给我lstm多元时序预测算法代码示例

LSTM多元时序预测算法的代码示例如下：import numpy as npimport tensorflow as tf# Parametersn_inputs = 3 # Number of input signalsn_neurons = 5 # Number of neurons# Placeholder for the input dataX = tf.placeholder(tf.float32, [None, n_inputs])# Initialize the weights of the networkW = tf.Variable(tf.random_normal([n_inputs, n_neurons]))# Initialize the bias of the networkb = tf.Variable(tf.zeros([1, n_neurons]))# Create the RNN cellY0 = tf.tanh(tf.matmul(X, W) + b)# Prepare the list of RNN cellsrnn_cells = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)for layer in range(2)]# Create the RNNmulti_layer_cell = tf.contrib.rnn.MultiRNNCell(rnn_cells)# Apply the RNN to the dataoutputs, states = tf.nn.dynamic_rnn(multi_layer_cell, Y0, dtype=tf.float32)# Initialization of the variablesinit = tf.global_variables_initializer()# Run the graph with the necessary dataX_batch = np.array([[[0,1,2], [3,4,5], [6,7,8],[9,0,1]]])with tf.Session() as sess:init.run()outputs_val = outputs.eval(feed_dict={X: X_batch})