Question

我对Keras和深度学习有点新鲜。我目前正在尝试复制此paper但是当我编译第二个模型（使用LSTM）时，我收到以下错误：

"TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'"

模型的描述如下：

输入（长度T是设备特定的窗口大小）
与过滤器size 3,5和7并行1D卷积分别为stride=1，number of filters=32， activation type=linear，border mode=same
合并输出的合并图层并行1D卷积
双向LSTM由前向LSTM组成和一个向后的LSTM，output_dim=128
双向LSTM由前向LSTM组成和一个向后的LSTM，output_dim=128
密集层，output_dim=128，activation type=ReLU
密集层，output_dim= T，activation type=linear

我的代码是：

from keras import layers, Input
from keras.models import Model

def lstm_net(T):
    input_layer = Input(shape=(T,1))
    branch_a = layers.Conv1D(32, 3, activation='linear', padding='same', strides=1)(input_layer)
    branch_b = layers.Conv1D(32, 5, activation='linear', padding='same', strides=1)(input_layer)
    branch_c = layers.Conv1D(32, 7, activation='linear', padding='same', strides=1)(input_layer)

    merge_layer = layers.Concatenate(axis=-1)([branch_a, branch_b, branch_c])
    print(merge_layer.shape)
    BLSTM1 = layers.Bidirectional(layers.LSTM(128, input_shape=(8,40,96)))(merge_layer)
    print(BLSTM1.shape)
    BLSTM2 = layers.Bidirectional(layers.LSTM(128))(BLSTM1)
    dense_layer = layers.Dense(128, activation='relu')(BLSTM2)
    output_dense = layers.Dense(1, activation='linear')(dense_layer)
    model = Model(input_layer, output_dense)
    model.name = "lstm_net"
    return model

model = lstm_net(40)

之后我得到了上述错误。我的目标是给出一批长度为40的8个序列作为输入，并获得一批8个长度为40的序列作为输出。我在Keras Github LSTM layer cannot connect to Dense layer after Flatten #818上发现了这个问题，@ fchollet建议我应该指定＆＃39; input_shape＆＃39;在我做的第一层，但可能不正确。我把两个打印语句看成形状如何变化，输出是：

(?, 40, 96)
(?, 256)

错误发生在BLSTM2定义的行上，可以完整地看到here

Answer 1

Your problem lies in these three lines:

BLSTM1 = layers.Bidirectional(layers.LSTM(128, input_shape=(8,40,96)))(merge_layer)
print(BLSTM1.shape)
BLSTM2 = layers.Bidirectional(layers.LSTM(128))(BLSTM1)

As a default, LSTM is returning only the last element of computations - so your data is losing its sequential nature. That's why the proceeding layer raises an error. Change this line to:

BLSTM1 = layers.Bidirectional(layers.LSTM(128, return_sequences=True))(merge_layer)
print(BLSTM1.shape)
BLSTM2 = layers.Bidirectional(layers.LSTM(128))(BLSTM1)

In order to make the input to the second LSTM to have sequential nature also.

Aside of this - I'd rather not use input_shape in middle model layer as it's automatically inferred.

尝试在Keras中创建BLSTM网络时出现TypeError

1 个答案: