Model improvement

[xiemark]

I change the model to the code listed below, `from utils import util_processor as pro

def get_model(params): model = Sequential()

# Conv1
model.add(Conv2D(64, (5, 5), input_shape=(10, 10, 4), padding='same', strides=(1,1)))
model.add(Activation('sigmoid'))

# Conv2
model.add(Conv2D(64, (5, 5), padding='same', strides=(1,1)))
model.add(Activation('sigmoid'))

# FC
model.add(Flatten())
model.add(Dense(128, activation='relu'))

model.add(Dense(params['classes']))
model.add(Activation('softmax'))
model.summary()
return model

`

The result improves a lot!

Total results > Label 1 : 1171 / 1500 78.1%

Total results > Label 2 : 1024 / 1500 68.3%

Total results > Label 3 : 1221 / 1500 81.4%

Total results > Label 4 : 1454 / 1500 96.9%

Total results > Label 5 : 783 / 1500 52.2%

Total results > Label 6 : 1055 / 1500 70.3%

Total results > Label 7 : 1312 / 1500 87.5%

Total results > Label 8 : 1280 / 1500 85.3%