LSTM mod的Keras多输出数据整形

Question

我有一个包含多个输出的Keras LSTM模型。 模型定义如下：

outputs=[]

main_input = Input(shape= (seq_length,feature_cnt), name='main_input')
lstm = LSTM(32,return_sequences=True)(main_input)
for _ in range((output_branches)): #output_branches is the number of output branches of the model
    prediction = LSTM(8,return_sequences=False)(lstm)
    out = Dense(1)(prediction)
    outputs.append(out)

model = Model(inputs=main_input, outputs=outputs)
model.compile(optimizer='rmsprop',loss='mse')    

我在重塑输出数据时遇到问题。 重塑输出数据的代码是：

^{2}$

我得到了以下错误：

ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 5 array(s), but instead got the following list of 1 arrays: [array([[[0.29670931], [0.16652206], [0.25114482], [0.36952324], [0.09429612]],

   [[0.16652206],
    [0.25114482],
    [0.36952324],
    [0.09429612],...

如何正确地重塑输出数据的形状？在

Answer 1

由于输出数量等于output_branches，因此输出数据必须是具有相同数量数组的list。在

基本上，如果输出数据是您的reshape建议的中间维度：

y = [ y[:,i] for i in range(output_branches)]   

Answer 2

这取决于y最初是如何构造的。这里我假设y是批处理中每个序列的单值标签。在

当有多个输入/输出时，model.fit()期望给出相应的输入/输出列表。np.split(y, output_branches, axis=-1)在下面的一个完全可复制的例子中，正是这样做的-对于每个批次，将单个输出列表拆分为一个单独的输出列表，其中每个输出（在本例中）是1个元素列表：

import tensorflow as tf
import numpy as np

tf.enable_eager_execution()

batch_size = 100
seq_length = 10
feature_cnt = 5
output_branches = 3

# Say we've got:
# - 100-element batch
# - of 10-element sequences
# - where each element of a sequence is a vector describing 5 features.
X = np.random.random_sample([batch_size, seq_length, feature_cnt])

# Every sequence of a batch is labelled with `output_branches` labels.
y = np.random.random_sample([batch_size, output_branches])
# Here y.shape() == (100, 3)

# Here we split the last axis of y (output_branches) into `output_branches` separate lists.
y = np.split(y, output_branches, axis=-1)
# Here y is not a numpy matrix anymore, but a list of matrices.
# E.g. y[0].shape() == (100, 1); y[1].shape() == (100, 1) etc...

outputs = []

main_input = tf.keras.layers.Input(shape=(seq_length, feature_cnt), name='main_input')
lstm = tf.keras.layers.LSTM(32, return_sequences=True)(main_input)
for _ in range(output_branches):
    prediction = tf.keras.layers.LSTM(8, return_sequences=False)(lstm)
    out = tf.keras.layers.Dense(1)(prediction)
    outputs.append(out)

model = tf.keras.models.Model(inputs=main_input, outputs=outputs)
model.compile(optimizer='rmsprop', loss='mse')

model.fit(X, y)

由于没有指定数据的确切外观，可能需要使用轴。在

编辑： 当作者从官方来源寻找答案时，它提到了here（虽然不是明确的，它只提到数据集应该产生什么，因此model.fit()期望什么样的输入结构）：

When calling fit with a Dataset object, it should yield either a tuple of lists like ([title_data, body_data, tags_data], [priority_targets, dept_targets]) or a tuple of dictionaries like ({'title': title_data, 'body': body_data, 'tags': tags_data}, {'priority': priority_targets, 'department': dept_targets}).