二维卷积层中Keras中merge（）函数的类型错误

Question

我正在尝试重新创建Inception模型版本4。但是我想在我的图像数据集标准形状(224,224,3)上对其进行训练，因此我不接受任何预先训练的权重。 但是我遇到这样的错误。

x = merge([x1, x2], mode='concat', concat_axis=channel_axis)
TypeError: 'module' object is not callable

这是代码：

def inception_stem(input):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    # Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th)
    x = conv_block(input, 32, 3, 3, subsample=(2, 2), border_mode='valid')
    x = conv_block(x, 32, 3, 3, border_mode='valid')
    x = conv_block(x, 64, 3, 3)

    x1 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(x)
    x2 = conv_block(x, 96, 3, 3, subsample=(2, 2), border_mode='valid')
    x = tf.concat([x1,x2],axis=channel_axis)
    #x = merge([x1, x2], mode='concat', concat_axis=channel_axis) #here is the error occuring try find out the reason behind it

    x1 = conv_block(x, 64, 1, 1)
    x1 = conv_block(x1, 96, 3, 3, border_mode='valid')

    x2 = conv_block(x, 64, 1, 1)
    x2 = conv_block(x2, 64, 1, 7)
    x2 = conv_block(x2, 64, 7, 1)
    x2 = conv_block(x2, 96, 3, 3, border_mode='valid')

    x = merge([x1, x2], mode='concat', concat_axis=channel_axis)

    x1 = conv_block(x, 192, 3, 3, subsample=(2, 2), border_mode='valid')
    x2 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(x)

    x = merge([x1, x2], mode='concat', concat_axis=channel_axis)
    return x

我正在使用python 3.6 ， keras 2.2.2 ， tensorflow-gpu 1.9.0 。

我关注了GitHub，但是答案并不明确。 谁能找到解决方案。

Answer 1

使用连接层，这应该对您有帮助

from tensorflow.python.keras.layers import concatenate
x = concatenate([x1, x2], axis=channel_axis)
return x