我正在尝试修改下面的示例，以模拟我的数据集的CNN，并遇到了一些错误https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/
X = D.replace(['Resting', 'Swimming', 'Feeding', 'Non directed motion'], [0, 1, 2, 3])X_Label = X['Label'].to_numpy()X_Data = X[['X_static','Y_static','Z_static','X_dynamic','Y_dynamic','Z_dynamic']].to_numpy()X_names = ['X_static','Y_static','Z_static','X_dynamic','Y_dynamic','Z_dynamic']X_Label_Names = np.array(['Resting', 'Swimming', 'Feeding', 'Non directed motion'])X_Data 是一个 5600 行 6 列的 numpy 矩阵。每列代表一段时间内的某种测量数据
X_Label 是一个 5600 行 1 列的矩阵，包含从 0 到 3 的值，这些值代表特征或属性。0 代表休息，1 代表游泳，依此类推。
X = X_Datay = X_Labeldef load_dataset_f(X,y): X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, stratify=y, random_state=random_state ) trainX = X_train trainy = y_train testX = X_test testy = y_test print(trainX) print(trainX.shape) print(trainy.shape) return trainX, trainy, testX, testy# fit and evaluate a modeldef evaluate_model_f(trainX, trainy, testX, testy): verbose, epochs, batch_size = 2, 10, 20 n_timesteps, n_features, n_outputs = 6, 1, 1 print('n timesteps --------------------------------------------------------------------') print(n_timesteps) model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features))) model.add(Conv1D(filters=64, kernel_size=3, activation='relu')) model.add(Dropout(0.5)) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(100, activation='relu')) model.add(Dense(n_outputs, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # fit network print(to_categorical(trainy)) model.fit(trainX.reshape(len(trainX),6,1), to_categorical(trainy)) # evaluate model _, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0) return accuracydef run_experiment_f(repeats=1): # load data trainX, trainy, testX, testy = load_dataset_f(X,y) print(trainX) # repeat experiment scores = list() for r in range(repeats): score = evaluate_model_f(trainX, trainy, testX, testy) score = score * 100.0 print('>#%d: %.3f' % (r+1, score)) scores.append(score) # summarize results summarize_results(scores) load_dataset_f(X,y)run_experiment_f() 我对tensorflow库不熟悉，在model.fit()处遇到错误，我不确定如何处理这个问题。示例中展示的矩阵是3D的，而我的数据是2D的，不知道这是否重要。如何让这段代码工作？
回答：
你需要确保你的Conv1D层的输入形状为(timesteps, features)，并且你最后的输出层的单元数等于数据集中唯一标签的数量。这里是一个工作示例：
