来自keras_contrib的Densenet问题

问题描述

我正在尝试使用 Densenet 来自keras_contrib，用于我自己的数据，尺寸为(30k,2,96,96).

I am trying to use the Densenet from the keras_contrib for my own data with dimensions (30k,2,96,96).

是否可以对我的形状数据使用此实现?它给出以下错误和警告.

Is it not possible to use this implementation with my data of the shape? It gives following error and warning.

    Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 96, 96, 2)    0                                            
__________________________________________________________________________________________________
initial_conv2D (Conv2D)         (None, 96, 96, 16)   288         input_1[0][0]                    
__________________________________________________________________________________________________
dense_0_0_bn (BatchNormalizatio (None, 96, 96, 16)   64          initial_conv2D[0][0]             
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 96, 96, 16)   0           dense_0_0_bn[0][0]               
__________________________________________________________________________________________________
dense_0_0_conv2D (Conv2D)       (None, 96, 96, 4)    576         activation_1[0][0]               
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 96, 96, 20)   0           initial_conv2D[0][0]             
                                                                 dense_0_0_conv2D[0][0]           
__________________________________________________________________________________________________
final_bn (BatchNormalization)   (None, 96, 96, 20)   80          concatenate_1[0][0]              
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 96, 96, 20)   0           final_bn[0][0]                   
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 96, 96, 2)    42          activation_2[0][0]               
==================================================================================================
Total params: 1,050
Trainable params: 978
Non-trainable params: 72
__________________________________________________________________________________________________
Finished compiling
/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras_preprocessing/image.py:1213: UserWarning: Expected input to be images (as Numpy array) following the data format convention "channels_last" (channels on axis 3), i.e. expected either 1, 3 or 4 channels on axis 3. However, it was passed an array with shape (39840, 96, 96, 2) (2 channels).
  ' channels).')
/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras_preprocessing/image.py:1437: UserWarning: NumpyArrayIterator is set to use the data format convention "channels_last" (channels on axis 3), i.e. expected either 1, 3, or 4 channels on axis 3. However, it was passed an array with shape (39840, 96, 96, 2) (2 channels).
  str(self.x.shape[channels_axis]) + ' channels).')
Traceback (most recent call last):
  File "keras_densenet.py", line 149, in <module>
    fit_model(X_train,y_train,X_val,y_val)
  File "keras_densenet.py", line 140, in fit_model
    verbose=2)
  File "/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras/engine/training.py", line 1415, in fit_generator
    initial_epoch=initial_epoch)
  File "/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras/engine/training_generator.py", line 140, in fit_generator
    val_x, val_y, val_sample_weight)
  File "/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras/engine/training.py", line 787, in _standardize_user_data
    exception_prefix='target')
  File "/home/arka/anaconda2/envs/hyperas/lib/python3.6/site-packages/keras/engine/training_utils.py", line 127, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking target: expected dense_1 to have 4 dimensions, but got array with shape (7440, 2)

这就是我在这里调用Densenet函数的方式.至少可以告诉我是否可以通过Densenet功能使用两个通道输入，这将是一个很大的帮助.

This is how I am calling the Densenet function here. At least can tell me if this is possible to use two channel inputs with this Densenet function, will be a great help.

推荐答案

文档说，它应该恰好具有3个输入通道. https://keras.io/applications/#densenet 您可以使用嵌入层，也可以使用具有恒定值的尺寸标注广告(我猜是这样).

The documentation says, it should have exactly 3 inputs channels. https://keras.io/applications/#densenetYou could use an embedding layer or maybe ad a dimension with constant values I guess.

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