在keras中使用reshape删除维度？

Question

是否可以使用重塑或任何其他功能删除维度。

我有以下网络。

import keras
from keras.layers.merge import Concatenate
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers import Dropout
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv2D, MaxPooling2D, Reshape, ZeroPadding2D
import numpy as np


#Number_of_splits = ((input_width-win_dim)+1)/stride_dim
splits = ((40-5)+1)/1
print splits


train_data_1 = np.random.randint(100,size=(100,splits,45,5,3))
test_data_1 = np.random.randint(100,size=(10,splits,45,5,3))
labels_train_data =np.random.randint(145,size=(100,15))
labels_test_data =np.random.randint(145,size=(10,15))


list_of_input = [Input(shape = (45,5,3)) for i in range(splits)]
list_of_conv_output = []
list_of_max_out = []
for i in range(splits):
    list_of_conv_output.append(Conv2D(filters = 145 , kernel_size = (15,3))(list_of_input[i])) #output dim: 36x(31,3,145)
    list_of_max_out.append((MaxPooling2D(pool_size=(2,2))(list_of_conv_output[i]))) #output dim: 36x(15,1,145)


merge = keras.layers.concatenate(list_of_max_out) #Output dim: (15,1,5220)
#reshape = Reshape((merge.shape[0],merge.shape[3]))(merge) # expected output dim: (15,145)


dense1 = Dense(units = 1000, activation = 'relu',    name = "dense_1")(merge)
dense2 = Dense(units = 1000, activation = 'relu',    name = "dense_2")(dense1)
dense3 = Dense(units = 145 , activation = 'softmax', name = "dense_3")(dense2)






model = Model(inputs = list_of_input , outputs = dense3)
model.compile(loss="sparse_categorical_crossentropy", optimizer="adam")


print model.summary()


raw_input("SDasd")
hist_current = model.fit(x = [train_input[i] for i in range(100)],
                    y = labels_train_data,
                    shuffle=False,
                    validation_data=([test_input[i] for i in range(10)], labels_test_data),
                    validation_split=0.1,
                    epochs=150000,
                    batch_size = 15,
                    verbose=1)

maxpooling 层创建了一个维度为 (15,1,36) 的输出，我想删除中间轴，因此输出维度最终为 (15,36)..

如果可能的话，我想避免指定外部尺寸，或者因为我已经尝试使用先前的图层尺寸来重塑它。

#reshape = Reshape((merge.shape[0],merge.shape[3]))(merge) # expected output dim: (15,145)

我需要整个网络的输出维度为 (15,145)，其中中间维度导致了一些问题。

如何删除中间尺寸？

Answer 1

reshape = Reshape((15,145))(merge) # expected output dim: (15,145)

Answer 2

我想删除所有等于 1 的维度，但不使用 Reshape 指定特定大小，这样如果我更改卷积中的输入大小或核数，我的代码就不会中断。这适用于 tensorflow 后端上的功能性 keras API。

from keras.layers.core import Reshape

old_layer = Conv2D(#actualArguments) (older_layer)
#old_layer yields, e.g., a (None, 15,1,36) size tensor, where None is the batch size

newdim = tuple([x for x in old_layer.shape.as_list() if x != 1 and x is not None])
#newdim is now (15, 36). Reshape does not take batch size as an input dimension.
reshape_layer = Reshape(newdim) (old_layer)

在keras中使用reshape删除维度？

Removing dimension using reshape in keras?

python-2.7

keras

reshape

dimensions