使用tflearn python将数据馈送到lstm

import tflearn
from tflearn.data_utils import to_categorical, pad_sequences

# 假设输入数据为X，标签为Y
# 将标签转换为one-hot编码
Y = to_categorical(Y, num_classes=num_classes)
# 对输入数据进行填充，使其长度一致
X = pad_sequences(X, maxlen=max_sequence_length, value=0.)

# 创建输入层
net = tflearn.input_data(shape=[None, max_sequence_length])
# 创建LSTM层
net = tflearn.lstm(net, n_units=128, dropout=0.8)
# 创建输出层
net = tflearn.fully_connected(net, num_classes, activation='softmax')

# 定义优化器
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy')

# 创建模型
model = tflearn.DNN(net, tensorboard_verbose=0)
# 训练模型
model.fit(X, Y, validation_set=(X_val, Y_val), show_metric=True,
          batch_size=batch_size, n_epoch=num_epochs)

# 进行预测
predictions = model.predict(X_test)