使用 keras 卷积 1D 层时出现负尺寸误差

Question

我正在尝试使用Keras创建一个char CNN。这种类型的 CNN 要求您使用Convolutional1D层。但是我尝试将它们添加到我的模型中的所有方法，它在创建阶段给了我错误。这是我的代码：

def char_cnn(n_vocab, max_len, n_classes):
conv_layers = [[256, 7, 3],
[256, 7, 3],
[256, 3, None],
[256, 3, None],
[256, 3, None],
[256, 3, 3]]
fully_layers = [1024, 1024]
th = 1e-6
embedding_size = 128
inputs = Input(shape=(max_len,), name='sent_input', dtype='int64')
# Embedding layer
x = Embedding(n_vocab, embedding_size, input_length=max_len)(inputs)
# Convolution layers
for cl in conv_layers:
x = Convolution1D(cl[0], cl[1])(x)
x = ThresholdedReLU(th)(x)
if not cl[2] is None:
x = MaxPooling1D(cl[2])(x)

x = Flatten()(x)

#Fully connected layers
for fl in fully_layers:
x = Dense(fl)(x)
x = ThresholdedReLU(th)(x)
x = Dropout(0.5)(x)

predictions = Dense(n_classes, activation='softmax')(x)
model = Model(input=inputs, output=predictions)
model.compile(optimizer='adam', loss='categorical_crossentropy')
return model

这是我尝试调用函数时收到的错误char_cnn

InvalidArgumentError                      Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
685           graph_def_version, node_def_str, input_shapes, input_tensors,
--> 686           input_tensors_as_shapes, status)
687   except errors.InvalidArgumentError as err:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
515             compat.as_text(c_api.TF_Message(self.status.status)),
--> 516             c_api.TF_GetCode(self.status.status))
517     # Delete the underlying status object from memory otherwise it stays alive
InvalidArgumentError: Negative dimension size caused by subtracting 3 from 1 for 'conv1d_26/convolution/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1,256], [1,3,256,256].

如何解决？

Answer 1

你的缩减采样太激进了，这里的关键论点是max_len：当它太小时，序列变得太短，无法执行卷积或最大池化。您设置pool_size=3，因此它会在每次池化后将序列缩小3倍(请参阅下面的示例)。我建议你试试pool_size=2.

该网络可以处理的最小max_len是max_len=123.在这种情况下x形状按以下方式转换(根据conv_layers)：

(?, 123, 128)
(?, 39, 256)
(?, 11, 256)
(?, 9, 256)
(?, 7, 256)
(?, 5, 256)

设置较小的值，例如max_len=120会导致在最后一层之前x.shape=(?, 4, 256)，并且无法执行此操作。