创建LSTM模型时出现“InvalidArgumentErrorandInvalidArgumentError:Graphexecutionerrortensorflow的错误。

这个错误通常是由于在训练LSTM模型时输入数据的维度不正确导致的。当LSTM接收到预期外的输入维度时，它会抛出此错误。

以下是一个示例代码，展示如何设置正确的输入张量维度：

from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras import backend as K
import numpy as np

batch_size = 32
timesteps = 100
input_dim = 1
units = 32
output_dim = 1

# Create sample data
x_train = np.random.random((batch_size, timesteps, input_dim))
y_train = np.random.random((batch_size, output_dim))

# Define the model
model = Sequential()
model.add(LSTM(units=units, batch_input_shape=(batch_size, timesteps, input_dim), return_sequences=True))
model.add(Dense(units=output_dim))

# Compile the model
model.compile(loss='mean_squared_error', optimizer='adam')

# Train the model
model.fit(x_train, y_train, epochs=1)

# Predict
x_test = np.random.random((1, timesteps, input_dim))
y_pred = model.predict(x_test)
print(y_pred)

在上面的示例中，我们在LSTM层中指定了输入数据的正确维度(batch_size, timesteps, input_dim)，以及在Dense层中指定了输出维度output_dim。这可以避免出现“InvalidArgumentError”错误。