tensorflow 下 用lstm预测sin

# -*- coding: utf-8 -*-"""本程序装tensorflow 版本1.1.0, python版本 3.6.0下测试成功."""from __future__ import print_functionimport numpy as npimport tensorflow as tffrom six.moves import rangeimport collectionsimport randomimport csvfrom matplotlib import pyplot as pltclass BatchGenerator:    def __init__(self, window_size, window_range):        self.cursor = 0.        self.window = window_size        self.range = window_range    def next(self):        x = np.zeros([self.window, 1])        y = np.zeros([self.window, 1])        d = np.arange(self.cursor, self.cursor + 1., 1. / self.window)        for i in range(self.window):            l = np.sin(d[i])            x[i, 0] = d[i]            y[i, 0] = l        self.cursor += 1. / self.window        return y[:self.window - 1], y[1:], x[:self.window - 1]num_nodes = 64window_size = 5window_range = 10num_unrollings = 100graph = tf.Graph()with graph.as_default():    # 参数初始化    # input gate    ix = tf.Variable(tf.truncated_normal([window_size - 1, num_nodes], -0.1, 0.1), name="ix")    ih = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1), name="ih")    ib = tf.Variable(tf.zeros([1, num_nodes]), name="ib")    # output_gate    ox = tf.Variable(tf.truncated_normal([window_size - 1, num_nodes], -0.1, 0.1), name="ox")    oh = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1), name="oh")    ob = tf.Variable(tf.zeros([1, num_nodes]), name="ob")    # forget_gate    fx = tf.Variable(tf.truncated_normal([window_size - 1, num_nodes], -0.1, 0.1), name="fx")    fh = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1), name="fh")    fb = tf.Variable(tf.zeros([1, num_nodes]), name="fb")    # cell    gx = tf.Variable(tf.truncated_normal([window_size - 1, num_nodes], -0.1, 0.1), name="gx")    gh = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1), name="gh")    gb = tf.Variable(tf.zeros([1, num_nodes]), name="gb")    # variables saving state across unrollings    saved_output = tf.Variable(tf.zeros([1, num_nodes]), trainable=False, name="saved_output")    saved_state = tf.Variable(tf.zeros([1, num_nodes]), trainable=False, name="saved_state")    # classifier's weights and biases    w = tf.Variable(tf.truncated_normal([num_nodes, window_size - 1], -0.1, 0.1), name="w")    b = tf.Variable(tf.zeros([window_size - 1]), name="b")    ##定义LSTM cell    def lstm_cell(x, h, c):        input_gate = tf.sigmoid(tf.matmul(x, ix) + tf.matmul(h, ih) + ib)        output_gate = tf.sigmoid(tf.matmul(x, ox) + tf.matmul(h, oh) + ob)        forget_gate = tf.sigmoid(tf.matmul(x, fx) + tf.matmul(h, fh) + fb)        gt = tf.tanh(tf.matmul(x, gx) + tf.matmul(h, gh) + gb)        ct = input_gate * gt + forget_gate * c        return output_gate * tf.tanh(ct), ct    # Input Data    train_data = list()    train_label = list()    for _ in range(num_unrollings + 1):        train_data.append( \            tf.placeholder(tf.float32, shape=[1, window_size - 1]))        train_label.append( \            tf.placeholder(tf.float32, shape=[1, window_size - 1]))    train_inputs = train_data    train_labels = train_label    # 将LSTM展开    outputs = list()    output = saved_output    state = saved_state    for i in train_inputs:        output, state = lstm_cell(i, output, state)        outputs.append(output)    # 将最后一层LSTM的输出和隐藏层保存    with tf.control_dependencies([saved_output.assign(output),saved_state.assign(state)]):        # 一层LSTM的RNN搭建        logits = tf.nn.xw_plus_b(tf.concat( outputs,0), w, b)        loss = tf.reduce_mean( \            tf.reduce_sum(tf.square(tf.concat( train_labels,0) - logits))        )    # optimizer    global_step = tf.Variable(0)    learning_rate = tf.train.exponential_decay(0.8, global_step, 1000, 0.5)    optimizer = tf.train.GradientDescentOptimizer(learning_rate)    gradients, v = zip(*optimizer.compute_gradients(loss))    gradients, _ = tf.clip_by_global_norm(gradients, 1.25)    optimizer = optimizer.apply_gradients(zip(gradients, v), global_step=global_step)    # Predictions    train_prediction = logits    # test eval:batch 1,no unrolling    sample_input = tf.placeholder(tf.float32, shape=[1, window_size - 1])    saved_sample_output = tf.Variable(tf.zeros([1, num_nodes]))    saved_sample_state = tf.Variable(tf.zeros([1, num_nodes]))    reset_sample_state = tf.group(saved_sample_output.assign(tf.zeros([1, num_nodes])), \        saved_sample_state.assign(tf.zeros([1, num_nodes])))    sample_output, sample_state = lstm_cell(sample_input, saved_sample_output, saved_sample_state)    with tf.control_dependencies([saved_sample_output.assign(sample_output),saved_sample_state.assign(sample_state)]):        sample_prediction = tf.nn.xw_plus_b(sample_output, w, b)num_steps = 1001summary_frequency = 100f = open("out.csv", "a+")writer = csv.writer(f)with tf.Session(graph=graph) as session:    tf.initialize_all_variables().run()    print("Initialized!!")    mean_loss = 0    feed_dict = dict()    count = 0    test = BatchGenerator(window_size, window_range)    for i in range(num_unrollings + 1):        temp1, temp2, _ = test.next()        feed_dict[train_data[i]] = temp1.reshape([1, window_size - 1])        feed_dict[train_label[i]] = temp2.reshape([1, window_size - 1])    for step in range(num_steps):        _, l, predictions, lr = session.run([optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)        if step % 100 == 0:            print(l)    X = list()    Label = list()    Prediction = list()    for i in range(100):        data, label, x = test.next()        feed = data.reshape([1, window_size - 1])        prediction = sample_prediction.eval({sample_input: feed})        X.append(x)        Label.append(label)        Prediction.append(prediction)    Prediction = np.array(Prediction).reshape([1, 100 * (window_size - 1)])    Label = np.array(Label).reshape([1, 100 * (window_size - 1)])    X = np.array(X).reshape([1, 100 * (window_size - 1)])    print(Label, Prediction)    plt.plot(X[0], Label[0], color="blue")    plt.plot(X[0], Prediction[0], color="red")    plt.show()    plt.savefig("out.png")    writer.writerows([data.T[0], label.T[0], prediction[0]])f.close()