如何用张量流绘制部分依赖图

Question

我有 gru 模型，有 5 个输入变量和 4 个输出变量。

lstm_model = Sequential()
lstm_model.add(GRU(25, input_shape=(X_train.shape[1],X_train.shape[2]), activation='tanh',recurrent_activation='sigmoid'  ,kernel_initializer='RandomUniform', kernel_regularizer=regularizers.l2(0.01),return_sequences=False))
lstm_model.add(Dense(13,activation='tanh',kernel_initializer='RandomUniform'))
lstm_model.add(Dense(4))
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
lstm_model.compile(loss='mean_squared_error', optimizer='Adam' ,metrics=[metrics.MeanAbsoluteError(name="mean_absolute_error", dtype=None)])

我想为每个输出变量绘制 PDP。我正在使用以下代码：

from sklearn.inspection import plot_partial_dependence
disp=plot_partial_dependence(lstm_model, X_train,target=1, verbose =1, features=[0,1,2,3,4],feature_names=f_columns)

此代码给出错误：

NotFittedError: This Sequential instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

Answer 1

您应该先拟合模型。在“plot_partial_dependence()”之前尝试“lstm_model.fit(X,y)”。