愉快的学习就从翻译开始吧_Multivariate Time Series Forecasting with LSTMs in Keras_5_Evaluate Model

After the model is fit, we can forecast for the entire test dataset.

模型被拟合后，我们就能在整个测试数据集上预测

We combine the forecast with the test dataset and invert the scaling. We also invert scaling on the test dataset with the expected pollution numbers.

我们合并在测试数据集上的预测和翻转缩放，我们也翻转测试数据集上期望污染数字的缩放

With forecasts and actual values in their original scale, we can then calculate an error score for the model. In this case, we calculate the Root Mean Squared Error (RMSE) that gives error in the same units as the variable itself.

结合预测和原始比例的实际值，我们可以模型的误差分数，这里我们计算给出与变量本身相同单位的均方根差。

# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:, 0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = inv_y[:, 0]
# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)

numpy.concatenate

numpy. concatenate ( (a1,  a2,  ...),  axis=0,  out=None )

Join a sequence of arrays along an existing axis.

Parameters:

a1, a2, … : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. out : ndarray, optional If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.

Returns:

res : ndarray The concatenated array.

See also

ma.concatenate

Concatenate function that preserves input masks.

array_split

Split an array into multiple sub-arrays of equal or near-equal size.

split

Split array into a list of multiple sub-arrays of equal size.

hsplit

Split array into multiple sub-arrays horizontally (column wise)

vsplit

Split array into multiple sub-arrays vertically (row wise)

dsplit

Split array into multiple sub-arrays along the 3rd axis (depth).

stack

Stack a sequence of arrays along a new axis.

hstack

Stack arrays in sequence horizontally (column wise)

vstack

Stack arrays in sequence vertically (row wise)

dstack

Stack arrays in sequence depth wise (along third dimension)

Notes

When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are not preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead.

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])

This function will not preserve masking of MaskedArray inputs.

>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data = [0 -- 2],
             mask = [False  True False],
       fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
             mask = False,
       fill_value = 999999)
>>> np.ma.concatenate([a, b])
masked_array(data = [0 -- 2 2 3 4],
             mask = [False  True False False False False],
       fill_value = 999999)