2020-05-25 15:39:48 +02:00
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{
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"cells": [
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 14,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install cv2"
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]
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 47,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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"import cv2\n",
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"from tqdm import tqdm"
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]
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 76,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"DATADIR = \"dataset/fruits-360/Training\"\n",
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"CATEGORIES = [\"Apple Red 1\", \"Banana\"]\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 77,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"for category in CATEGORIES: # do dogs and cats\n",
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|
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" path = os.path.join(DATADIR,category) # create path to dogs and cats\n",
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" for img in os.listdir(path): # iterate over each image per dogs and cats\n",
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" img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) # convert to array\n",
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" plt.imshow(img_array, cmap='gray') # graph it\n",
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" plt.show() # display!\n",
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"\n",
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" break # we just want one for now so break\n",
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" break #...and one more!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 85,
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2020-05-25 15:39:48 +02:00
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████████████████████████████████| 492/492 [00:00<00:00, 1702.96it/s]\n",
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"982\n"
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]
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}
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],
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"source": [
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"training_data = []\n",
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2020-05-25 18:40:29 +02:00
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"IMG_SIZE = 32\n",
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2020-05-25 15:39:48 +02:00
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"\n",
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"def create_training_data():\n",
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" for category in CATEGORIES: # do dogs and cats\n",
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"\n",
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" path = os.path.join(DATADIR,category) # create path to dogs and cats\n",
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" class_num = CATEGORIES.index(category) # get the classification (0 or a 1). 0=dog 1=cat\n",
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"\n",
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" for img in tqdm(os.listdir(path)): # iterate over each image per dogs and cats\n",
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" try:\n",
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" img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) # convert to array\n",
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" new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) # resize to normalize data size\n",
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" training_data.append([new_array, class_num]) # add this to our training_data\n",
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" except Exception as e: # in the interest in keeping the output clean...\n",
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" pass\n",
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" #except OSError as e:\n",
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" # print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n",
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" #except Exception as e:\n",
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" # print(\"general exception\", e, os.path.join(path,img))\n",
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"\n",
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"create_training_data()\n",
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"\n",
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"print(len(training_data))"
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]
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 86,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"330\n"
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2020-05-25 15:39:48 +02:00
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[[[1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [[1. ],\n",
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"\n",
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"\n",
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2020-05-25 18:40:29 +02:00
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2020-05-25 15:39:48 +02:00
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2020-05-25 18:40:29 +02:00
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" [1. ]]]])"
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]
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},
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"execution_count": 86,
|
2020-05-25 15:39:48 +02:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
|
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|
|
"DATADIR_test = \"dataset/fruits-360/Test\"\n",
|
2020-05-25 18:40:29 +02:00
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|
|
"CATEGORIES_test = [\"Apple Red 1\", \"Banana\"]\n",
|
2020-05-25 15:39:48 +02:00
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"\n",
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|
"IMG_SIZE = 32\n",
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"\n",
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|
"#new_array_test = cv2.resize(img_array_test, (IMG_SIZE, IMG_SIZE))\n",
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"\n",
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"\n",
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"test_data = []\n",
|
|
|
|
|
"def create_test_data():\n",
|
|
|
|
|
" for category in CATEGORIES_test: # do dogs and cats\n",
|
|
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|
"\n",
|
|
|
|
|
" path = os.path.join(DATADIR_test,category) # create path to dogs and cats\n",
|
|
|
|
|
" class_num = CATEGORIES_test.index(category) # get the classification (0 or a 1). 0=dog 1=cat\n",
|
|
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|
"\n",
|
|
|
|
|
" for img in tqdm(os.listdir(path)): # iterate over each image per dogs and cats\n",
|
|
|
|
|
" try:\n",
|
|
|
|
|
" img_array_test = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) # convert to array\n",
|
|
|
|
|
" new_array_test = cv2.resize(img_array_test, (IMG_SIZE, IMG_SIZE)) # resize to normalize data size\n",
|
|
|
|
|
" test_data.append([new_array_test, class_num]) # add this to our training_data\n",
|
|
|
|
|
" except Exception as e: # in the interest in keeping the output clean...\n",
|
|
|
|
|
" pass\n",
|
|
|
|
|
" #except OSError as e:\n",
|
|
|
|
|
" # print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n",
|
|
|
|
|
" #except Exception as e:\n",
|
|
|
|
|
" # print(\"general exception\", e, os.path.join(path,img))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"create_test_data()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"print(len(test_data))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"import random\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"random.shuffle(test_data)\n",
|
|
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|
|
"\n",
|
|
|
|
|
"X_test = []\n",
|
|
|
|
|
"y_test = []\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"for features,label in test_data:\n",
|
|
|
|
|
" X_test.append(features)\n",
|
|
|
|
|
" y_test.append(label)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"X_test = np.array(X_test).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
|
|
|
|
|
"y_test = np.array(y_test)\n",
|
|
|
|
|
"X_test = X_test/255.0\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"X_test"
|
|
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|
]
|
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},
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{
|
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|
"cell_type": "code",
|
2020-05-25 18:40:29 +02:00
|
|
|
"execution_count": 87,
|
2020-05-25 15:39:48 +02:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
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|
|
|
{
|
|
|
|
|
"data": {
|
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|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
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|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
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|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
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|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
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|
|
|
]
|
|
|
|
|
},
|
|
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|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
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|
},
|
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|
"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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2020-05-25 18:40:29 +02:00
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}
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],
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"source": [
|
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|
|
"for category in CATEGORIES_test: # do dogs and cats\n",
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|
" path = os.path.join(DATADIR_test,category) # create path to dogs and cats\n",
|
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|
" for img in os.listdir(path): # iterate over each image per dogs and cats\n",
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" img_array_OK = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) # convert to array\n",
|
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|
|
" plt.imshow(img_array_OK, cmap='gray') # graph it\n",
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" plt.show() # display!"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": 88,
|
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|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
2020-05-25 15:39:48 +02:00
|
|
|
{
|
2020-05-25 18:40:29 +02:00
|
|
|
"name": "stdout",
|
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|
"output_type": "stream",
|
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|
|
"text": [
|
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|
"0\n",
|
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|
"1\n",
|
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|
"0\n",
|
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"0\n",
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"0\n",
|
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|
"0\n",
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|
"1\n",
|
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|
"0\n",
|
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|
"1\n",
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"0\n"
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|
]
|
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|
}
|
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|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"import random\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"random.shuffle(training_data)\n",
|
|
|
|
|
"for sample in training_data[:10]:\n",
|
|
|
|
|
" print(sample[1])"
|
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|
]
|
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|
},
|
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|
{
|
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|
"cell_type": "code",
|
|
|
|
|
"execution_count": 89,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
2020-05-25 15:39:48 +02:00
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2020-05-25 18:40:29 +02:00
|
|
|
"array([[[[0.99607843],\n",
|
|
|
|
|
" [0.99215686],\n",
|
|
|
|
|
" [0.99607843],\n",
|
2020-05-25 15:39:48 +02:00
|
|
|
" ...,\n",
|
|
|
|
|
" [1. ],\n",
|
|
|
|
|
" [1. ],\n",
|
|
|
|
|
" [1. ]],\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" [[1. ],\n",
|
2020-05-25 18:40:29 +02:00
|
|
|
" [0.99607843],\n",
|
2020-05-25 15:39:48 +02:00
|
|
|
" [1. ],\n",
|
|
|
|
|
" ...,\n",
|
|
|
|
|
" [1. ],\n",
|
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|
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|
" [1. ],\n",
|
|
|
|
|
" [1. ]],\n",
|
|
|
|
|
"\n",
|
2020-05-25 18:40:29 +02:00
|
|
|
" [[0.99607843],\n",
|
2020-05-25 15:39:48 +02:00
|
|
|
" [1. ],\n",
|
2020-05-25 18:40:29 +02:00
|
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" ...,\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [1. ],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" ...,\n",
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"\n",
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2020-05-25 18:40:29 +02:00
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" [[0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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2020-05-25 18:40:29 +02:00
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" [[0.99607843],\n",
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" [0.99607843],\n",
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" [0.99607843],\n",
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" ...,\n",
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2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ]]],\n",
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"\n",
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"\n",
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" [[[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" ...,\n",
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"\n",
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" ...,\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [1. ],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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2020-05-25 18:40:29 +02:00
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2020-05-25 15:39:48 +02:00
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"\n",
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" [[1. ],\n",
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" ...,\n",
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"\n",
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"\n",
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2020-05-25 18:40:29 +02:00
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" [[[0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [0.99607843],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [0.99607843],\n",
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2020-05-25 18:40:29 +02:00
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" ...,\n",
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2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [1. ],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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" ...,\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" ...,\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ]]],\n",
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"\n",
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"\n",
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" ...,\n",
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"\n",
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"\n",
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" [[[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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" ...,\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
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" [0.99607843],\n",
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2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]]],\n",
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"\n",
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"\n",
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" [[[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]]],\n",
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"\n",
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"\n",
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" [[[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [1. ],\n",
|
2020-05-25 15:39:48 +02:00
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" [1. ]],\n",
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"\n",
|
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
|
2020-05-25 18:40:29 +02:00
|
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" [1. ],\n",
|
|
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" [0.99215686],\n",
|
2020-05-25 15:39:48 +02:00
|
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|
" [1. ]],\n",
|
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|
"\n",
|
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" [[1. ],\n",
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" [1. ],\n",
|
2020-05-25 18:40:29 +02:00
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" [1. ],\n",
|
2020-05-25 15:39:48 +02:00
|
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" ...,\n",
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" [0.99607843],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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2020-05-25 18:40:29 +02:00
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" [0.99607843],\n",
|
2020-05-25 15:39:48 +02:00
|
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
|
2020-05-25 18:40:29 +02:00
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|
" [[0.99607843],\n",
|
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" [0.99607843],\n",
|
2020-05-25 15:39:48 +02:00
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]],\n",
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"\n",
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" [[1. ],\n",
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" [1. ],\n",
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" [1. ],\n",
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" ...,\n",
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" [1. ],\n",
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" [1. ],\n",
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" [1. ]]]])"
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]
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},
|
2020-05-25 18:40:29 +02:00
|
|
|
"execution_count": 89,
|
2020-05-25 15:39:48 +02:00
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|
|
"metadata": {},
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|
"output_type": "execute_result"
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}
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],
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"source": [
|
|
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|
"X = []\n",
|
|
|
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|
"y = []\n",
|
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|
"\n",
|
|
|
|
|
"for features,label in training_data:\n",
|
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|
" X.append(features)\n",
|
|
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|
" y.append(label)\n",
|
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|
"\n",
|
|
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|
"\n",
|
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|
"X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
|
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|
"y = np.array(y)\n",
|
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|
"X = X/255.0\n",
|
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|
"\n",
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"X"
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]
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},
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{
|
|
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|
"cell_type": "code",
|
2020-05-25 18:40:29 +02:00
|
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"execution_count": 90,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"import tensorflow as tf\n",
|
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|
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"from tensorflow.keras.datasets import cifar10\n",
|
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"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
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"from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
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"import time\n",
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"from tensorflow.keras.callbacks import TensorBoard\n"
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]
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 93,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 0.7000 - accuracy: 0.43 - ETA: 3s - loss: 0.6784 - accuracy: 0.46 - ETA: 3s - loss: 0.7304 - accuracy: 0.47 - ETA: 4s - loss: 0.6721 - accuracy: 0.60 - ETA: 4s - loss: 0.6202 - accuracy: 0.65 - ETA: 4s - loss: 0.5751 - accuracy: 0.69 - ETA: 3s - loss: 0.5313 - accuracy: 0.73 - ETA: 3s - loss: 0.4885 - accuracy: 0.76 - ETA: 3s - loss: 0.4477 - accuracy: 0.79 - ETA: 2s - loss: 0.4109 - accuracy: 0.81 - ETA: 2s - loss: 0.3770 - accuracy: 0.83 - ETA: 2s - loss: 0.3481 - accuracy: 0.84 - ETA: 2s - loss: 0.3226 - accuracy: 0.85 - ETA: 1s - loss: 0.2999 - accuracy: 0.86 - ETA: 1s - loss: 0.2802 - accuracy: 0.87 - ETA: 1s - loss: 0.2629 - accuracy: 0.88 - ETA: 1s - loss: 0.2478 - accuracy: 0.89 - ETA: 0s - loss: 0.2341 - accuracy: 0.89 - ETA: 0s - loss: 0.2218 - accuracy: 0.90 - ETA: 0s - loss: 0.2107 - accuracy: 0.90 - ETA: 0s - loss: 0.2007 - accuracy: 0.91 - ETA: 0s - loss: 0.1963 - accuracy: 0.91 - 6s 275ms/step - loss: 0.1963 - accuracy: 0.9141 - val_loss: 1.3482e-04 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 2/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 2.3973e-04 - accuracy: 1.00 - ETA: 2s - loss: 1.6589e-04 - accuracy: 1.00 - ETA: 3s - loss: 1.1534e-04 - accuracy: 1.00 - ETA: 3s - loss: 9.7862e-05 - accuracy: 1.00 - ETA: 3s - loss: 8.3413e-05 - accuracy: 1.00 - ETA: 3s - loss: 9.5683e-05 - accuracy: 1.00 - ETA: 3s - loss: 1.1356e-04 - accuracy: 1.00 - ETA: 2s - loss: 1.0275e-04 - accuracy: 1.00 - ETA: 2s - loss: 1.0816e-04 - accuracy: 1.00 - ETA: 2s - loss: 1.1923e-04 - accuracy: 1.00 - ETA: 2s - loss: 1.1239e-04 - accuracy: 1.00 - ETA: 2s - loss: 1.0340e-04 - accuracy: 1.00 - ETA: 1s - loss: 9.6399e-05 - accuracy: 1.00 - ETA: 1s - loss: 8.9736e-05 - accuracy: 1.00 - ETA: 1s - loss: 8.4122e-05 - accuracy: 1.00 - ETA: 1s - loss: 7.9141e-05 - accuracy: 1.00 - ETA: 1s - loss: 7.5065e-05 - accuracy: 1.00 - ETA: 0s - loss: 7.1371e-05 - accuracy: 1.00 - ETA: 0s - loss: 6.7650e-05 - accuracy: 1.00 - ETA: 0s - loss: 6.4359e-05 - accuracy: 1.00 - ETA: 0s - loss: 6.1338e-05 - accuracy: 1.00 - ETA: 0s - loss: 6.0014e-05 - accuracy: 1.00 - 6s 250ms/step - loss: 6.0014e-05 - accuracy: 1.0000 - val_loss: 2.2133e-06 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 3/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 3.4949e-07 - accuracy: 1.00 - ETA: 4s - loss: 2.6784e-07 - accuracy: 1.00 - ETA: 5s - loss: 4.5241e-07 - accuracy: 1.00 - ETA: 5s - loss: 4.0997e-07 - accuracy: 1.00 - ETA: 5s - loss: 3.9216e-07 - accuracy: 1.00 - ETA: 5s - loss: 7.0848e-07 - accuracy: 1.00 - ETA: 4s - loss: 6.1749e-07 - accuracy: 1.00 - ETA: 4s - loss: 7.2774e-07 - accuracy: 1.00 - ETA: 4s - loss: 7.2770e-07 - accuracy: 1.00 - ETA: 3s - loss: 6.6690e-07 - accuracy: 1.00 - ETA: 3s - loss: 6.1100e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.8800e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.5553e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.2389e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.6763e-07 - accuracy: 1.00 - ETA: 1s - loss: 9.7040e-07 - accuracy: 1.00 - ETA: 1s - loss: 9.1789e-07 - accuracy: 1.00 - ETA: 1s - loss: 8.6942e-07 - accuracy: 1.00 - ETA: 0s - loss: 9.2821e-07 - accuracy: 1.00 - ETA: 0s - loss: 9.8482e-07 - accuracy: 1.00 - ETA: 0s - loss: 9.8677e-07 - accuracy: 1.00 - ETA: 0s - loss: 1.0926e-06 - accuracy: 1.00 - 7s 326ms/step - loss: 1.0926e-06 - accuracy: 1.0000 - val_loss: 1.1274e-06 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 4/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 9.3543e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.6372e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.7405e-07 - accuracy: 1.00 - ETA: 4s - loss: 3.6809e-07 - accuracy: 1.00 - ETA: 4s - loss: 5.5971e-07 - accuracy: 1.00 - ETA: 4s - loss: 5.5550e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.2300e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.5279e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.0537e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.9800e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.0414e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.4073e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.1244e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.0896e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.6236e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.6817e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.4127e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.1969e-07 - accuracy: 1.00 - ETA: 0s - loss: 5.3040e-07 - accuracy: 1.00 - ETA: 0s - loss: 5.4725e-07 - accuracy: 1.00 - ETA: 0s - loss: 5.2317e-07 - accuracy: 1.00 - ETA: 0s - loss: 5.1945e-07 - accuracy: 1.00 - 6s 288ms/step - loss: 5.1945e-07 - accuracy: 1.0000 - val_loss: 9.0020e-07 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 5/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 3.5776e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.6172e-07 - accuracy: 1.00 - ETA: 3s - loss: 3.9244e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.7938e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.3512e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.9126e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.1785e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.6509e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.7543e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.7773e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.2520e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.9695e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.9290e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.8368e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.4872e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.2601e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.1352e-07 - accuracy: 1.00 - ETA: 0s - loss: 5.2218e-07 - accuracy: 1.00 - ETA: 0s - loss: 5.0176e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.8196e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.7142e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.7855e-07 - accuracy: 1.00 - 6s 256ms/step - loss: 4.7855e-07 - accuracy: 1.0000 - val_loss: 8.1100e-07 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 6/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 1.6182e-07 - accuracy: 1.00 - ETA: 2s - loss: 1.9603e-07 - accuracy: 1.00 - ETA: 3s - loss: 1.8195e-07 - accuracy: 1.00 - ETA: 3s - loss: 1.8987e-07 - accuracy: 1.00 - ETA: 3s - loss: 3.6627e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.1952e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.6124e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.7919e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.2213e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.3192e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.1827e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.0402e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.9070e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.7234e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.7557e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.5591e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.4145e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.7361e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.5363e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.4301e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.4301e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.4066e-07 - accuracy: 1.00 - 6s 266ms/step - loss: 4.4066e-07 - accuracy: 1.0000 - val_loss: 7.4324e-07 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 7/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 5.3206e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.0425e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.7385e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.1628e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.3873e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.2209e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.4227e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.0069e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.7068e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.7892e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.6228e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.3152e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.1876e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.5285e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.2686e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.2191e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.4233e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.3127e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.3422e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.2765e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.4732e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.3941e-07 - accuracy: 1.00 - 5s 246ms/step - loss: 4.3941e-07 - accuracy: 1.0000 - val_loss: 7.2757e-07 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 8/10\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 1.7271e-07 - accuracy: 1.00 - ETA: 2s - loss: 1.9229e-07 - accuracy: 1.00 - ETA: 2s - loss: 2.8270e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.3243e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.3498e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.9688e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.4403e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.4043e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.0607e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.5414e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.4423e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.1843e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.8989e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.7154e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.4475e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.2086e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.2447e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.0324e-07 - accuracy: 1.00 - ETA: 0s - loss: 3.9401e-07 - accuracy: 1.00 - ETA: 0s - loss: 3.9920e-07 - accuracy: 1.00 - ETA: 0s - loss: 3.9091e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.3060e-07 - accuracy: 1.00 - 5s 225ms/step - loss: 4.3060e-07 - accuracy: 1.0000 - val_loss: 7.5845e-07 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 9/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 1.6855e-07 - accuracy: 1.00 - ETA: 2s - loss: 1.4520e-07 - accuracy: 1.00 - ETA: 2s - loss: 1.4985e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.5213e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.2928e-07 - accuracy: 1.00 - ETA: 3s - loss: 4.9254e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.7473e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.8662e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.3971e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.3172e-07 - accuracy: 1.00 - ETA: 2s - loss: 4.0025e-07 - accuracy: 1.00 - ETA: 2s - loss: 3.8896e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.0113e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.3684e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.2528e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.5092e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.2654e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.2406e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.0759e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.4311e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.2337e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.2162e-07 - accuracy: 1.00 - 5s 236ms/step - loss: 4.2162e-07 - accuracy: 1.0000 - val_loss: 6.9809e-07 - val_accuracy: 1.0000\n",
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2020-05-25 15:39:48 +02:00
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"Epoch 10/10\n",
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2020-05-25 18:40:29 +02:00
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"22/22 [==============================] - ETA: 0s - loss: 7.1469e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.0496e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.0121e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.4408e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.2681e-07 - accuracy: 1.00 - ETA: 3s - loss: 6.1092e-07 - accuracy: 1.00 - ETA: 3s - loss: 5.8013e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.6488e-07 - accuracy: 1.00 - ETA: 2s - loss: 6.3478e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.9166e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.5549e-07 - accuracy: 1.00 - ETA: 2s - loss: 5.9309e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.5233e-07 - accuracy: 1.00 - ETA: 1s - loss: 5.1747e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.9120e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.8762e-07 - accuracy: 1.00 - ETA: 1s - loss: 4.6909e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.6668e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.6091e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.4684e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.2857e-07 - accuracy: 1.00 - ETA: 0s - loss: 4.2247e-07 - accuracy: 1.00 - 5s 242ms/step - loss: 4.2247e-07 - accuracy: 1.0000 - val_loss: 7.0464e-07 - val_accuracy: 1.0000\n"
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2020-05-25 15:39:48 +02:00
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]
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},
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{
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"data": {
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"text/plain": [
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2020-05-25 18:40:29 +02:00
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"<tensorflow.python.keras.callbacks.History at 0x1979bdf9608>"
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2020-05-25 15:39:48 +02:00
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]
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},
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2020-05-25 18:40:29 +02:00
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"execution_count": 93,
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2020-05-25 15:39:48 +02:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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2020-05-25 18:40:29 +02:00
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"import time \n",
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"from datetime import datetime\n",
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"#date = datetime.now()\n",
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"#date = date.strftime(\"%d-%m-%Y-%H-%M-%S\")\n",
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"#NAME = \"Fruits-64x2-CNN-{}\".format(date)\n",
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2020-05-25 15:39:48 +02:00
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"NAME = \"Fruits-64x2-CNN-{}\".format(int(time.time()))\n",
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"\n",
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"tensorboard = TensorBoard(log_dir='logs/{}'.format(NAME))\n",
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"model = Sequential()\n",
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"\n",
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"model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n",
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"model.add(Activation('relu'))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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"\n",
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"model.add(Conv2D(256, (3, 3)))\n",
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"model.add(Activation('relu'))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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"\n",
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"model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n",
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"\n",
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"model.add(Dense(64))\n",
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"\n",
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"model.add(Dense(1))\n",
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"model.add(Activation('sigmoid'))\n",
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"\n",
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"model.compile(loss='binary_crossentropy',\n",
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" optimizer='adam',\n",
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" metrics=['accuracy'])\n",
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"\n",
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"model.fit(X,\n",
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" y, \n",
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" batch_size=32, epochs=10, validation_split=0.3, \n",
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" validation_data = (X_test, y_test),\n",
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" callbacks=[tensorboard])"
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]
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},
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{
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"cell_type": "code",
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2020-05-25 18:40:29 +02:00
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'25-05-2020-17-11-43'"
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]
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},
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"execution_count": 35,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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{
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"cell_type": "code",
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"execution_count": 97,
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2020-05-25 15:39:48 +02:00
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"data": {
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"text/plain": [
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2020-05-25 18:40:29 +02:00
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"Reusing TensorBoard on port 6006 (pid 19136), started 5:12:32 ago. (Use '!kill 19136' to kill it.)"
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2020-05-25 15:39:48 +02:00
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]
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/html": [
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"\n",
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2020-05-25 18:40:29 +02:00
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" <iframe id=\"tensorboard-frame-4722089ca21acafb\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
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" </iframe>\n",
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" <script>\n",
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" (function() {\n",
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" const frame = document.getElementById(\"tensorboard-frame-4722089ca21acafb\");\n",
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2020-05-25 15:39:48 +02:00
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" const url = new URL(\"/\", window.location);\n",
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" url.port = 6006;\n",
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" frame.src = url;\n",
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" })();\n",
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" </script>\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"%tensorboard --logdir logs"
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]
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{
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"cell_type": "code",
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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