from keras.models import Sequential
from keras.layers import Dense
import pandas as pd
import numpy as np
import pickle
data = pd.read_csv("msi.csv",header=None)
data.shape
small = data.dropna()
small.shape
X = small.iloc[:,0:4].values
y = small.iloc[:,4].values
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'])
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 1 4 -0.3356 1.3352 1.5351 -0.6308 0 .. ... ... ... ... .. 217 1.7897 -0.1683 -0.2239 0.0128 0 218 0.8280 1.7848 0.4983 -1.0822 0 219 -1.0404 0.0381 -1.8959 -0.4171 0 220 -2.0500 -0.7193 0.3134 -2.6699 1 221 1.4275 -0.1427 0.1080 -2.5215 0 [222 rows x 5 columns]
small.shape
(222, 5)
model.fit(X, y, epochs=150, batch_size=10)
Epoch 1/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4395 - accuracy: 0.8063 Epoch 2/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4395 - accuracy: 0.8063 Epoch 3/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4398 - accuracy: 0.8063 Epoch 4/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4390 - accuracy: 0.8108 Epoch 5/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4387 - accuracy: 0.8153 Epoch 6/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4378 - accuracy: 0.8108 Epoch 7/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4374 - accuracy: 0.8108 Epoch 8/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4369 - accuracy: 0.8108 Epoch 9/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4364 - accuracy: 0.8108 Epoch 10/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4366 - accuracy: 0.8063 Epoch 11/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4371 - accuracy: 0.8063 Epoch 12/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4360 - accuracy: 0.8108 Epoch 13/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4350 - accuracy: 0.8108 Epoch 14/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4360 - accuracy: 0.8063 Epoch 15/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4359 - accuracy: 0.8108 Epoch 16/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4341 - accuracy: 0.8063 Epoch 17/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4363 - accuracy: 0.8108 Epoch 18/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4338 - accuracy: 0.8063 Epoch 19/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4338 - accuracy: 0.8108 Epoch 20/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4332 - accuracy: 0.8108 Epoch 21/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4334 - accuracy: 0.8108 Epoch 22/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4327 - accuracy: 0.8153 Epoch 23/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4334 - accuracy: 0.8108 Epoch 24/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4324 - accuracy: 0.8108 Epoch 25/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4329 - accuracy: 0.8198 Epoch 26/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4312 - accuracy: 0.8108 Epoch 27/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4305 - accuracy: 0.8108 Epoch 28/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4318 - accuracy: 0.8108 Epoch 29/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4305 - accuracy: 0.8108 Epoch 30/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4312 - accuracy: 0.8153 Epoch 31/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4298 - accuracy: 0.8153 Epoch 32/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4293 - accuracy: 0.8153 Epoch 33/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4296 - accuracy: 0.8153 Epoch 34/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4302 - accuracy: 0.8153 Epoch 35/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4289 - accuracy: 0.8153 Epoch 36/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4281 - accuracy: 0.8108 Epoch 37/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4290 - accuracy: 0.8153 Epoch 38/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4270 - accuracy: 0.8153 Epoch 39/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4275 - accuracy: 0.8153 Epoch 40/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4267 - accuracy: 0.8153 Epoch 41/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4261 - accuracy: 0.8153 Epoch 42/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4261 - accuracy: 0.8153 Epoch 43/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4253 - accuracy: 0.8153 Epoch 44/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4266 - accuracy: 0.8153 Epoch 45/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4264 - accuracy: 0.8153 Epoch 46/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4261 - accuracy: 0.8153 Epoch 47/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4239 - accuracy: 0.8153 Epoch 48/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4243 - accuracy: 0.8153 Epoch 49/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4235 - accuracy: 0.8153 Epoch 50/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4237 - accuracy: 0.8153 Epoch 51/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4233 - accuracy: 0.8153 Epoch 52/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4249 - accuracy: 0.8198 Epoch 53/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4232 - accuracy: 0.8198 Epoch 54/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4224 - accuracy: 0.8198 Epoch 55/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4247 - accuracy: 0.8243 Epoch 56/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4213 - accuracy: 0.8243 Epoch 57/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4228 - accuracy: 0.8243 Epoch 58/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4211 - accuracy: 0.8243 Epoch 59/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4205 - accuracy: 0.8243 Epoch 60/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4197 - accuracy: 0.8243 Epoch 61/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4206 - accuracy: 0.8243 Epoch 62/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4193 - accuracy: 0.8243 Epoch 63/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4194 - accuracy: 0.8198 Epoch 64/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4191 - accuracy: 0.8243 Epoch 65/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4183 - accuracy: 0.8243 Epoch 66/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4180 - accuracy: 0.8243 Epoch 67/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4173 - accuracy: 0.8243 Epoch 68/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4179 - accuracy: 0.8198 Epoch 69/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4172 - accuracy: 0.8288 Epoch 70/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4175 - accuracy: 0.8243 Epoch 71/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4151 - accuracy: 0.8243 Epoch 72/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4166 - accuracy: 0.8288 Epoch 73/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4155 - accuracy: 0.8288 Epoch 74/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4153 - accuracy: 0.8243 Epoch 75/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4144 - accuracy: 0.8288 Epoch 76/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4140 - accuracy: 0.8288 Epoch 77/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4146 - accuracy: 0.8243 Epoch 78/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4132 - accuracy: 0.8288 Epoch 79/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4131 - accuracy: 0.8243 Epoch 80/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4123 - accuracy: 0.8243 Epoch 81/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4129 - accuracy: 0.8243 Epoch 82/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4132 - accuracy: 0.8243 Epoch 83/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4118 - accuracy: 0.8243 Epoch 84/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4118 - accuracy: 0.8288 Epoch 85/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4111 - accuracy: 0.8288 Epoch 86/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4108 - accuracy: 0.8243 Epoch 87/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4113 - accuracy: 0.8243 Epoch 88/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4109 - accuracy: 0.8243 Epoch 89/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4096 - accuracy: 0.8243 Epoch 90/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4093 - accuracy: 0.8288 Epoch 91/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4088 - accuracy: 0.8288 Epoch 92/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4100 - accuracy: 0.8243 Epoch 93/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4081 - accuracy: 0.8243 Epoch 94/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4089 - accuracy: 0.8288 Epoch 95/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4079 - accuracy: 0.8243 Epoch 96/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4077 - accuracy: 0.8288 Epoch 97/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4071 - accuracy: 0.8243 Epoch 98/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4079 - accuracy: 0.8288 Epoch 99/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4064 - accuracy: 0.8243 Epoch 100/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4063 - accuracy: 0.8288 Epoch 101/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4055 - accuracy: 0.8288 Epoch 102/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4056 - accuracy: 0.8288 Epoch 103/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4051 - accuracy: 0.8288 Epoch 104/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4060 - accuracy: 0.8243 Epoch 105/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4063 - accuracy: 0.8288 Epoch 106/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4061 - accuracy: 0.8288 Epoch 107/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4055 - accuracy: 0.8288 Epoch 108/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4050 - accuracy: 0.8243 Epoch 109/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4044 - accuracy: 0.8288 Epoch 110/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4035 - accuracy: 0.8333 Epoch 111/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4030 - accuracy: 0.8333 Epoch 112/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4034 - accuracy: 0.8333 Epoch 113/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4037 - accuracy: 0.8333 Epoch 114/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4039 - accuracy: 0.8288 Epoch 115/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4027 - accuracy: 0.8288 Epoch 116/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4025 - accuracy: 0.8333 Epoch 117/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4028 - accuracy: 0.8288 Epoch 118/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4019 - accuracy: 0.8288 Epoch 119/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4019 - accuracy: 0.8333 Epoch 120/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4011 - accuracy: 0.8333 Epoch 121/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4020 - accuracy: 0.8378 Epoch 122/150 23/23 [==============================] - 0s 1ms/step - loss: 0.4002 - accuracy: 0.8378 Epoch 123/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3996 - accuracy: 0.8378 Epoch 124/150 23/23 [==============================] - 0s 2ms/step - loss: 0.4000 - accuracy: 0.8378 Epoch 125/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3989 - accuracy: 0.8378 Epoch 126/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3996 - accuracy: 0.8333 Epoch 127/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3995 - accuracy: 0.8333 Epoch 128/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3984 - accuracy: 0.8378 Epoch 129/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3983 - accuracy: 0.8378 Epoch 130/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3979 - accuracy: 0.8378 Epoch 131/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3990 - accuracy: 0.8378 Epoch 132/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3982 - accuracy: 0.8378 Epoch 133/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3972 - accuracy: 0.8378 Epoch 134/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3972 - accuracy: 0.8378 Epoch 135/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3961 - accuracy: 0.8378 Epoch 136/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3969 - accuracy: 0.8378 Epoch 137/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3960 - accuracy: 0.8378 Epoch 138/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3967 - accuracy: 0.8378 Epoch 139/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3962 - accuracy: 0.8288 Epoch 140/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3975 - accuracy: 0.8378 Epoch 141/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3961 - accuracy: 0.8333 Epoch 142/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3971 - accuracy: 0.8378 Epoch 143/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3950 - accuracy: 0.8378 Epoch 144/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3952 - accuracy: 0.8333 Epoch 145/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3938 - accuracy: 0.8378 Epoch 146/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3943 - accuracy: 0.8378 Epoch 147/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3941 - accuracy: 0.8423 Epoch 148/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3934 - accuracy: 0.8333 Epoch 149/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3926 - accuracy: 0.8378 Epoch 150/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3927 - accuracy: 0.8333
<keras.callbacks.History at 0x22f93c9e988>
history=model.fit(X, y, epochs=150, batch_size=10)
Epoch 1/150 23/23 [==============================] - 0s 2ms/step - loss: 0.3456 - accuracy: 0.8288 Epoch 2/150 23/23 [==============================] - 0s 2ms/step - loss: 0.3453 - accuracy: 0.8378 Epoch 3/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3442 - accuracy: 0.8468 Epoch 4/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3441 - accuracy: 0.8514 Epoch 5/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3434 - accuracy: 0.8378 Epoch 6/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3433 - accuracy: 0.8423 Epoch 7/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3437 - accuracy: 0.8514 Epoch 8/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3440 - accuracy: 0.8423 Epoch 9/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3423 - accuracy: 0.8378 Epoch 10/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3409 - accuracy: 0.8378 Epoch 11/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3415 - accuracy: 0.8423 Epoch 12/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3395 - accuracy: 0.8423 Epoch 13/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3414 - accuracy: 0.8468 Epoch 14/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3395 - accuracy: 0.8423 Epoch 15/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3383 - accuracy: 0.8423 Epoch 16/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3388 - accuracy: 0.8423 Epoch 17/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3380 - accuracy: 0.8423 Epoch 18/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3375 - accuracy: 0.8423 Epoch 19/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3374 - accuracy: 0.8468 Epoch 20/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3391 - accuracy: 0.8423 Epoch 21/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3381 - accuracy: 0.8468 Epoch 22/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3357 - accuracy: 0.8468 Epoch 23/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3350 - accuracy: 0.8468 Epoch 24/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3366 - accuracy: 0.8423 Epoch 25/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3351 - accuracy: 0.8423 Epoch 26/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3342 - accuracy: 0.8468 Epoch 27/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3334 - accuracy: 0.8468 Epoch 28/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3339 - accuracy: 0.8468 Epoch 29/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3344 - accuracy: 0.8423 Epoch 30/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3355 - accuracy: 0.8423 Epoch 31/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8468 Epoch 32/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3340 - accuracy: 0.8468 Epoch 33/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3351 - accuracy: 0.8423 Epoch 34/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3324 - accuracy: 0.8423 Epoch 35/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3324 - accuracy: 0.8514 Epoch 36/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3318 - accuracy: 0.8514 Epoch 37/150 23/23 [==============================] - 0s 2ms/step - loss: 0.3309 - accuracy: 0.8514 Epoch 38/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3307 - accuracy: 0.8423 Epoch 39/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3307 - accuracy: 0.8378 Epoch 40/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3299 - accuracy: 0.8378 Epoch 41/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3291 - accuracy: 0.8378 Epoch 42/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3297 - accuracy: 0.8378 Epoch 43/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3291 - accuracy: 0.8378 Epoch 44/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3288 - accuracy: 0.8423 Epoch 45/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3307 - accuracy: 0.8423 Epoch 46/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3291 - accuracy: 0.8423 Epoch 47/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8468 Epoch 48/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3277 - accuracy: 0.8468 Epoch 49/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3306 - accuracy: 0.8468 Epoch 50/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3271 - accuracy: 0.8468 Epoch 51/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3265 - accuracy: 0.8468 Epoch 52/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3286 - accuracy: 0.8423 Epoch 53/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3264 - accuracy: 0.8514 Epoch 54/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3265 - accuracy: 0.8468 Epoch 55/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3257 - accuracy: 0.8468 Epoch 56/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3251 - accuracy: 0.8468 Epoch 57/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3245 - accuracy: 0.8468 Epoch 58/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3228 - accuracy: 0.8468 Epoch 59/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3235 - accuracy: 0.8468 Epoch 60/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3247 - accuracy: 0.8468 Epoch 61/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3247 - accuracy: 0.8423 Epoch 62/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3242 - accuracy: 0.8468 Epoch 63/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3222 - accuracy: 0.8468 Epoch 64/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3223 - accuracy: 0.8514 Epoch 65/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3221 - accuracy: 0.8468 Epoch 66/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3216 - accuracy: 0.8423 Epoch 67/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3212 - accuracy: 0.8468 Epoch 68/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3221 - accuracy: 0.8514 Epoch 69/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3227 - accuracy: 0.8468 Epoch 70/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3213 - accuracy: 0.8468 Epoch 71/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3223 - accuracy: 0.8423 Epoch 72/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3210 - accuracy: 0.8468 Epoch 73/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3239 - accuracy: 0.8468 Epoch 74/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3195 - accuracy: 0.8468 Epoch 75/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3204 - accuracy: 0.8423 Epoch 76/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3179 - accuracy: 0.8514 Epoch 77/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3175 - accuracy: 0.8514 Epoch 78/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3171 - accuracy: 0.8514 Epoch 79/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3177 - accuracy: 0.8514 Epoch 80/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3179 - accuracy: 0.8468 Epoch 81/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3165 - accuracy: 0.8468 Epoch 82/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3181 - accuracy: 0.8514 Epoch 83/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3185 - accuracy: 0.8468 Epoch 84/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3159 - accuracy: 0.8514 Epoch 85/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3160 - accuracy: 0.8514 Epoch 86/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3171 - accuracy: 0.8514 Epoch 87/150 23/23 [==============================] - 0s 2ms/step - loss: 0.3139 - accuracy: 0.8514 Epoch 88/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3154 - accuracy: 0.8468 Epoch 89/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3135 - accuracy: 0.8468 Epoch 90/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3138 - accuracy: 0.8468 Epoch 91/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3127 - accuracy: 0.8468 Epoch 92/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3130 - accuracy: 0.8468 Epoch 93/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3134 - accuracy: 0.8559 Epoch 94/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3134 - accuracy: 0.8468 Epoch 95/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3147 - accuracy: 0.8468 Epoch 96/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3143 - accuracy: 0.8468 Epoch 97/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3142 - accuracy: 0.8468 Epoch 98/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3110 - accuracy: 0.8514 Epoch 99/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3111 - accuracy: 0.8514 Epoch 100/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3116 - accuracy: 0.8468 Epoch 101/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3120 - accuracy: 0.8468 Epoch 102/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3105 - accuracy: 0.8514 Epoch 103/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3098 - accuracy: 0.8514 Epoch 104/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3109 - accuracy: 0.8514 Epoch 105/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3094 - accuracy: 0.8468 Epoch 106/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3091 - accuracy: 0.8468 Epoch 107/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3086 - accuracy: 0.8468 Epoch 108/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3073 - accuracy: 0.8468 Epoch 109/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3081 - accuracy: 0.8423 Epoch 110/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3092 - accuracy: 0.8423 Epoch 111/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3069 - accuracy: 0.8514 Epoch 112/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3072 - accuracy: 0.8423 Epoch 113/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3079 - accuracy: 0.8468 Epoch 114/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3063 - accuracy: 0.8468 Epoch 115/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3074 - accuracy: 0.8514 Epoch 116/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3058 - accuracy: 0.8468 Epoch 117/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3077 - accuracy: 0.8468 Epoch 118/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3056 - accuracy: 0.8514 Epoch 119/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3050 - accuracy: 0.8468 Epoch 120/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3079 - accuracy: 0.8514 Epoch 121/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3031 - accuracy: 0.8423 Epoch 122/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3036 - accuracy: 0.8423 Epoch 123/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3030 - accuracy: 0.8468 Epoch 124/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3033 - accuracy: 0.8468 Epoch 125/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3021 - accuracy: 0.8468 Epoch 126/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3029 - accuracy: 0.8423 Epoch 127/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3027 - accuracy: 0.8468 Epoch 128/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3018 - accuracy: 0.8468 Epoch 129/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3032 - accuracy: 0.8468 Epoch 130/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3025 - accuracy: 0.8468 Epoch 131/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3008 - accuracy: 0.8559 Epoch 132/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3008 - accuracy: 0.8468 Epoch 133/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3028 - accuracy: 0.8468 Epoch 134/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3014 - accuracy: 0.8423 Epoch 135/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3003 - accuracy: 0.8468 Epoch 136/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3005 - accuracy: 0.8468 Epoch 137/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3004 - accuracy: 0.8559 Epoch 138/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2984 - accuracy: 0.8514 Epoch 139/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2987 - accuracy: 0.8514 Epoch 140/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3002 - accuracy: 0.8468 Epoch 141/150 23/23 [==============================] - 0s 1ms/step - loss: 0.3045 - accuracy: 0.8514 Epoch 142/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2983 - accuracy: 0.8514 Epoch 143/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2975 - accuracy: 0.8468 Epoch 144/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2965 - accuracy: 0.8514 Epoch 145/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2963 - accuracy: 0.8514 Epoch 146/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2963 - accuracy: 0.8514 Epoch 147/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2983 - accuracy: 0.8468 Epoch 148/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2964 - accuracy: 0.8468 Epoch 149/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2953 - accuracy: 0.8514 Epoch 150/150 23/23 [==============================] - 0s 1ms/step - loss: 0.2957 - accuracy: 0.8514
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 MSI 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()
_, accuracy = model.evaluate(X, y)
print('Accuracy: %.2f' % (accuracy*100))
7/7 [==============================] - 0s 2ms/step - loss: 0.2936 - accuracy: 0.8559 Accuracy: 85.59
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_probs
array([[1.78641677e-02],
[6.54379619e-05],
[3.25500965e-04],
[6.19835854e-01],
[2.42896974e-02],
[1.05383992e-01],
[3.49226415e-01],
[1.35085493e-01],
[1.87719852e-01],
[8.66127014e-03],
[1.93861425e-02],
[8.72610509e-02],
[3.71589929e-01],
[1.25923753e-03],
[2.86265194e-01],
[1.36966020e-01],
[1.78950220e-01],
[7.79716074e-02],
[2.11236638e-05],
[3.52800786e-01],
[4.34492230e-02],
[1.12131000e-01],
[5.43035746e-01],
[9.17702496e-01],
[9.62218344e-02],
[5.84137738e-02],
[3.88309807e-01],
[8.53401423e-03],
[3.61427486e-01],
[1.60611212e-01],
[6.61975861e-01],
[3.19603086e-02],
[4.36515272e-01],
[3.64569128e-02],
[3.41386259e-01],
[8.42251778e-01],
[1.20969534e-01],
[6.65158033e-04],
[2.84329176e-01],
[1.04564965e-01],
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[7.88454711e-01],
[1.20667517e-02],
[3.74524891e-02],
[3.47409546e-02],
[7.25506246e-02],
[1.48383588e-01],
[4.09154892e-02],
[2.43292570e-01],
[8.08010101e-02],
[1.40094757e-02],
[3.17329705e-01],
[2.52758145e-01],
[3.02911699e-02],
[3.15487385e-04],
[2.77154535e-01],
[1.52257711e-01],
[9.53324139e-02],
[6.57869279e-02],
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[2.58653700e-01],
[2.60835588e-02],
[3.58297944e-01],
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[3.44277620e-02],
[9.14836228e-02],
[3.06923926e-01],
[9.51260328e-04],
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[4.89165187e-02],
[1.71483964e-01],
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[6.78420067e-04],
[2.83637941e-02],
[2.49545693e-01],
[7.56621361e-04]], dtype=float32)
yhat_classes = (model.predict(X) > 0.5).astype("int32")
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: 0.836735
accuracy = accuracy_score(y, yhat_classes)
print('Accuracy: %f' % accuracy)
Accuracy: 0.855856
recall = recall_score(y, yhat_classes)
print('Recall: %f' % recall)
Recall: 0.630769
f1 = f1_score(y, yhat_classes)
print('F1 score: %f' % f1)
F1 score: 0.719298
kappa = cohen_kappa_score(y, yhat_classes)
print('Cohens kappa: %f' % kappa)
Cohens kappa: 0.624881
auc = roc_auc_score(y, yhat_probs)
print('ROC AUC: %f' % auc)
ROC AUC: 0.942969
matrix = confusion_matrix(y, yhat_classes)
print(matrix)
[[149 8] [ 24 41]]