详解如何获取深度学习模型中间层的输出值

水深无声 2022-11-13 12:25 268阅读 0赞

# 1. 引入 #

深度学习模型，大都是多层的网络，各个层可能各有不同（Dense, Dropout, Flatten, Activation, BatchNormalization, GlobalAveragePooling2D,Conv2D, MaxPooling2D, ZeroPadding2D,LSTM）。

有时候我们需要获取多层网络中某一层的输出值，用于做可视化，或者Embedding。

下面就以一个例子为例说明如何获取神经网络某一层的输出值。

# 2. 构建网络，各层加上name #

本文构建的多层网络模型如下：

from keras.models import Sequential
    from keras.layers import Dense, Dropout, Flatten
    from keras.layers import Conv2D, MaxPooling2D
    from keras_self_attention import SeqSelfAttention
    import keras
    import numpy as np
    
    num_classes = 6 # classification number
    x_train = np.random.randn(100, 15, 20, 3) # x_train.shape = (100, 15, 20, 3)
    y_train = np.random.randint(1, size=(100,num_classes)) # y_train.shape = (100, 6)
    input_shape = x_train.shape[-3:]# (15, 20, 3)
    
    
    model = Sequential()
    model.add(Conv2D(32, (2,11), activation='relu', padding='same', input_shape=input_shape, name='layer_conv_1'))
    model.add(Conv2D(32, (2,11), activation='relu', padding='same', name='layer_conv_2'))
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 1), name='layer_mp_1'))
    model.add(Conv2D(128, (2,7), activation='relu', padding='same', name='layer_conv_3'))
    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 1), name='layer_mp_2'))
    model.add( keras.layers.Reshape((48,128), name='layer_rsp_1') )
    model.add( SeqSelfAttention( attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL, name='layer_attention_1') )
    model.add(Flatten(name='layer_flatten_1'))
    model.add(Dense(440, activation='relu', name='layer_dense_1'))
    model.add(Dropout(0.5, name='layer_dropout_1'))
    model.add(Dense(num_classes, activation='softmax', name='layer_dense_2'))
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    model.summary()

这里注意几点：

1.  要在模型每一层都加入name属性，比如（name=‘layer\_conv\_1’）
2.  模型的训练数据是用numpy生成的随机数，具体参见注释
3.  使用的版本：tensorflow2.4.0，keras2.4.3，python==3.7

模型构建完成后，参数如下所示

Model: "sequential_1"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    layer_conv_1 (Conv2D)        (None, 15, 20, 32)        2144      
    _________________________________________________________________
    layer_conv_2 (Conv2D)        (None, 15, 20, 32)        22560     
    _________________________________________________________________
    layer_mp_1 (MaxPooling2D)    (None, 7, 18, 32)         0         
    _________________________________________________________________
    layer_conv_3 (Conv2D)        (None, 7, 18, 128)        57472     
    _________________________________________________________________
    layer_mp_2 (MaxPooling2D)    (None, 3, 16, 128)        0         
    _________________________________________________________________
    layer_rsp_1 (Reshape)        (None, 48, 128)           0         
    _________________________________________________________________
    layer_attention_1 (SeqSelfAt (None, 48, 128)           16385     
    _________________________________________________________________
    layer_flatten_1 (Flatten)    (None, 6144)              0         
    _________________________________________________________________
    layer_dense_1 (Dense)        (None, 440)               2703800   
    _________________________________________________________________
    layer_dropout_1 (Dropout)    (None, 440)               0         
    _________________________________________________________________
    layer_dense_2 (Dense)        (None, 6)                 2646      
    =================================================================
    Total params: 2,805,007
    Trainable params: 2,805,007
    Non-trainable params: 0
    _________________________________________________________________

# 3. 获取中间层的输出 #

1.  模型构建完成后，需要先训练模型，这样才能得到各层的权重：

model.fit(x_train, y_train,batch_size=64,epochs=2,verbose=1)

1.  模型训练完成后，指定好要输出的中间层名字，并建立从输入到输出的`函数对象`：

import keras.backend as k
    
    layer_name = 'layer_conv_3'
    layer_output = model.get_layer(layer_name).output # get output by layer name
    layer_input = model.input
    output_func = k.function([layer_input], [layer_output]) # construct function

这里设置layer\_name为layer\_conv\_3，表示我们想获取name为layer\_conv\_3的这一层的输出值。

1.  获取输入输出的数据维度

（1）原始输入数据，有100个样本，每个样本都是三维的。x\_train.shape = (100, 15, 20, 3)

x_preproc = x_train

（2）中间层layer\_conv\_3的输出数据维度

output_shape = output_func([x_preproc[0][None, ...]])[0].shape
    print(output_shape)# (1, 7, 18, 128)

获取到的中间层的输出维度，是(1, 7, 18, 128)。从第2部分中模型参数输出中可知，这个数据正好就是和(None, 7, 18, 128) 匹配的，只是这里能用代码动态获取到这个值而已。

（3）输入100个样本，则中间层layer\_conv\_3对应这100个样本的输出数据维度：

activations = np.zeros((x_preproc.shape[0],) + output_shape[1:], dtype=float32)
    print(activations.shape) # (100, 7, 18, 128)

最终，activations是一个全零的array，这就是存储中间层最终输出值的地方。

1.  把x\_train送入模型，获取最终中间层的输出值

batch_size = 8
    for batch_index in range(int(np.ceil(x_preproc.shape[0] / float(batch_size)))):
        begin, end = batch_index * batch_size, min((batch_index + 1) * batch_size, x_preproc.shape[0])
        activations[begin:end] = output_func([x_preproc[begin:end]])[0]

这里按照各个batch\_size来迭代，依次把x\_train的一部分送入`函数对象`，获取这个部分对应的中间层输出，多次迭代后就能得到完整的中间层输出的数据了。

1.  最终得到的中间层输出数据

最终得到的中间层输出数据存储在activations中，它的shape为(100, 7, 18, 128)，数据示意如下：

array([[[[0.26599178, 0.73428845, 0.16254057, ..., 0.06111438,
              0.22640981, 0.35272944],
             [0.283226  , 0.6876849 , 0.03790125, ..., 0.34651148,
              0.10112678, 0.29799798],
             [0.54103273, 0.9894899 , 0.10496318, ..., 0.7219487 ,
              0.06900553, 0.38379622],
             ...,
             [0.277767  , 0.47766227, 0.19746281, ..., 0.91875714,
              0.028616  , 0.41216236],
             [0.17200978, 0.47316927, 0.12632905, ..., 0.82960546,
              0.12838002, 0.15124908],
             [0.2677489 , 0.29046324, 0.16919036, ..., 0.86634046,
              0.14427625, 0.09604399]],
             ...,
             [0.17622252, 0.2056456 , 0.13514128, ..., 0.52697086,
              0.05512847, 0.45330787],
             [0.13311246, 0.13437134, 0.17722939, ..., 0.41641268,
              0.        , 0.35246032],
             [0.03272216, 0.07479057, 0.04990054, ..., 0.29089817,
              0.0585143 , 0.17479342]]]], dtype=float32)

# 4. 总结 #

本文讲解了获取keras模型的中间层输出值的具体步骤，这是参考了文献1中`get_activations`函数写出来的，完整的函数请参考文献1。

# 5. 参考 #

1.  https://stackoverflow.com/questions/41711190/keras-how-to-get-the-output-of-each-layer