具有多种功能的RNN

我对机器学习算法（基本的随机森林和线性回归类型的东西）有一些自学的知识。我决定分支并开始与Keras学习RNN。在查看大多数通常涉及库存预测的示例时，我没有找到实现多个功能的任何基本示例，除了第一列是功能日期，另一列是输出。我是否缺少关键的基本事物？

如果有人举个例子，我将不胜感激。

谢谢！

递归神经网络（RNN）旨在学习序列数据。如您所料，它们绝对可以将多个功能作为输入！Keras的RNN接受时间步长T的 2D输入（T，F）和特征F（我在这里忽略了批次尺寸）。

但是，您并不总是需要或想要中间时间步长t = 1、2 ...（T -1）。因此，Keras灵活地支持两种模式。要使其输出所有T个时间步长，return_sequences=True请在构造时传递到您的RNN（例如LSTM或GRU）。如果只希望最后一个时间步长t = T，则使用return_sequences=False（如果不传递return_sequences给构造函数，则为默认设置）。

以下是这两种模式的示例。

示例1：学习序列

这是训练LSTM（RNN类型）的快速示例，该LSTM使整个序列保持不变。在此示例中，每个输入数据点具有2个时间步长，每个时间步长都具有3个功能；输出数据有2个时间步长（因为return_sequences=True），每个时间步长都有4个数据点（因为这是我传递给的大小LSTM）。

import keras.layers as L
import keras.models as M

import numpy

# The inputs to the model.
# We will create two data points, just for the example.
data_x = numpy.array([
    # Datapoint 1
    [
        # Input features at timestep 1
        [1, 2, 3],
        # Input features at timestep 2
        [4, 5, 6]
    ],
    # Datapoint 2
    [
        # Features at timestep 1
        [7, 8, 9],
        # Features at timestep 2
        [10, 11, 12]
    ]
])

# The desired model outputs.
# We will create two data points, just for the example.
data_y = numpy.array([
    # Datapoint 1
    [
        # Target features at timestep 1
        [101, 102, 103, 104],
        # Target features at timestep 2
        [105, 106, 107, 108]
    ],
    # Datapoint 2
    [
        # Target features at timestep 1
        [201, 202, 203, 204],
        # Target features at timestep 2
        [205, 206, 207, 208]
    ]
])

# Each input data point has 2 timesteps, each with 3 features.
# So the input shape (excluding batch_size) is (2, 3), which
# matches the shape of each data point in data_x above.
model_input = L.Input(shape=(2, 3))

# This RNN will return timesteps with 4 features each.
# Because return_sequences=True, it will output 2 timesteps, each
# with 4 features. So the output shape (excluding batch size) is
# (2, 4), which matches the shape of each data point in data_y above.
model_output = L.LSTM(4, return_sequences=True)(model_input)

# Create the model.
model = M.Model(input=model_input, output=model_output)

# You need to pick appropriate loss/optimizers for your problem.
# I'm just using these to make the example compile.
model.compile('sgd', 'mean_squared_error')

# Train
model.fit(data_x, data_y)

示例2：了解最后一个时间步

另一方面，如果您想训练仅输出序列中最后一个时间步的LSTM，则需要进行设置return_sequences=False（或将其从构造函数中完全删除，因为这False是默认设置）。然后，您的输出数据（data_y在上面的示例中）需要重新排列，因为您只需要提供最后的时间步长即可。因此，在第二个示例中，每个输入数据点仍具有2个时间步长，每个时间步长都具有3个功能。但是，输出数据对于每个数据点来说都是单个矢量，因为我们已将所有内容展平为单个时间步长。不过，每个输出向量仍然具有4个特征（因为这是我传递给的大小LSTM）。

import keras.layers as L
import keras.models as M

import numpy

# The inputs to the model.
# We will create two data points, just for the example.
data_x = numpy.array([
    # Datapoint 1
    [
        # Input features at timestep 1
        [1, 2, 3],
        # Input features at timestep 2
        [4, 5, 6]
    ],
    # Datapoint 2
    [
        # Features at timestep 1
        [7, 8, 9],
        # Features at timestep 2
        [10, 11, 12]
    ]
])

# The desired model outputs.
# We will create two data points, just for the example.
data_y = numpy.array([
    # Datapoint 1
    # Target features at timestep 2
    [105, 106, 107, 108],
    # Datapoint 2
    # Target features at timestep 2
    [205, 206, 207, 208]
])

# Each input data point has 2 timesteps, each with 3 features.
# So the input shape (excluding batch_size) is (2, 3), which
# matches the shape of each data point in data_x above.
model_input = L.Input(shape=(2, 3))

# This RNN will return timesteps with 4 features each.
# Because return_sequences=False, it will output 2 timesteps, each
# with 4 features. So the output shape (excluding batch size) is
# (2, 4), which matches the shape of each data point in data_y above.
model_output = L.LSTM(4, return_sequences=False)(model_input)

# Create the model.
model = M.Model(input=model_input, output=model_output)

# You need to pick appropriate loss/optimizers for your problem.
# I'm just using these to make the example compile.
model.compile('sgd', 'mean_squared_error')

# Train
model.fit(data_x, data_y)