x_test = [[8],[6],[0],[2],[0],[0],[0],[0],[112.128],[0],[0],[2],[0],[1],[1],[2],[2]]
prediction = model.predict(model, x_test, batch_size = 32, verbose = 1)

TypeError                                 Traceback (most recent call last)
<ipython-input-14-286495dc15a7> in <module>()
  1 x_test = [[8],[6],[0],[2],[0],[0],[0],[0],[112.128],[0],[0],[2],[0],[1],[1],[2],[2]]
  2
----> 3 prediction = model.predict(model, x_test, batch_size =(17,1), verbose = 1)

TypeError: predict() got multiple values for argument 'batch_size'

model = Sequential()

model.add(Dense(32, input_dim=17, init='uniform', activation='relu' ))
model.add(Dense(64, init='uniform', activation='relu'))
model.add(Dense(128, init='uniform', activation='relu'))
model.add(Dense(64, init='uniform', activation='sigmoid'))
model.add(Dense(32, init='uniform', activation='sigmoid'))
model.add(Dense(16, init='uniform', activation='sigmoid'))
model.add(Dense(8, init='uniform', activation='sigmoid'))
model.add(Dense(4, init='uniform', activation='sigmoid'))
model.add(Dense(1, init='uniform', activation='sigmoid'))

# Compile model
model.compile(loss='mean_squared_logarithmic_error', optimizer='SGD', metrics=['accuracy'])

# Fit model
history = model.fit(X, Y, nb_epoch=300, validation_split=0.2, batch_size=3)