tensorflow19《TensorFlow实战Google深度学习框架》笔记-08-04 预测正弦函数 code

# 《TensorFlow实战Google深度学习框架》08 循环神经网络
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts08.05.py # 预测正弦函数

import numpy as np
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.estimators.estimator import SKCompat
from tensorflow.python.ops import array_ops as array_ops_
import matplotlib.pyplot as plt
learn = tf.contrib.learn

# 1. 设置神经网络的参数
HIDDEN_SIZE = 30
NUM_LAYERS = 2

TIMESTEPS = 10
TRAINING_STEPS = 3000
BATCH_SIZE = 32

TRAINING_EXAMPLES = 10000
TESTING_EXAMPLES = 1000
SAMPLE_GAP = 0.01

# 2. 定义生成正弦数据的函数
def generate_data(seq):
X = []
y = []

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)

# 3. 定义lstm模型
def lstm_model(X, y):
lstm_cell = tf.contrib.rnn.BasicLSTMCell(HIDDEN_SIZE, state_is_tuple=True)
cell = tf.contrib.rnn.MultiRNNCell([lstm_cell] * 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联接层计算线性回归，并将数据压缩成一维数组的结构。
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

# 4. 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])
'''
Mean Square Error is: 0.007281
'''
fig = plt.figure()
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'])
#fig.savefig('sin.png')
plt.show();