''' Created on 2017年5月21日 @author: weizhen ''' # 以下程序为预测离散化之后的sin函数 import numpy as np import tensorflow as tf from tensorflow.contrib import rnn # 加载matplotlib工具包，使用该工具包可以对预测的sin函数曲线进行绘图 import matplotlib as mpl from tensorflow.contrib.learn.python.learn.estimators.estimator import SKCompat mpl.use('Agg') from matplotlib import pyplot as plt learn = tf.contrib.learn HIDDEN_SIZE = 30 # Lstm中隐藏节点的个数 NUM_LAYERS = 2 # LSTM的层数 TIMESTEPS = 10 # 循环神经网络的截断长度 TRAINING_STEPS = 10000 # 训练轮数 BATCH_SIZE = 32 # batch大小 TRAINING_EXAMPLES = 10000 # 训练数据个数 TESTING_EXAMPLES = 1000 # 测试数据个数 SAMPLE_GAP = 0.01 # 采样间隔 # 定义生成正弦数据的函数 def generate_data(seq): X = [] Y = [] # 序列的第i项和后面的TIMESTEPS-1项合在一起作为输入;第i+TIMESTEPS项作为输出 # 即用sin函数前面的TIMESTPES个点的信息，预测第i+TIMESTEPS个点的函数值 for i in range(len(seq) - TIMESTEPS - 1): 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 LstmCell(): lstm_cell = rnn.BasicLSTMCell(HIDDEN_SIZE,state_is_tuple=True) return lstm_cell # 定义lstm模型 def lstm_model(X, y): cell = rnn.MultiRNNCell([LstmCell() for _ in range(NUM_LAYERS)]) output, _ = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32) output = tf.reshape(output, [-1, HIDDEN_SIZE]) # 通过无激活函数的全连接层计算线性回归，并将数据压缩成一维数组结构 predictions = tf.contrib.layers.fully_connected(output, 1, None) # 将predictions和labels调整统一的shape labels = tf.reshape(y, [-1]) predictions = tf.reshape(predictions, [-1]) loss = tf.losses.mean_squared_error(predictions, labels) train_op = tf.contrib.layers.optimize_loss(loss, tf.contrib.framework.get_global_step(), optimizer="Adagrad", learning_rate=0.1) return predictions, loss, train_op # 进行训练 # 封装之前定义的lstm regressor = SKCompat(learn.Estimator(model_fn=lstm_model, model_dir="Models/model_2")) # 生成数据 test_start = TRAINING_EXAMPLES * SAMPLE_GAP test_end = (TRAINING_EXAMPLES + TESTING_EXAMPLES) * SAMPLE_GAP train_X, train_y = generate_data(np.sin(np.linspace(0, test_start, TRAINING_EXAMPLES, dtype=np.float32))) test_X, test_y = generate_data(np.sin(np.linspace(test_start, test_end, TESTING_EXAMPLES, dtype=np.float32))) # 拟合数据 regressor.fit(train_X, train_y, batch_size=BATCH_SIZE, steps=TRAINING_STEPS) # 计算预测值 predicted = [[pred] for pred in regressor.predict(test_X)] # 计算MSE rmse = np.sqrt(((predicted - test_y) ** 2).mean(axis=0)) print("Mean Square Error is:%f" % rmse[0]) plot_predicted, = plt.plot(predicted, label='predicted') plot_test, = plt.plot(test_y, label='real_sin') plt.legend([plot_predicted, plot_test],['predicted', 'real_sin']) plt.show()
预测的结果如下所示
2017-05-21 17:43:49.057377: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.057871: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.058284: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.058626: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.058981: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.059897: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.060207: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-21 17:43:49.060843: W c: f_jenkinshomeworkspace elease-windevicecpuoswindows ensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. Mean Square Error is:0.001686