1.模型

多层LSTM

2.用到的函数

tf.nn.rnn_cell.BasicLSTMCell(num_units)

num_units这个参数的大小就是LSTM输出结果的维度。例如num_units=128， 那么LSTM网络最后输出就是一个128维的向量。http://www.mtcnn.com/?p=529

tf.nn.dynamic_rnn

https://blog.csdn.net/junjun150013652/article/details/81331448

tf.contrib.layers.fully_connected(inputs,num_outputs)

增加一个全连接层
自动初始化w和b
激活函数默认为relu函数
输出个数由num_outputs指定

tf.losses.mean_squared_error

https://www.w3cschool.cn/tensorflow_python/tensorflow_python-zkxr2x87.html

3.代码

import numpy as np
import tensorflow as tf
import matplotlib as mpl
mpl.use('Agg')
from matplotlib import pyplot as plt

import warnings
warnings.filterwarnings("ignore")

hidden_size = 30
num_layers = 2

timesteps = 10
training_steps = 1000
batch_size = 32

training_examples = 10000
testing_examples  =1000
sample_gap = 0.01

def generate_data(seq) :
    x = []
    y = []
    for i in range(len(seq) - timesteps) :
        x.append([seq[i: i + timesteps]])
        y.append([seq[i + timesteps]])
        
    return np.array(x, dtype=np.float32), np.array(y, dtype=np.float32)

def lstm_model(x, y, is_training) :
    cell = tf.nn.rnn_cell.MultiRNNCell(
            [tf.nn.rnn_cell.BasicLSTMCell(hidden_size)
            for _ in range(num_layers)])
    
    outputs, _ = tf.nn.dynamic_rnn(cell, x, dtype=tf.float32)
    
    # 下标为-1表示取出列表的最后一行数据值
    output = outputs[:, -1, :]
    
    predictions = tf.contrib.layers.fully_connected(
                    output, 1, activation_fn=None)
    
    if not is_training :
        return predictions, None, None
    
    loss = tf.losses.mean_squared_error(labels=y, predictions=predictions)
    
    trian_op = tf.contrib.layers.optimize_loss(
                loss, tf.train.get_global_step(),
                optimizer='Adagrad', learning_rate=0.1)
    
    return predictions, loss, trian_op

def train(sess, train_x, train_y) :
    ds = tf.data.Dataset.from_tensor_slices((train_x, train_y))
    ds = ds.repeat().shuffle(1000).batch(batch_size)
    x, y = ds.make_one_shot_iterator().get_next()
    
    with tf.variable_scope("model", reuse=tf.AUTO_REUSE) :
        predictions, loss, train_op = lstm_model(x, y, True)
        
    sess.run(tf.global_variables_initializer())
    
    for i in range (training_steps) :
        _, l = sess.run([train_op, loss])
        if i % 100 == 0 :
            print("train step: " + str(i) + ", loss: " + str(l))
            
def run_eval(sess, test_x, test_y) :
    ds = tf.data.Dataset.from_tensor_slices((test_x, test_y))
    ds = ds.batch(1)
    x, y = ds.make_one_shot_iterator().get_next()
    
    with tf.variable_scope("model", reuse=True) :
        predection, _, _ = lstm_model(x, [0,0], False)
        
    predictions = []
    labels = []
    for i in range(testing_examples) :
        p, l = sess.run([predection, y])
        predictions.append(p)
        labels.append(l)
        
    predictions = np.array(predictions).squeeze()
    labels = np.array(labels).squeeze()
    rmse = np.sqrt(((predictions - labels) ** 2).mean(axis=0))
    print("Mean square error is: %f" % rmse)
    
    %matplotlib inline
    plt.figure()
    plt.plot(predictions, label='predictions')
    plt.plot(labels,  label='real_sin')
    plt.legend()
    plt.show()
    
test_start = (training_examples + timesteps) * sample_gap
test_end = test_start + (testing_examples + timesteps) * sample_gap
test_x, test_y = generate_data(np.sin(np.linspace(
                    0, test_start, training_examples+timesteps, dtype=np.float32)))

with tf.Session() as sess :
    train(sess, train_x, train_y)
    run_eval(sess, test_x, test_y)