我是一个LSTM的新手，如果这个问题很基础请见谅。我一直在尝试制作一个简单的LSTM模型，该模型从csv文本文件中加载数据进行训练
trainX = pd.read_csv("Train\\X_Data.txt", header=None, delim_whitespace=True).to_numpy() trainY = pd.read_csv("Train\\Y_Data.txt", header=None, delim_whitespace=True).to_numpy() testX = pd.read_csv("Test\\X_Data.txt", header=None, delim_whitespace=True).to_numpy() testY = pd.read_csv("Test\\Y_Data.txt", header=None, delim_whitespace=True).to_numpy() n_timesteps = trainX.shape[0] n_features = trainX.shape[1] model = Sequential() model.add(LSTM(100, input_shape=trainX.shape, return_sequences=True)) model.add(Dropout(0.5)) model.add(Dense(100, activation='relu')) #may need 2 neurons as there are two classes model.add(Dense(1, activation='sigmoid')) model.summary() model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # fit network model.fit(trainX, trainY, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=1) # evaluate model evalLosses, evalAccuracy = model.evaluate(testX, testY, batch_size=BATCH_SIZE, verbose=1) print("Overall Accuracy: " + str(evalAccuracy)) print("Overall Loss: " + str(evalLosses))我的输入是：
trainY.shape = (35, 1)trainX.shape = (35, 150)trainX = [[0.48597709 0.52190752 0.62556772 ... 0.09958187 0.12535847 0.0833305 ] [0.40917949 0.40525872 0.24515716 ... 0.33276069 0.40186229 0.36288622] [0.16203835 0.14811591 0.1618184 ... 0.08745848 0.09398027 0.1056776 ] ... [0.21770377 0.24859037 0.20659391 ... 0.01323494 0.01249982 0.01307911] [0.27596078 0.26605097 0.36028712 ... 0.10316001 0.10662966 0.10724351] [0.34860233 0.3500129 0.35434798 ... 0.04347154 0.02899346 0.02327774]]trainY = [[0] [0] [0] [0] . . . [0] [0] [1] [1] [1]]当我尝试将数据拟合到我的模型时，我得到了以下错误
ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 150)我该如何加载数据？形状是2维（35,150），那么为什么Keras只看到（None, 150）？
谢谢
回答：
trainX.shape = (35, 150) 这意味着你有 35 个样本，每个样本有 150 个特征。但是根据Keras的要求，你需要在第一个位置传递 batch_size。因此，你需要将 2D 输入扩展为 3D：
trainX = tf.expand_dims(trainX, axis=-1) # new shape = (35, 150, 1)trainY = tf.expand_dims(trainY, axis=-1) # new shape = (35, 150, 1)然后你可以将数据传递给模型：
model = Sequential()model.add(LSTM(100,input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True)model.add(Dropout(0.5))model.add(Dense(100, activation='relu'))model.add(Dense(1, activation='sigmoid'))编辑：
由于你处理的是二分类任务，将损失函数从 categorical_crossentropy 更改为 binary_crossentropy。
