我准备了一个简单的数据集和标签集。我想学习如何在Keras中实现简单的RNN。我已经准备好了我的数据。当我不使用归一化（MinMaxScaler）时，一切都能正常编译，没有错误。
然而，当我尝试使用缩放器时，我得到了 ValueError: 发现维度为3的数组。估计器期望 <= 2.。这是代码：
# -*- coding: utf-8 -*- #!/usr/bin/env python3import tensorflow as tffrom tensorflow import kerasfrom keras.models import Sequentialfrom keras.layers import Dense, SimpleRNN from keras.callbacks import ModelCheckpointfrom keras import backend as Kfrom sklearn.preprocessing import MinMaxScalerfrom sklearn.model_selection import train_test_splitimport numpy import matplotlib.pyplot as pltdef stagger(a, delay): num_of_rows = a.shape[0] num_of_cols = a.shape[1] data_in = numpy.zeros((num_of_rows + delay, num_of_cols * (1 + delay))) data_in[0:num_of_rows, 0:4] = a data_in[1:(num_of_rows + 1), 4:8] = a a = data_in[0:num_of_rows, :] return adataset = numpy.array([[0, 2, 0, 324], [1, 2, 0,324], [2, 2, 0, 324], [3, 2, 0, 324], [4, 2, 0, 324], [5, 2, 0, 324], [6, 2, 0, 324], [7, 2, 0, 324], [8, 2, 0, 324], [9, 2, 0, 324], [ 10, 2, 0, 324], [ 11, 2, 0, 324], [ 12, 2, 0, 324], [ 13, 2, 0, 324], [ 14, 2, 0, 324], [ 15, 2, 0, 324], [ 16, 2, 0, 324], [ 17, 2, 0, 324],[ 18, 2, 0, 324], [ 19, 2, 0, 324], [ 20, 2, 0, 324], [ 21, 2, 0, 324],[ 22, 2, 0, 324], [ 23, 2, 0, 324]])labels = numpy.array([[0.82174763], [0.62098727], [0.45012733], [1.5912102 ], [0.37570953], [0.2930966 ], [0.34982923], [0.72239097], [1.37881947], [1.79550653], [1.88867237], [1.93567087], [1.9771925 ], [2.10873853], [2.158302 ], [2.11018633], [1.9714166 ], [2.2553416 ], [2.41161887], [2.41161887], [2.30333453], [2.38390613], [2.21882553], [2.0707972 ]])delay = 2input_shape = (1, 4*(1+delay))min_max_scaler = MinMaxScaler(feature_range=(0, 1))# 准备数据集dataset = stagger(dataset, delay)# 分割数据集x_train, x_test, y_train, y_test = train_test_split(dataset, labels, test_size=0.2, shuffle=False)x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.25, shuffle=False)# 归一化数据集x_train = min_max_scaler.fit_transform(x_train)x_test = min_max_scaler.transform(x_test)x_val = min_max_scaler.transform(x_val)# 重塑数据集x_train = numpy.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))x_test = numpy.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))x_val = numpy.reshape(x_val, (x_val.shape[0], 1, x_val.shape[1]))y_train = numpy.reshape(y_train, (y_train.shape[0], 1, y_train.shape[1]))y_test = numpy.reshape(y_test, (y_test.shape[0], 1, y_test.shape[1]))y_val = numpy.reshape(y_val, (y_val.shape[0], 1, y_val.shape[1]))# RNN模型model = Sequential()model.add(SimpleRNN(64, activation="relu", kernel_initializer='random_uniform', input_shape=input_shape, return_sequences=True))model.add(Dense(32, activation="relu", kernel_initializer= 'random_uniform')) model.add(Dense(1, activation="linear", kernel_initializer= 'random_uniform'))# 训练和预测callback = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=2, verbose=0, mode='auto')model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy', tf.keras.metrics.MeanSquaredError()])history = model.fit(x_train, y_train, epochs=100, batch_size=8, validation_data=(x_val, y_val), callbacks=[callback])results = model.evaluate(x_test, y_test)# 绘图test_predictions = model.predict(x_test)test_predictions = min_max_scaler.inverse_transform(test_predictions)y_test = y_test[:,:,0]test_predictions = test_predictions[:,:,0]plt.plot(y_test)plt.plot(test_predictions)plt.legend(['y_test', 'predictions'], loc='upper left')plt.show()回答：
这是因为你将3D序列传递给了MinMaxScaler。它接受2D序列。你需要做的是将你的预测转换为2D，然后再返回到3D。这可以在一行中完成…
test_predictions = min_max_scaler.inverse_transform(test_predictions.reshape(-1,1)).reshape(test_predictions.shape)