{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"nncw.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPZ+y9ClSPYLOPAXqmN3d6g"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"TGIxH9Tmt5zp"},"source":["import numpy as np\r\n","import pandas as pd\r\n","import tensorflow as tf\r\n","import matplotlib.pyplot as plt\r\n","import matplotlib as mpl\r\n","from sklearn.model_selection import train_test_split\r\n","\r\n","%load_ext tensorboard"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fksHv5rXACEX"},"source":["# Neural Network Training\r\n"]},{"cell_type":"markdown","metadata":{"id":"l4zqVWyRAM0Z"},"source":["## Load Dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":314},"id":"Hj5l_tdZuYh7","executionInfo":{"status":"ok","timestamp":1614723046534,"user_tz":0,"elapsed":681,"user":{"displayName":"Andy Pack","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64","userId":"16615063155528027547"}},"outputId":"c7ffb838-3582-4e41-9075-5126b2dc323c"},"source":["data = pd.read_csv('features.csv', header=None).T\r\n","data.columns = ['Clump thickness', 'Uniformity of cell size', 'Uniformity of cell shape', 'Marginal adhesion', 'Single epithelial cell size', 'Bare nuclei', 'Bland chomatin', 'Normal nucleoli', 'Mitoses']\r\n","labels = pd.read_csv('targets.csv', header=None).T\r\n","labels.columns = ['Benign', 'Malignant']\r\n","data.describe()"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n"," | Clump thickness | \n","Uniformity of cell size | \n","Uniformity of cell shape | \n","Marginal adhesion | \n","Single epithelial cell size | \n","Bare nuclei | \n","Bland chomatin | \n","Normal nucleoli | \n","Mitoses | \n","
---|---|---|---|---|---|---|---|---|---|
count | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","699.000000 | \n","
mean | \n","0.441774 | \n","0.313448 | \n","0.320744 | \n","0.280687 | \n","0.321602 | \n","0.354363 | \n","0.343777 | \n","0.286695 | \n","0.158941 | \n","
std | \n","0.281574 | \n","0.305146 | \n","0.297191 | \n","0.285538 | \n","0.221430 | \n","0.360186 | \n","0.243836 | \n","0.305363 | \n","0.171508 | \n","
min | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","
25% | \n","0.200000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.200000 | \n","0.100000 | \n","0.200000 | \n","0.100000 | \n","0.100000 | \n","
50% | \n","0.400000 | \n","0.100000 | \n","0.100000 | \n","0.100000 | \n","0.200000 | \n","0.100000 | \n","0.300000 | \n","0.100000 | \n","0.100000 | \n","
75% | \n","0.600000 | \n","0.500000 | \n","0.500000 | \n","0.400000 | \n","0.400000 | \n","0.500000 | \n","0.500000 | \n","0.400000 | \n","0.100000 | \n","
max | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","1.000000 | \n","