X_train shape: (44066, 8, 1)
 X_test shape: (5441, 8, 1)
 y_train shape: (44066, 8)
 y_test shape: (5441, 8)
 X_val shape: (4897, 8, 1)
 y_val shape: (4897, 8)

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(8,1)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(8, activation='softmax'))
print(model.summary())

model.compile(
        optimizer="adam",
        loss="categorical_crossentropy",
        metrics=['accuracy'])


model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test),
          callbacks=[TestCallback((X_val, y_val))])

Epoch 1/100
44066/44066 [==============================] - 8s 193us/step - loss: 2.0616 - acc: 0.1394 - val_loss: 2.0586 - val_acc: 0.1378

Testing loss: 2.0589641586770715, acc: 0.1378394935674903

Epoch 2/100
44066/44066 [==============================] - 5s 113us/step - loss: 2.0556 - acc: 0.1486 - val_loss: 2.0523 - val_acc: 0.1617

Testing loss: 2.0537997951842533, acc: 0.15090871962425975

Epoch 3/100
44066/44066 [==============================] - 6s 140us/step - loss: 2.0502 - acc: 0.1595 - val_loss: 2.0506 - val_acc: 0.1608

Testing loss: 2.051545942513729, acc: 0.15805595262405556

Epoch 4/100
44066/44066 [==============================] - 5s 122us/step - loss: 2.0484 - acc: 0.1617 - val_loss: 2.0483 - val_acc: 0.1638

Testing loss: 2.049225082562022, acc: 0.15683071268123341

Epoch 5/100
44066/44066 [==============================] - 5s 109us/step - loss: 2.0471 - acc: 0.1607 - val_loss: 2.0468 - val_acc: 0.1638

Testing loss: 2.048356912879523, acc: 0.16173167245252196

Epoch 6/100
44066/44066 [==============================] - 5s 109us/step - loss: 2.0463 - acc: 0.1623 - val_loss: 2.0452 - val_acc: 0.1619

Testing loss: 2.04520957813668, acc: 0.16071063916683684

Epoch 7/100
44066/44066 [==============================] - 5s 109us/step - loss: 2.0452 - acc: 0.1656 - val_loss: 2.0473 - val_acc: 0.1616

Testing loss: 2.0473607019037283, acc: 0.16009801919542577

Epoch 8/100
44066/44066 [==============================] - 5s 108us/step - loss: 2.0453 - acc: 0.1631 - val_loss: 2.0451 - val_acc: 0.1632

Testing loss: 2.0451171192218265, acc: 0.15989381253828874

Epoch 9/100
44066/44066 [==============================] - 5s 108us/step - loss: 2.0435 - acc: 0.1681 - val_loss: 2.0433 - val_acc: 0.1671

Testing loss: 2.0452189264137792, acc: 0.15907698590974065

Epoch 10/100
44066/44066 [==============================] - 5s 108us/step - loss: 2.0433 - acc: 0.1656 - val_loss: 2.0422 - val_acc: 0.1660

Testing loss: 2.042981141339474, acc: 0.1613232591382479