keras：对于小批量中的每个样本，具有不同滤波器的一维卷积

这是非常棘手的问题，我们将获得帮助K.depthwise_conv2d（唯一一个单独处理通道的卷积），在该过程中，我们会将样本转换为通道，每个通道具有所需的输出，然后重塑为预期的形状

因此，想法是：

1-变换输入形状（适当重新排序）

#from `(batch_size, maxlen, input_dim)` 
#to `(1, maxlen, input_dim, batch_size)`   
x = K.expand_dims(x, axis=0)
x = K.permute_dimensions(x, (0,2,3,1))

2-具有形状的滤镜 (kernel_size, input_dim, batch_size, output_dim)

#transform your kernels:
filters = K.permute_dimensions(filters, (2, 3, 0, 1))

3转换结果（正确排序）：

#from `(1, result_len, 1, batch_size * output_dim)` 
#to `(batch_size, result_len, output_dim)`   
results = K.reshape(results, (output_length, -1, output_dim) #-1 for batch size
results = K.permute_dimensions(results, (1,0,2))

这是一个示例代码：

from keras.layers import *
import keras.backend as K
from keras.models import Model

#dimensions
length = 11 #maxlen
features = 3 #input_dim
filtersize = 2 #kernel_size
out_dim = 5 #output_dim
samples = 7 #batch_size

#keep track of output length for reshaping
outlen = length - filtersize + 1

#creating dummy filters with the desired shape
npfilters = np.arange(features*filtersize*out_dim*samples)
npfilters = npfilters.astype(np.float64)
npfilters = npfilters.reshape((filtersize, features, samples, out_dim))
kerasfilters = K.variable(npfilters)

#function that performs the convolution
def sample_wise_conv1d(x):

    #reshape and reorder inputs properly
    x= K.expand_dims(x, axis=0)
    x = K.permute_dimensions(x, (0,2,3,1))
    print('in shape', K.int_shape(x))

    #perform the convolution
    print("filter shape", K.int_shape(kerasfilters))
    results =  K.depthwise_conv2d(x, kerasfilters)
    print('out shape', K.int_shape(results))

    #reshape and reorder the results properly
    results = K.reshape(results, (outlen, samples, out_dim))
    results = K.permute_dimensions(results, (1,0,2))
    print('final shape', K.int_shape(results))

    return results


#creating a model that performs the operation
inputs = Input((length, features))
outputs = Lambda(sample_wise_conv1d)(inputs)

model = Model(inputs, outputs)

#predicting from the model
inputdata = np.arange(samples*length*features).reshape((samples, length, features))
results = model.predict(inputdata)

print(results.shape)

测试代码

#creating a single conv1D model for each sample
for i in range(samples):

    #get the respective input sample and filter
    x = inputdata[i:i+1]
    filts = npfilters[:,:,i,:]
    print(filts.shape)

    #make a model with conv1d
    in1D = Input((length, features))
    out1D = Lambda(lambda x: K.conv1d(x, K.variable(filts)))(in1D)
    model1D = Model(in1D, out1D)

    #compare this model's predictions with the respective prediction from the function
    pred1D = model1D.predict(x)
    pred2D = results[i]

    print(pred1D == pred2D)