RNN - 预测正弦函数
参考《TensorFlow实战Google深度学习框架》。不使用TFLearn，只使用TensorFlow完整代码看这里如果对RNN不理解，请看RNN递归神经网络的直观理解：基于TensorFlow的简单RNN例子import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt%matplotlib inline
数据准备
# 训练数据个数training_examples = 10000# 测试数据个数testing_examples = 1000# sin函数的采样间隔sample_gap = 0.01# 每个训练样本的长度timesteps = 20
def generate_data(seq):'''生成数据，seq是一序列的连续的sin的值'''X = []y = []# 用前 timesteps 个sin值，估计第 timesteps+1 个# 因此， 输入 X 是一段序列，输出 y 是一个值 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)
test_start = training_examples*sample_gaptest_end = test_start + testing_examples*sample_gaptrain_x, train_y = generate_data( np.sin( np.linspace(0, test_start, training_examples) ) )test_x, test_y = generate_data( np.sin( np.linspace(test_start, test_end, testing_examples) ) )
建立RNN模型
设置模型参数
lstm_size = 30lstm_layers = 2batch_size = 64
定义输入输出
x = tf.placeholder(tf.float32, [None, timesteps, 1], name='input_x')y_ = tf.placeholder(tf.float32, [None, 1], name='input_y')keep_prob = tf.placeholder(tf.float32, name='keep_prob')
建立LSTM层
# 有lstm_size个单元lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)# 添加dropoutdrop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)# 一层不够，就多来几层def lstm_cell():return tf.contrib.rnn.BasicLSTMCell(lstm_size)cell = tf.contrib.rnn.MultiRNNCell([ lstm_cell() for _ in range(lstm_layers)])# 进行forward，得到隐层的输出outputs, final_state = tf.nn.dynamic_rnn(cell, x, dtype=tf.float32)# 在本问题中只关注最后一个时刻的输出结果，该结果为下一个时刻的预测值outputs = outputs[:,-1]# 定义输出层, 输出值[-1,1]，因此激活函数用tanhpredictions = tf.contrib.layers.fully_connected(outputs, 1, activation_fn=tf.tanh)# 定义损失函数cost = tf.losses.mean_squared_error(y_, predictions)# 定义优化步骤optimizer = tf.train.AdamOptimizer().minimize(cost)
训练
# 获取一个batch_size大小的数据def get_batches(X, y, batch_size=64):for i in range(0, len(X), batch_size):begin_i = iend_i = i + batch_size if (i+batch_size) < len(X) else len(X)yield X[begin_i:end_i], y[begin_i:end_i]
epochs = 20session = tf.Session()with session.as_default() as sess:# 初始化变量tf.global_variables_initializer().run()iteration = 1for e in range(epochs):for xs, ys in get_batches(train_x, train_y, batch_size):# xs[:,:,None] 增加一个维度，例如[64, 20] ==> [64, 20, 1]，为了对应输入# 同理 ys[:,None]feed_dict = { x:xs[:,:,None], y_:ys[:,None], keep_prob:.5 }loss, _ = sess.run([cost, optimizer], feed_dict=feed_dict)if iteration % 100 == 0:print('Epochs:{}/{}'.format(e, epochs),'Iteration:{}'.format(iteration),'Train loss: {:.8f}'.format(loss))iteration += 1
Epochs:0/20 Iteration:100 Train loss: 0.01009926Epochs:1/20 Iteration:200 Train loss: 0.0673Epochs:1/20 Iteration:300 Train loss: 0.00237983Epochs:2/20 Iteration:400 Train loss: 0.00029798Epochs:3/20 Iteration:500 Train loss: 0.00283409Epochs:3/20 Iteration:600 Train loss: 0.00115144Epochs:4/20 Iteration:700 Train loss: 0.00130756Epochs:5/20 Iteration:800 Train loss: 0.00029282Epochs:5/20 Iteration:900 Train loss: 0.00045034Epochs:6/20 Iteration:1000 Train loss: 0.00007531Epochs:7/20 Iteration:1100 Train loss: 0.00189699Epochs:7/20 Iteration:1200 Train loss: 0.00022669Epochs:8/20 Iteration:1300 Train loss: 0.00065262Epochs:8/20 Iteration:1400 Train loss: 0.00001342Epochs:9/20 Iteration:1500 Train loss: 0.00037799Epochs:10/20 Iteration:1600 Train loss: 0.00009412Epochs:10/20 Iteration:1700 Train loss: 0.00110568Epochs:11/20 Iteration:1800 Train loss: 0.00024895Epochs:12/20 Iteration:1900 Train loss: 0.00287319Epochs:12/20 Iteration:2000 Train loss: 0.00012025Epochs:13/20 Iteration:2100 Train loss: 0.00353661Epochs:14/20 Iteration:2200 Train loss: 0.00045697Epochs:14/20 Iteration:2300 Train loss: 0.00103393Epochs:15/20 Iteration:2400 Train loss: 0.00045038Epochs:16/20 Iteration:2500 Train loss: 0.00022164Epochs:16/20 Iteration:2600 Train loss: 0.00026206Epochs:17/20 Iteration:2700 Train loss: 0.00279484Epochs:17/20 Iteration:2800 Train loss: 0.00024887Epochs:18/20 Iteration:2900 Train loss: 0.00263336Epochs:19/20 Iteration:3000 Train loss: 0.00071482Epochs:19/20 Iteration:3100 Train loss: 0.00026286
测试
with session.as_default() as sess:## 测试结果feed_dict = {x:test_x[:,:,None], keep_prob:1.0}results = sess.run(predictions, feed_dict=feed_dict)plt.plot(results,'r', label='predicted')plt.plot(test_y, 'g--', label='real sin')plt.legend()plt.show()
