Tensorflow LSTM连续序列预测方法实践

本文展示了如何使用循环神经网络去估计一个向量序列，我们会使用到LSTM的网络。我在网上找的

大多数用到LSTM的例子都是用来解决自然语言处理方面问题的，还没有找到相关的例子可以用在预测连续

值序列上，所以写下了本文。

所以本文的任务是基于历史观察数据去预测一系列连续的实数。传统的神经网络做不到这一点，但是循环神经

网络可以解决该问题，因为他们能够存储历史信息来预测未来事件。

在下面的一个例子中我们将尝试预测一组函数sin、cos和x*sin(x).

首先让我们建立一个模型lstm_model, 这个模型是一系列不同时间步的lstm单元的堆叠,后接一个深层网络。

def lstm_model(time_steps, rnn_layers, dense_layers=None):
"""
Creates a deep model based on:
* stacked lstm cells
* an optional dense layers
:param time_steps: the number of time steps the model will be looking at.
:param rnn_layers: list of int or dict
* list of int: the steps used to instantiate the `BasicLSTMCell` cell
* list of dict: [{steps: int, keep_prob: int}, ...]
:param dense_layers: list of nodes for each layer
:return: the model definition
"""

def lstm_cells(layers):
if isinstance(layers[0], dict):
return [tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.BasicLSTMCell(layer['steps'],
state_is_tuple=True),
layer['keep_prob'])
if layer.get('keep_prob') else tf.nn.rnn_cell.BasicLSTMCell(layer['steps'],
state_is_tuple=True)
for layer in layers]
return [tf.nn.rnn_cell.BasicLSTMCell(steps, state_is_tuple=True) for steps in layers]

def dnn_layers(input_layers, layers):
if layers and isinstance(layers, dict):
return learn.ops.dnn(input_layers,
layers['layers'],
activation=layers.get('activation'),
dropout=layers.get('dropout'))
elif layers:
return learn.ops.dnn(input_layers, layers)
else:
return input_layers

def _lstm_model(X, y):
stacked_lstm = tf.nn.rnn_cell.MultiRNNCell(lstm_cells(rnn_layers), state_is_tuple=True)
x_ = learn.ops.split_squeeze(1, time_steps, X)
output, layers = tf.nn.rnn(stacked_lstm, x_, dtype=dtypes.float32)
output = dnn_layers(output[-1], dense_layers)
return learn.models.linear_regression(output, y)

return _lstm_model

所以我们的模型接收的数据维度应该是这样的:(batch size,time steps of the first lstm cell, num_features)

下一步要做的就是把我们的数据重整成模型可以接收的格式。

def rnn_data(data, time_steps, labels=False):
"""
creates new data frame based on previous observation
* example:
l = [1, 2, 3, 4, 5]
time_steps = 2
-> labels == False [[1, 2], [2, 3], [3, 4]]
-> labels == True [2, 3, 4, 5]
"""
rnn_df = []
for i in range(len(data) - time_steps):
if labels:
try:
rnn_df.append(data.iloc[i + time_steps].as_matrix())
except AttributeError:
rnn_df.append(data.iloc[i + time_steps])
else:
data_ = data.iloc[i: i + time_steps].as_matrix()
rnn_df.append(data_ if len(data_.shape) > 1 else [[i] for i in data_])
return np.array(rnn_df)

def split_data(data, val_size=0.1, test_size=0.1):
"""
splits data to training, validation and testing parts
"""
ntest = int(round(len(data) * (1 - test_size)))
nval = int(round(len(data.iloc[:ntest]) * (1 - val_size)))

df_train, df_val, df_test = data.iloc[:nval], data.iloc[nval:ntest], data.iloc[ntest:]

return df_train, df_val, df_test

def prepare_data(data, time_steps, labels=False, val_size=0.1, test_size=0.1):
"""
Given the number of `time_steps` and some data,
prepares training, validation and test data for an lstm cell.
"""
df_train, df_val, df_test = split_data(data, val_size, test_size)
return (rnn_data(df_train, time_steps, labels=labels),
rnn_data(df_val, time_steps, labels=labels),
rnn_data(df_test, time_steps, labels=labels))

def generate_data(fct, x, time_steps, seperate=False):
"""generates data with based on a function fct"""
data = fct(x)
if not isinstance(data, pd.DataFrame):
data = pd.DataFrame(data)
train_x, val_x, test_x = prepare_data(data['a'] if seperate else data, time_steps)
train_y, val_y, test_y =
a410
prepare_data(data['b'] if seperate else data, time_steps, labels=True)
return dict(train=train_x, val=val_x, test=test_x), dict(train=train_y, val=val_y, test=test_y)

这就会产生数据允许我们的模型往序列的前time_steps回看来预测未来数据。例如第一个单元

是10个time steps的单元，那么每做一次预测，我们都需要输入10个历史数据点。我们想要预测的

数值应该和数据点里的第10个相关。

首先定义超参数

LOG_DIR = './ops_logs'
TIMESTEPS = 5
RNN_LAYERS = [{'steps': TIMESTEPS}, {'steps': TIMESTEPS, 'keep_prob': 0.5}]
DENSE_LAYERS = [2]
TRAINING_STEPS = 130000
BATCH_SIZE = 100
PRINT_STEPS = TRAINING_STEPS / 100

现在可以建立一个回归模型

regressor = learn.TensorFlowEstimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS),
n_classes=0,
verbose=1,
steps=TRAINING_STEPS,
optimizer='Adagrad',
learning_rate=0.03,
batch_size=BATCH_SIZE)

预测sin函数

X, y = generate_data(np.sin, np.linspace(0, 100, 10000), TIMESTEPS, seperate=False)
# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
every_n_steps=PRINT_STEPS,
early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'], validation_monitor, logdir=LOG_DIR)

# > last training steps
# Step #9700, epoch #119, avg. train loss: 0.00082, avg. val loss: 0.00084
# Step #9800, epoch #120, avg. train loss: 0.00083, avg. val loss: 0.00082
# Step #9900, epoch #122, avg. train loss: 0.00082, avg. val loss: 0.00082
# Step #10000, epoch #123, avg. train loss: 0.00081, avg. val loss: 0.00081

预测测试数据

mse = mean_squared_error(regressor.predict(X['test']), y['test'])
print ("Error: {}".format(mse))
# 0.000776

同时预测 sin和cos函数

def sin_cos(x):
return pd.DataFrame(dict(a=np.sin(x), b=np.cos(x)), index=x)

X, y = generate_data(sin_cos, np.linspace(0, 100, 10000), TIMESTEPS, seperate=False)
# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
every_n_steps=PRINT_STEPS,
early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'], validation_monitor, logdir=LOG_DIR)

# > last training steps
# Step #9500, epoch #117, avg. train loss: 0.00120, avg. val loss: 0.00118
# Step #9600, epoch #118, avg. train loss: 0.00121, avg. val loss: 0.00118
# Step #9700, epoch #119, avg. train loss: 0.00118, avg. val loss: 0.00118
# Step #9800, epoch #120, avg. train loss: 0.00118, avg. val loss: 0.00116
# Step #9900, epoch #122, avg. train loss: 0.00118, avg. val loss: 0.00115
# Step #10000, epoch #123, avg. train loss: 0.00117, avg. val loss: 0.00115

预测测试数据

mse = mean_squared_error(regressor.predict(X['test']), y['test'])
print ("Error: {}".format(mse))
# 0.001144

还有一个x*sinx的就不玩了，下面贴出英文原文
http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html