Keras报告TypeError:+不支持的操作数类型:'NoneType'和'int'

问题描述

我是Keras的初学者，只写一个玩具示例.它报告TypeError.代码和错误如下:

I'm a beginner in Keras and just write a toy example. It reports a TypeError. The code and error are as follows:

代码:

inputs = keras.Input(shape=(3, ))

cell = keras.layers.SimpleRNNCell(units=5, activation='softmax')
label = keras.layers.RNN(cell)(inputs)

model = keras.models.Model(inputs=inputs, outputs=label)
model.compile(optimizer='rmsprop',
              loss='mae',
              metrics=['acc'])

data = np.array([[1, 2, 3], [3, 4, 5]])
labels = np.array([1, 2])
model.fit(x=data, y=labels)

错误:

Traceback (most recent call last):
    File "/Users/david/Documents/code/python/Tensorflow/test.py", line 27, in <module>
        run()
    File "/Users/david/Documents/code/python/Tensorflow/test.py", line 21, in run
        label = keras.layers.RNN(cell)(inputs)
    File "/Users/david/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/recurrent.py", line 619, in __call__
...
    File "/Users/david/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py", line 473, in __call__
        scale /= max(1., (fan_in + fan_out) / 2.)
TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'

那么我该如何处理呢?

So how can I deal with it?

推荐答案

RNN层的输入将具有(num_timesteps, num_features)的形状，即每个样本均由num_timesteps个时间步组成，其中每个时间步都是长度为num_features.此外，时间步长(即num_timesteps)可以是可变的，也可以是未知的(即None)，但是特征的数目(即num_features)应该是固定的，并应从头开始指定.因此，您需要更改输入层的形状以与RNN层保持一致.例如:

The input to a RNN layer would have a shape of (num_timesteps, num_features), i.e. each sample consists of num_timesteps timesteps where each timestep is a vector of length num_features. Further, the number of timesteps (i.e. num_timesteps) could be variable or unknown (i.e. None) but the number of features (i.e. num_features) should be fixed and specified from the beginning. Therefore, you need to change the shape of Input layer to be consistent with the RNN layer. For example:

inputs = keras.Input(shape=(None, 3))  # variable number of timesteps each with length 3
inputs = keras.Input(shape=(4, 3))     # 4 timesteps each with length 3
inputs = keras.Input(shape=(4, None))  # this is WRONG! you can't do this. Number of features must be fixed

然后，还需要更改输入数据的形状(即data)，以使其与指定的输入形状一致(即，其形状必须为(num_samples, num_timesteps, num_features)).

Then, you also need to change the shape of input data (i.e. data) as well to be consistent with the input shape you have specified (i.e. it must have a shape of (num_samples, num_timesteps, num_features)).

作为旁注，您可以通过直接使用SimpleRNN层来更简单地定义RNN层:

As a side note, you could define the RNN layer more simply by using the SimpleRNN layer directly:

label = keras.layers.SimpleRNN(units=5, activation='softmax')(inputs)

这篇关于Keras报告TypeError:+不支持的操作数类型:'NoneType'和'int'的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！