from keras.models import Sequential
from keras.layers import Dense
import pandas as pd
import numpy as np
import pickle
data = pd.read_csv("mlh1.csv",header=None)
data.shape
small = data.dropna()
small.shape
X = small.iloc[:,0:4].values
y = small.iloc[:,4].values
print(data)
0 1 2 3 4 0 0.9908 -0.6535 0.6548 -2.2655 0 1 -0.3444 0.5817 1.8871 -1.2221 0 2 0.1206 0.9814 1.1101 -1.1757 0 3 0.9666 -0.1179 0.5870 0.2025 0 4 -0.3356 1.3352 1.5351 -0.6308 0 .. ... ... ... ... .. 218 1.7897 -0.1683 -0.2239 0.0128 0 219 0.8280 1.7848 0.4983 -1.0822 0 220 -1.0404 0.0381 -1.8959 -0.4171 0 221 -2.0500 -0.7193 0.3134 -2.6699 0 222 1.4275 -0.1427 0.1080 -2.5215 0 [223 rows x 5 columns]
model = Sequential()
model.add(Dense(12, input_dim=4, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
history=model.fit(X, y, epochs=150, batch_size=10)
Epoch 1/150 23/23 [==============================] - 1s 2ms/step - loss: 0.6689 - accuracy: 0.6054 Epoch 2/150 23/23 [==============================] - 0s 2ms/step - loss: 0.5538 - accuracy: 0.8655 Epoch 3/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4537 - accuracy: 0.9013 Epoch 4/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3808 - accuracy: 0.9013 Epoch 5/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3270 - accuracy: 0.8969 Epoch 6/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2872 - accuracy: 0.9013 Epoch 7/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2538 - accuracy: 0.9103 Epoch 8/150 23/23 [==============================] - 0s 2ms/step - loss: 0.2278 - accuracy: 0.9148 Epoch 9/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2086 - accuracy: 0.9327 Epoch 10/150 23/23 [==============================] - 0s 2ms/step - loss: 0.1946 - accuracy: 0.9327 Epoch 11/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1823 - accuracy: 0.9462 Epoch 12/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1737 - accuracy: 0.9507 Epoch 13/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1654 - accuracy: 0.9552 Epoch 14/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1587 - accuracy: 0.9596 Epoch 15/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1530 - accuracy: 0.9552 Epoch 16/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1492 - accuracy: 0.9552 Epoch 17/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1450 - accuracy: 0.9552 Epoch 18/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1421 - accuracy: 0.9552 Epoch 19/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1396 - accuracy: 0.9552 Epoch 20/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1375 - accuracy: 0.9507 Epoch 21/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1363 - accuracy: 0.9507 Epoch 22/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1344 - accuracy: 0.9552 Epoch 23/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1329 - accuracy: 0.9552 Epoch 24/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1314 - accuracy: 0.9552 Epoch 25/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1309 - accuracy: 0.9552 Epoch 26/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1285 - accuracy: 0.9507 Epoch 27/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1288 - accuracy: 0.9462 Epoch 28/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1270 - accuracy: 0.9507 Epoch 29/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1263 - accuracy: 0.9552 Epoch 30/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1253 - accuracy: 0.9596 Epoch 31/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1243 - accuracy: 0.9507 Epoch 32/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1237 - accuracy: 0.9507 Epoch 33/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1245 - accuracy: 0.9462 Epoch 34/150 23/23 [==============================] - 0s 2ms/step - loss: 0.1251 - accuracy: 0.9596 Epoch 35/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1224 - accuracy: 0.9596 Epoch 36/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1220 - accuracy: 0.9552 Epoch 37/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1211 - accuracy: 0.9507 Epoch 38/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1209 - accuracy: 0.9507 Epoch 39/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1203 - accuracy: 0.9507 Epoch 40/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1202 - accuracy: 0.9507 Epoch 41/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1201 - accuracy: 0.9462 Epoch 42/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1195 - accuracy: 0.9462 Epoch 43/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1194 - accuracy: 0.9552 Epoch 44/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1198 - accuracy: 0.9552 Epoch 45/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1188 - accuracy: 0.9507 Epoch 46/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1181 - accuracy: 0.9507 Epoch 47/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1180 - accuracy: 0.9507 Epoch 48/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1173 - accuracy: 0.9552 Epoch 49/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1191 - accuracy: 0.9552 Epoch 50/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1182 - accuracy: 0.9462 Epoch 51/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1168 - accuracy: 0.9507 Epoch 52/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1177 - accuracy: 0.9507 Epoch 53/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1163 - accuracy: 0.9507 Epoch 54/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1160 - accuracy: 0.9552 Epoch 55/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1170 - accuracy: 0.9596 Epoch 56/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1157 - accuracy: 0.9552 Epoch 57/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1156 - accuracy: 0.9552 Epoch 58/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1161 - accuracy: 0.9462 Epoch 59/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1150 - accuracy: 0.9552 Epoch 60/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1157 - accuracy: 0.9552 Epoch 61/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1150 - accuracy: 0.9552 Epoch 62/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1145 - accuracy: 0.9552 Epoch 63/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1145 - accuracy: 0.9552 Epoch 64/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1146 - accuracy: 0.9507 Epoch 65/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1137 - accuracy: 0.9552 Epoch 66/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1145 - accuracy: 0.9641 Epoch 67/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1142 - accuracy: 0.9596 Epoch 68/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1147 - accuracy: 0.9552 Epoch 69/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1143 - accuracy: 0.9552 Epoch 70/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1131 - accuracy: 0.9552 Epoch 71/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1133 - accuracy: 0.9596 Epoch 72/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1127 - accuracy: 0.9552 Epoch 73/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1126 - accuracy: 0.9552 Epoch 74/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1130 - accuracy: 0.9552 Epoch 75/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1128 - accuracy: 0.9507 Epoch 76/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1126 - accuracy: 0.9552 Epoch 77/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1115 - accuracy: 0.9552 Epoch 78/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1116 - accuracy: 0.9552 Epoch 79/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1120 - accuracy: 0.9552 Epoch 80/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1117 - accuracy: 0.9596 Epoch 81/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1127 - accuracy: 0.9507 Epoch 82/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1108 - accuracy: 0.9552 Epoch 83/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1112 - accuracy: 0.9552 Epoch 84/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1100 - accuracy: 0.9596 Epoch 85/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1102 - accuracy: 0.9552 Epoch 86/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1098 - accuracy: 0.9552 Epoch 87/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1104 - accuracy: 0.9552 Epoch 88/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1098 - accuracy: 0.9552 Epoch 89/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1098 - accuracy: 0.9552 Epoch 90/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1098 - accuracy: 0.9552 Epoch 91/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1089 - accuracy: 0.9552 Epoch 92/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1092 - accuracy: 0.9596 Epoch 93/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1082 - accuracy: 0.9552 Epoch 94/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1090 - accuracy: 0.9552 Epoch 95/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1080 - accuracy: 0.9596 Epoch 96/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1086 - accuracy: 0.9596 Epoch 97/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1081 - accuracy: 0.9596 Epoch 98/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1079 - accuracy: 0.9596 Epoch 99/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1084 - accuracy: 0.9552 Epoch 100/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1069 - accuracy: 0.9596 Epoch 101/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1074 - accuracy: 0.9596 Epoch 102/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1081 - accuracy: 0.9596 Epoch 103/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1066 - accuracy: 0.9596 Epoch 104/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1067 - accuracy: 0.9596 Epoch 105/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1068 - accuracy: 0.9552 Epoch 106/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1063 - accuracy: 0.9596 Epoch 107/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1064 - accuracy: 0.9596 Epoch 108/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1066 - accuracy: 0.9596 Epoch 109/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1059 - accuracy: 0.9596 Epoch 110/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1054 - accuracy: 0.9596 Epoch 111/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1056 - accuracy: 0.9596 Epoch 112/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1062 - accuracy: 0.9552 Epoch 113/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1052 - accuracy: 0.9596 Epoch 114/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1050 - accuracy: 0.9596 Epoch 115/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1048 - accuracy: 0.9641 Epoch 116/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1051 - accuracy: 0.9596 Epoch 117/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1045 - accuracy: 0.9596 Epoch 118/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1043 - accuracy: 0.9596 Epoch 119/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1042 - accuracy: 0.9596 Epoch 120/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1049 - accuracy: 0.9596 Epoch 121/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1036 - accuracy: 0.9596 Epoch 122/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1040 - accuracy: 0.9596 Epoch 123/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1037 - accuracy: 0.9596 Epoch 124/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1045 - accuracy: 0.9596 Epoch 125/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1034 - accuracy: 0.9596 Epoch 126/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1037 - accuracy: 0.9596 Epoch 127/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1030 - accuracy: 0.9596 Epoch 128/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1036 - accuracy: 0.9596 Epoch 129/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1031 - accuracy: 0.9596 Epoch 130/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1034 - accuracy: 0.9641 Epoch 131/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1026 - accuracy: 0.9641 Epoch 132/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1019 - accuracy: 0.9596 Epoch 133/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1021 - accuracy: 0.9596 Epoch 134/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1028 - accuracy: 0.9596 Epoch 135/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1019 - accuracy: 0.9596 Epoch 136/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1024 - accuracy: 0.9641 Epoch 137/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1026 - accuracy: 0.9596 Epoch 138/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1015 - accuracy: 0.9596 Epoch 139/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1010 - accuracy: 0.9552 Epoch 140/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1016 - accuracy: 0.9641 Epoch 141/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1014 - accuracy: 0.9596 Epoch 142/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1007 - accuracy: 0.9596 Epoch 143/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1006 - accuracy: 0.9596 Epoch 144/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1003 - accuracy: 0.9596 Epoch 145/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1002 - accuracy: 0.9596 Epoch 146/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1002 - accuracy: 0.9596 Epoch 147/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0997 - accuracy: 0.9596 Epoch 148/150 23/23 [==============================] - 0s 1ms/step - loss: 0.1013 - accuracy: 0.9596 Epoch 149/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0992 - accuracy: 0.9596 Epoch 150/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0998 - accuracy: 0.9552
from matplotlib.pyplot import figure
figure(num=None, figsize=(8, 6), dpi=300, facecolor='w', edgecolor='k')
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'])
plt.title('CRC mlh1 validation accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['accuracy'], loc='lower right' )
plt.show()
from matplotlib.pyplot import figure
figure(num=None, figsize=(8, 6), dpi=300, facecolor='w', edgecolor='k')
import matplotlib.pyplot as plt
plt.plot(history.history['loss'])
plt.title('CRC mlh1 validation loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['loss'], loc='upper right' )
plt.show()
model.fit(X, y, epochs=150, batch_size=10)
Epoch 1/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0993 - accuracy: 0.9552 Epoch 2/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0992 - accuracy: 0.9552 Epoch 3/150 23/23 [==============================] - 0s 2ms/step - loss: 0.1001 - accuracy: 0.9596 Epoch 4/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0986 - accuracy: 0.9596 Epoch 5/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0988 - accuracy: 0.9596 Epoch 6/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0982 - accuracy: 0.9596 Epoch 7/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0985 - accuracy: 0.9596 Epoch 8/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0983 - accuracy: 0.9596 Epoch 9/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0982 - accuracy: 0.9596 Epoch 10/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0984 - accuracy: 0.9552 Epoch 11/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0992 - accuracy: 0.9552 Epoch 12/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0983 - accuracy: 0.9552 Epoch 13/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0977 - accuracy: 0.9596 Epoch 14/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0973 - accuracy: 0.9596 Epoch 15/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0976 - accuracy: 0.9596 Epoch 16/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0992 - accuracy: 0.9596 Epoch 17/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0975 - accuracy: 0.9552 Epoch 18/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0980 - accuracy: 0.9596 Epoch 19/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0976 - accuracy: 0.9552 Epoch 20/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0972 - accuracy: 0.9596 Epoch 21/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0964 - accuracy: 0.9596 Epoch 22/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0967 - accuracy: 0.9596 Epoch 23/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0962 - accuracy: 0.9596 Epoch 24/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0959 - accuracy: 0.9596 Epoch 25/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0961 - accuracy: 0.9552 Epoch 26/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0961 - accuracy: 0.9552 Epoch 27/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0951 - accuracy: 0.9552 Epoch 28/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0952 - accuracy: 0.9596 Epoch 29/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0950 - accuracy: 0.9596 Epoch 30/150 23/23 [==============================] - 0s 2ms/step - loss: 0.0955 - accuracy: 0.9552 Epoch 31/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0952 - accuracy: 0.9596 Epoch 32/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0965 - accuracy: 0.9552 Epoch 33/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0941 - accuracy: 0.9552 Epoch 34/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0945 - accuracy: 0.9552 Epoch 35/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0950 - accuracy: 0.9552 Epoch 36/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0943 - accuracy: 0.9552 Epoch 37/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0936 - accuracy: 0.9552 Epoch 38/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0939 - accuracy: 0.9552 Epoch 39/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0935 - accuracy: 0.9552 Epoch 40/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0933 - accuracy: 0.9641 Epoch 41/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0940 - accuracy: 0.9596 Epoch 42/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0921 - accuracy: 0.9596 Epoch 43/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0924 - accuracy: 0.9552 Epoch 44/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0929 - accuracy: 0.9552 Epoch 45/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0931 - accuracy: 0.9552 Epoch 46/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0921 - accuracy: 0.9552 Epoch 47/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0924 - accuracy: 0.9552 Epoch 48/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0927 - accuracy: 0.9552 Epoch 49/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0923 - accuracy: 0.9552 Epoch 50/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0931 - accuracy: 0.9552 Epoch 51/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0950 - accuracy: 0.9552 Epoch 52/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0920 - accuracy: 0.9552 Epoch 53/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0921 - accuracy: 0.9552 Epoch 54/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0914 - accuracy: 0.9552 Epoch 55/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0909 - accuracy: 0.9552 Epoch 56/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0911 - accuracy: 0.9552 Epoch 57/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0932 - accuracy: 0.9596 Epoch 58/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0913 - accuracy: 0.9552 Epoch 59/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0924 - accuracy: 0.9507 Epoch 60/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0904 - accuracy: 0.9552 Epoch 61/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0904 - accuracy: 0.9552 Epoch 62/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0905 - accuracy: 0.9552 Epoch 63/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0902 - accuracy: 0.9552 Epoch 64/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0899 - accuracy: 0.9552 Epoch 65/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0898 - accuracy: 0.9552 Epoch 66/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0901 - accuracy: 0.9552 Epoch 67/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0903 - accuracy: 0.9552 Epoch 68/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0890 - accuracy: 0.9552 Epoch 69/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0889 - accuracy: 0.9552 Epoch 70/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0889 - accuracy: 0.9596 Epoch 71/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0896 - accuracy: 0.9552 Epoch 72/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0882 - accuracy: 0.9552 Epoch 73/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0884 - accuracy: 0.9552 Epoch 74/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0895 - accuracy: 0.9552 Epoch 75/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0882 - accuracy: 0.9552 Epoch 76/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0883 - accuracy: 0.9552 Epoch 77/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0878 - accuracy: 0.9552 Epoch 78/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0877 - accuracy: 0.9552 Epoch 79/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0877 - accuracy: 0.9552 Epoch 80/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0885 - accuracy: 0.9552 Epoch 81/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0874 - accuracy: 0.9552 Epoch 82/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0879 - accuracy: 0.9552 Epoch 83/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0871 - accuracy: 0.9596 Epoch 84/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0868 - accuracy: 0.9552 Epoch 85/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0870 - accuracy: 0.9552 Epoch 86/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0863 - accuracy: 0.9552 Epoch 87/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0862 - accuracy: 0.9552 Epoch 88/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0865 - accuracy: 0.9552 Epoch 89/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0867 - accuracy: 0.9552 Epoch 90/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0859 - accuracy: 0.9552 Epoch 91/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0873 - accuracy: 0.9552 Epoch 92/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0869 - accuracy: 0.9552 Epoch 93/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0864 - accuracy: 0.9552 Epoch 94/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0865 - accuracy: 0.9552 Epoch 95/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0874 - accuracy: 0.9552 Epoch 96/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0856 - accuracy: 0.9552 Epoch 97/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0855 - accuracy: 0.9552 Epoch 98/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0854 - accuracy: 0.9552 Epoch 99/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0862 - accuracy: 0.9552 Epoch 100/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0847 - accuracy: 0.9596 Epoch 101/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0851 - accuracy: 0.9552 Epoch 102/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0841 - accuracy: 0.9596 Epoch 103/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0837 - accuracy: 0.9552 Epoch 104/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0845 - accuracy: 0.9552 Epoch 105/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0848 - accuracy: 0.9596 Epoch 106/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0844 - accuracy: 0.9552 Epoch 107/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0840 - accuracy: 0.9596 Epoch 108/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0841 - accuracy: 0.9552 Epoch 109/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0839 - accuracy: 0.9596 Epoch 110/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0839 - accuracy: 0.9552 Epoch 111/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0829 - accuracy: 0.9552 Epoch 112/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0837 - accuracy: 0.9552 Epoch 113/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0827 - accuracy: 0.9552 Epoch 114/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0825 - accuracy: 0.9552 Epoch 115/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0826 - accuracy: 0.9552 Epoch 116/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0828 - accuracy: 0.9596 Epoch 117/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0822 - accuracy: 0.9552 Epoch 118/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0821 - accuracy: 0.9552 Epoch 119/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0816 - accuracy: 0.9552 Epoch 120/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0818 - accuracy: 0.9552 Epoch 121/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0818 - accuracy: 0.9552 Epoch 122/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0823 - accuracy: 0.9596 Epoch 123/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0810 - accuracy: 0.9596 Epoch 124/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0821 - accuracy: 0.9596 Epoch 125/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0819 - accuracy: 0.9641 Epoch 126/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0810 - accuracy: 0.9596 Epoch 127/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0809 - accuracy: 0.9596 Epoch 128/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0807 - accuracy: 0.9552 Epoch 129/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0810 - accuracy: 0.9596 Epoch 130/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0808 - accuracy: 0.9552 Epoch 131/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0810 - accuracy: 0.9596 Epoch 132/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0804 - accuracy: 0.9552 Epoch 133/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0812 - accuracy: 0.9596 Epoch 134/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0807 - accuracy: 0.9596 Epoch 135/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0805 - accuracy: 0.9552 Epoch 136/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0806 - accuracy: 0.9507 Epoch 137/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0797 - accuracy: 0.9596 Epoch 138/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0799 - accuracy: 0.9596 Epoch 139/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0798 - accuracy: 0.9596 Epoch 140/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0794 - accuracy: 0.9596 Epoch 141/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0802 - accuracy: 0.9552 Epoch 142/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0796 - accuracy: 0.9596 Epoch 143/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0792 - accuracy: 0.9596 Epoch 144/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0792 - accuracy: 0.9596 Epoch 145/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0784 - accuracy: 0.9596 Epoch 146/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0795 - accuracy: 0.9552 Epoch 147/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0816 - accuracy: 0.9596 Epoch 148/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0785 - accuracy: 0.9552 Epoch 149/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0786 - accuracy: 0.9552 Epoch 150/150 23/23 [==============================] - 0s 1ms/step - loss: 0.0784 - accuracy: 0.9596
<keras.callbacks.History at 0x21c0c864d08>
_, accuracy = model.evaluate(X, y)
print('Accuracy: %.2f' % (accuracy*100))
7/7 [==============================] - 0s 2ms/step - loss: 0.0773 - accuracy: 0.9641 Accuracy: 96.41
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 12) 60
dense_1 (Dense) (None, 8) 104
dense_2 (Dense) (None, 1) 9
=================================================================
Total params: 173
Trainable params: 173
Non-trainable params: 0
_________________________________________________________________
yhat_probs = model.predict(X)
yhat_classes = (model.predict(X) > 0.5).astype("int32")
yhat_classes
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222/222 [==============================] - 0s 41us/step
yhat_probs
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yhat_classes
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from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, cohen_kappa_score, accuracy_score, roc_auc_score
precision = precision_score(y, yhat_classes)
print('Precision: %f' % precision)
Precision: 0.869565
accuracy = accuracy_score(y, yhat_classes)
print('Accuracy: %f' % accuracy)
Accuracy: 0.964126
recall = recall_score(y, yhat_classes)
print('Recall: %f' % recall)
Recall: 0.800000
f1 = f1_score(y, yhat_classes)
print('F1 score: %f' % f1)
F1 score: 0.833333
kappa = cohen_kappa_score(y, yhat_classes)
print('Cohens kappa: %f' % kappa)
Cohens kappa: 0.813272
auc = roc_auc_score(y, yhat_probs)
print('ROC AUC: %f' % auc)
ROC AUC: 0.992323
matrix = confusion_matrix(y, yhat_classes)
print(matrix)
[[195 3] [ 5 20]]