x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.25)
mean = x_train.mean(axis=0)
std = x_train.std(axis=0)
train_data = (x_train - mean) / std
test_data = (x_test - mean) / std
model = Sequential([Dense(64, input_shape=(6,)), Activation('relu'),
Dense(32), Activation('relu'),
Dense(1)])
sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
k = model.fit
[loss, sgd] = model.evaluate(test_data, y_test, verbose=1)
最后一步不知道哪出了问题。。test_data, y_test都是dataframe啊
TypeError Traceback (most recent call last)
in
----> 1 [loss, mse] = model.evaluate(test_data, y_test, verbose=1)
TypeError: 'numpy.float64' object is not iterable