ValueError：lstm 层的输入 0 与该层不兼容：预期 ndim=3，发现 ndim=2。收到完整形状：[无，18]

我是 Keras 新手，我正在尝试构建一个供个人使用/未来学习的模型。我刚刚开始使用 python，并想出了这段代码（在视频和教程的帮助下）。我有 16324 个实例的数据，每个实例由 18 个特征和 1 个因变量组成。

import pandas as pd
import os
import time
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint

EPOCHS = 10
BATCH_SIZE = 64
NAME = f"-TEST-{int(time.time())}"

df = pd.read_csv("EntryData.csv", names=['1SH5', '1SHA', '1SA5', '1SAA', '1WH5', '1WHA', '2SA5', '2SAA', '2SH5', '2SHA', '2WA5', '2WAA', '3R1', '3R2', '3R3', '3R4', '3R5', '3R6', 'Target'])

df_val = 14554 

validation_df = df[df.index > df_val]
df = df[df.index <= df_val]

train_x = df.drop(columns=['Target'])
train_y = df[['Target']]
validation_x = validation_df.drop(columns=['Target'])
validation_y = validation_df[['Target']]

model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(LSTM(128, return_sequences=True))
model.add(Dropout(0.1))
model.add(BatchNormalization())

model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(2, activation='softmax'))

opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)

model.compile(loss='sparse_categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])

tensorboard = TensorBoard(log_dir=f'logs/{NAME}')

filepath = "RNN_Final-{epoch:02d}-{val_acc:.3f}"  
checkpoint = ModelCheckpoint("models/{}.model".format(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')) # saves only the best ones

history = model.fit(
    train_x, train_y,
    batch_size=BATCH_SIZE,
    epochs=EPOCHS,
    validation_data=(validation_x, validation_y),
    callbacks=[tensorboard, checkpoint],)

score = model.evaluate(validation_x, validation_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

model.save("models/{}".format(NAME))

In line

model.add(LSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True))

抛出一个错误：

ValueError：lstm 层的输入 0 与该层不兼容： 预期 ndim=3，发现 ndim=2。收到完整形状：[无，18]

我在这个网站和谷歌上搜索解决方案几个小时了，但我无法找到正确的答案，或者我无法实现类似问题的解决方案。

感谢您的任何提示。

LSTM 网络期望三维输入这种格式的：

(n_samples, time_steps, features)

这可能有两种主要方式造成问题。

1. 您的输入是二维的
2. 您堆叠了（多个）LSTM 层

1.您的输入是二维的

您需要将输入转换为 3D。

x = x.reshape(len(x), 1, x.shape[1])
# or
x = np.expand_dims(x, 1)

然后，在第一层中指定正确的输入形状：

LSTM(64, input_shape=(x.shape[1:]))

2. 你有堆叠的 LSTM 层

默认情况下，LSTM 层将not返回序列，即它们将返回 2D 输出。这意味着第二个 LSTM 层将没有所需的 3D 输入。为了解决这个问题，您需要设置return_sequences=True:

tf.keras.layers.LSTM(8, return_sequences=True),
tf.keras.layers.LSTM(8)

以下是重现和解决 2D 输入问题的方法：

import tensorflow as tf
import numpy as np

x = np.random.rand(100, 10)
# x = np.expand_dims(x, 1) # uncomment to solve the problem
y = np.random.randint(0, 2, 100)

model = tf.keras.Sequential([
    tf.keras.layers.LSTM(8),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

history = model.fit(x, y, validation_split=0.1)

以下是重现和解决堆叠 LSTM 层问题的方法：

import tensorflow as tf
import numpy as np

x = np.random.rand(100, 1, 10)
y = np.random.randint(0, 2, 100)

model = tf.keras.Sequential([
    tf.keras.layers.LSTM(8), # use return_sequences=True to solve the problem
    tf.keras.layers.LSTM(8),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

history = model.fit(x, y, validation_split=0.1)