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
array([[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [1], [0], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [0], [0], [1], [0], [1], [0], [0], [0], [1], [0], [0], [0], [1], [1], [0], [1], [0], [1], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]])
222/222 [==============================] - 0s 41us/step
yhat_probs
array([[6.6917082e-08], [3.1836925e-10], [5.4667616e-06], [1.8223703e-01], [3.1095867e-06], [1.5097857e-04], [4.5718189e-05], [7.5930852e-13], [1.6880035e-04], [1.3288140e-02], [5.7659827e-12], [1.4023453e-01], [1.7783749e-09], [6.5513697e-09], [2.4984330e-02], [1.4648749e-10], [7.1092437e-10], [9.4391787e-08], [3.3584604e-12], [3.5706750e-15], [1.6278539e-08], [2.8434946e-05], [1.4968079e-01], [1.9277036e-02], [8.2872821e-06], [2.1554771e-13], [9.6510246e-13], [6.5667172e-10], [1.1805335e-14], [6.6314167e-16], [2.9190859e-01], [1.3816059e-03], [1.3611817e-08], [2.2496453e-01], [7.3589586e-11], [1.7558408e-01], [4.1397615e-13], [3.4772019e-10], [8.0708340e-15], [3.6735654e-15], [7.4476058e-15], [6.8505912e-16], [3.2041798e-21], [5.2791400e-11], [1.3077998e-20], [1.1017170e-16], [1.6713089e-11], [4.5752525e-04], [6.1873851e-17], [1.9605683e-05], [9.4469010e-09], [1.3892949e-03], [1.0023251e-24], [2.8460252e-11], [6.0486598e-12], [1.3625690e-01], [3.9178820e-11], [2.2203933e-10], [1.7749695e-05], [6.4421554e-12], [2.1966143e-14], [7.9974957e-11], [1.5999709e-16], [4.1275853e-18], [3.8494538e-05], [6.1403797e-14], [2.6268825e-05], [5.1562085e-15], [5.5857907e-08], [1.4457107e-04], [6.1335893e-05], [7.7071474e-12], [6.1255634e-02], [1.6590753e-14], [1.5680280e-09], [5.0941078e-18], [4.4291333e-17], [1.0358824e-11], [1.7932426e-13], [2.2268671e-01], [5.3908111e-12], [2.3252254e-07], [3.2165945e-03], [2.6685211e-17], [1.5043930e-16], [5.0222014e-12], [4.5999107e-01], [3.6081979e-12], [2.1784366e-13], [2.6682969e-06], [6.0402579e-11], [5.2944342e-12], [7.3653606e-15], [1.8909825e-11], [8.4003619e-19], [2.8381097e-08], [9.0864980e-01], [5.3596083e-10], [1.2201460e-08], [1.7540346e-05], [1.3301977e-12], [8.4892855e-16], [1.3332984e-13], [6.8948352e-06], [3.5327668e-11], [3.7449583e-06], [2.3631440e-15], [6.1335384e-27], [5.7567992e-11], [2.5187624e-01], [2.4674714e-02], [2.3528137e-26], [8.3174409e-06], [7.6118439e-01], [3.5106408e-22], [4.8422009e-12], [2.7843797e-11], [4.6060639e-05], [8.6119653e-11], [2.6830228e-06], [4.6191552e-05], [9.9264142e-17], [1.4450486e-06], [2.7200003e-10], [3.4447085e-15], [2.8837810e-09], [3.8316561e-13], [3.0595782e-14], [4.0633213e-22], [1.7749611e-18], [3.7291792e-09], [3.4328491e-06], [7.2747938e-18], [1.2444002e-13], [3.3164327e-13], [8.2992017e-03], [1.0376709e-17], [6.2153108e-06], [6.0181200e-19], [1.0938542e-13], [3.7213979e-13], [2.1375703e-12], [6.5291723e-18], [9.7808564e-01], [8.4773076e-01], [4.2903647e-01], [9.2407870e-01], [5.7650989e-01], [7.7931166e-01], [7.2734088e-01], [9.0189207e-01], [9.5244807e-01], [5.0026423e-01], [9.5092046e-01], [7.6472461e-01], [8.9610028e-01], [2.0508477e-01], [2.8876670e-07], [8.6479616e-01], [7.2475424e-13], [7.9789376e-01], [1.2903409e-10], [3.4601504e-01], [2.7948898e-01], [9.8436570e-01], [1.4622968e-12], [2.9974550e-01], [9.1367960e-04], [9.2709219e-01], [5.0832611e-01], [8.2115994e-06], [8.6799479e-01], [3.4182894e-13], [5.2238452e-01], [9.4687998e-02], [5.3101248e-14], [2.9437741e-12], [5.3163790e-06], [1.9590214e-08], [6.4985251e-01], [5.0841122e-06], [2.8988365e-05], [6.8423923e-08], [7.2152034e-08], [1.3820352e-07], [1.9109190e-02], [1.2427479e-08], [5.5215492e-07], [6.9070006e-07], [2.5685617e-01], [3.0896217e-02], [6.7491274e-06], [1.4746487e-03], [1.3130754e-01], [6.6363982e-06], [2.6965916e-02], [3.4940243e-04], [1.0800200e-04], [3.3926800e-01], [6.7242166e-16], [1.9176754e-07], [1.9367945e-10], [5.4902671e-10], [1.4577897e-10], [9.9312901e-01], [3.7500739e-02], [2.5908550e-08], [2.2315979e-04], [9.6669810e-09], [5.5374912e-05], [1.0538148e-04], [3.8928630e-10], [2.3698128e-06], [8.3796732e-07], [2.7452695e-01], [4.7966885e-05], [1.9950399e-08], [2.6169717e-03], [2.5515497e-02], [2.0359782e-06], [2.6465039e-12], [7.2048937e-18], [6.4730434e-09]], dtype=float32)
yhat_classes
array([[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [1], [0], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [0], [0], [1], [0], [1], [0], [0], [0], [1], [0], [0], [0], [1], [1], [0], [1], [0], [1], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]])
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]]