model.py

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, Activation

batch_size = 100
num_classes = 5
epochs = 50

# input image dimensions
img_rows, img_cols = 100, 100
channels = 3

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),padding='same',input_shape=(img_rows, img_cols, channels)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64,(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))


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

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

Thanks. But i also confused that the loss.
Does the loss function can be replaced by the binary_crossentropy?
If yes ,what does the difference between the multi-loss and binary_crossentropy?
Thanks.