(counter)\n", - " else:\n", - " c = str(counter)\n", + "men1 = [\"Men1\", [1, 0, 0, 0]]\n", + "men2 = [\"Men2\", [0, 1, 0, 0]]\n", + "men3 = [\"Men3\", [0, 0, 1, 0]]\n", + "female1 = [\"Female1\", [0, 0, 0, 1]]\n", "\n", - " raw_img = io.imread(file + str(c) + \".jpg\")\n", + "someperson = [\"SomePerson\", [0, 0, 0, 0]]\n", + "\n", + "for person in [men1, men2, men3, female1, someperson]:\n", + " files_on_dir = os.listdir(person[0])\n", + " print(f\"Name: {person[0]}\\nReal prediction: {person[1]}\\nFiles on dir: {len(files_on_dir)}\")\n", + " \n", + " train_threshold = len(files_on_dir) * 0.8\n", + " for file_index in range(len(files_on_dir)):\n", + " file_name = person[0] + \"/\" + files_on_dir[file_index]\n", + " if \".jpg\" in file_name:\n", + " if file_index % 10 == 0:\n", + " print(f\"Files done: {file_index}/{len(files_on_dir)}\")\n", + " \n", + " raw_img = io.imread(file_name)\n", " dtc_img = detector(raw_img, 1)\n", + " \n", " for k, d in enumerate(dtc_img):\n", " shape = sp(raw_img, d)\n", - "\n", - " li.append([facerec.compute_face_descriptor(raw_img, shape), pred])\n", - " \n", - " counter += 1\n", - " except:\n", - " for _ in range(len(li) // 10):\n", - " rand_index = random.randint(0, len(li) - 1)\n", - " li_test.append(li[rand_index])\n", - " del li[rand_index]\n", - " li_train += li\n", + " descriptor = facerec.compute_face_descriptor(raw_img, shape)\n", " \n", - " print(\"Done!\")\n", - " break" + " if file_index <= train_threshold:\n", + " li_train.append([descriptor, person[1]])\n", + " else:\n", + " li_test.append([descriptor, person[1]])" ] }, { @@ -305,7 +293,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "809 88\n" + "719 178\n" ] } ], @@ -323,7 +311,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "809 809 88 88\n" + "719 719 178 178\n" ] } ], @@ -356,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "id": "d8072a52", "metadata": {}, "outputs": [ @@ -365,169 +353,169 @@ "output_type": "stream", "text": [ "Epoch 1/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0565 - accuracy: 0.8529\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0594 - accuracy: 0.8220\n", "Epoch 2/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0130 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 0.9040\n", "Epoch 3/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0110 - accuracy: 0.9082\n", "Epoch 4/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0087 - accuracy: 0.9110\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0081 - accuracy: 0.9166\n", "Epoch 5/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0072 - accuracy: 0.9122\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0079 - accuracy: 0.9138\n", "Epoch 6/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9085\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0071 - accuracy: 0.9305\n", "Epoch 7/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0063 - accuracy: 0.9098\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0067 - accuracy: 0.9179\n", "Epoch 8/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0069 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0070 - accuracy: 0.9152\n", "Epoch 9/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0066 - accuracy: 0.9234\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0061 - accuracy: 0.9249\n", "Epoch 10/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0064 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0067 - accuracy: 0.9096\n", "Epoch 11/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9152\n", "Epoch 12/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0059 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0055 - accuracy: 0.9124\n", "Epoch 13/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9098\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0060 - accuracy: 0.9110\n", "Epoch 14/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9085\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9110\n", "Epoch 15/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0053 - accuracy: 0.9073\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0059 - accuracy: 0.9152\n", "Epoch 16/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9207\n", "Epoch 17/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0061 - accuracy: 0.9061\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 0.9110\n", "Epoch 18/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0055 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0053 - accuracy: 0.9152\n", "Epoch 19/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0050 - accuracy: 0.9122\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 0.9138\n", "Epoch 20/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 0.9124\n", "Epoch 21/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0047 - accuracy: 0.9193\n", "Epoch 22/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9068\n", "Epoch 23/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0047 - accuracy: 0.9172\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0052 - accuracy: 0.9152\n", "Epoch 24/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9179\n", "Epoch 25/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9221\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 0.9221\n", "Epoch 26/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9098\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0050 - accuracy: 0.9166\n", "Epoch 27/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 0.9209\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9138\n", "Epoch 28/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9172\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0050 - accuracy: 0.9166\n", "Epoch 29/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9124\n", "Epoch 30/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 0.9124\n", "Epoch 31/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9172\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9166\n", "Epoch 32/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9209\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9152\n", "Epoch 33/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9138\n", "Epoch 34/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9166\n", "Epoch 35/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9234\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9152\n", "Epoch 36/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9152\n", "Epoch 37/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9124\n", "Epoch 38/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9110\n", "Epoch 39/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9122\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 0.9249\n", "Epoch 40/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9138\n", "Epoch 41/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9073\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9110\n", "Epoch 42/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9110\n", "Epoch 43/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9098\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9124\n", "Epoch 44/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9110\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9179\n", "Epoch 45/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9122\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9124\n", "Epoch 46/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9110\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9166\n", "Epoch 47/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9122\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0035 - accuracy: 0.9124\n", "Epoch 48/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9172\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9138\n", "Epoch 49/500\n", - "26/26 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[==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9193\n", "Epoch 460/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9166\n", "Epoch 461/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9138\n", "Epoch 462/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9138\n", "Epoch 463/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9138\n", "Epoch 464/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9207\n", "Epoch 465/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9152\n", "Epoch 466/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9234\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9068\n", "Epoch 467/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9110\n", "Epoch 468/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9096\n", "Epoch 469/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9221\n", "Epoch 470/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9124\n", "Epoch 471/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9122\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9013\n", "Epoch 472/500\n", - "26/26 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 0.9209\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9207\n", "Epoch 473/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9234\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9124\n", "Epoch 474/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9209\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9179\n", "Epoch 475/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9221\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9193\n", "Epoch 476/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9147\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9193\n", "Epoch 477/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9172\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9096\n", "Epoch 478/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9221\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9207\n", "Epoch 479/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9061\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9082\n", "Epoch 480/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9166\n", "Epoch 481/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9082\n", "Epoch 482/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9197\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9152\n", "Epoch 483/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9234\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9124\n", "Epoch 484/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9110\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9193\n", "Epoch 485/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9193\n", "Epoch 486/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9197\n", - "Epoch 487/500\n" + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9138\n", + "Epoch 487/500\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9082\n", + "Epoch 488/500\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9147\n", - "Epoch 488/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9124\n", "Epoch 489/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9138\n", "Epoch 490/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9234\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9138\n", "Epoch 491/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9124\n", "Epoch 492/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9098\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9166\n", "Epoch 493/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9135\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n", "Epoch 494/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9179\n", "Epoch 495/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9193\n", "Epoch 496/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9159\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9082\n", "Epoch 497/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9221\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9193\n", "Epoch 498/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9061\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9096\n", "Epoch 499/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9221\n", + "23/23 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9179\n", "Epoch 500/500\n", - "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9135\n" + "23/23 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9054\n" ] } ], @@ -1417,13 +1405,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "id": "d66ef167", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1453,7 +1441,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "id": "910499e0", "metadata": {}, "outputs": [ @@ -1461,16 +1449,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "26/26 [==============================] - 0s 1ms/step - loss: 0.0013 - accuracy: 0.9642\n" + "23/23 [==============================] - 0s 1ms/step - loss: 9.0275e-04 - accuracy: 0.9166\n" ] }, { "data": { "text/plain": [ - "[0.0013105241814628243, 0.9641532897949219]" + "[0.0009027527994476259, 0.9165507555007935]" ] }, - "execution_count": 18, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }