TensorFlow之keras.layers.Conv2D( )

謙曰盛發表於2021-06-07
Keras

  keras.layers.Conv2D( ) 函式引數

    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):

引數：

filters 卷積核個數的變化，filters 影響的是最後輸入結果的的第三個維度的變化，例如，輸入的維度是 (600, 600, 3), filters 的個數是 64，轉變後的維度是 (600, 600, 64)

>>> from keras.layers import (Input, Reshape)
>>> input = Input(shape=(600, 600, 3))
>>> x = Conv2D(64, (1, 1), strides=(1, 1), name='conv1')(input)
>>> x
<tf.Tensor 'conv1_1/BiasAdd:0' shape=(?, 600, 600, 64) dtype=float32>

kernel_size 引數 表示卷積核的大小，可以直接寫一個數，影響的是輸出結果前兩個資料的維度，例如，（600, 600, 3）=> (599, 599, 64)

>>> from keras.layers import (Input, Conv2D)
>>> input = Input(shape=(600, 600, 3))
>>> Conv2D(64, (2, 2), strides=(1, 1), name='conv1')(input)
<tf.Tensor 'conv1/BiasAdd:0' shape=(?, 599, 599, 64) dtype=float32>

直接寫 2 也是可以的

>>> from keras.layers import (Input, Conv2D)
>>> input = Input(shape=(600, 600, 3))
>>> Conv2D(64, 2, strides=(1, 1), name='conv1')(input)
<tf.Tensor 'conv1_2/BiasAdd:0' shape=(?, 599, 599, 64) dtype=float32>

strides  步長 同樣會影響輸出的前兩個維度，例如，(600, 600, 3）=> (300, 300, 64)，值得注意的是，括號裡的資料可以不一致，分別控制橫座標和縱座標，這裡步長的計算公式為：

>>> from keras.layers import (Input, Conv2D)
>>> input = Input(shape=(600, 600, 3))
>>> Conv2D(64, 1, strides=(2, 2), name='conv1')(input)
<tf.Tensor 'conv1_4/BiasAdd:0' shape=(?, 300, 300, 64) dtype=float32>

padding 是否對周圍進行填充，“same” 即使通過kernel_size 縮小了維度，但是四周會填充 0，保持原先的維度；“valid”表示儲存不為0的有效資訊。多個對比效果如下：

>>> Conv2D(64, 1, strides=(2, 2), padding="same", name='conv1')(input)
<tf.Tensor 'conv1_6/BiasAdd:0' shape=(?, 300, 300, 64) dtype=float32>
>>> Conv2D(64, 3, strides=(2, 2), padding="same", name='conv1')(input)
<tf.Tensor 'conv1_7/BiasAdd:0' shape=(?, 300, 300, 64) dtype=float32>
>>> Conv2D(64, 3, strides=(1, 1), padding="same", name='conv1')(input)
<tf.Tensor 'conv1_8/BiasAdd:0' shape=(?, 600, 600, 64) dtype=float32>
>>> Conv2D(64, 3, strides=(1, 1), padding="valid", name='conv1')(input)
<tf.Tensor 'conv1_9/BiasAdd:0' shape=(?, 598, 598, 64) dtype=float32>

通過這種最簡單的方式，可以觀察 ResNet50 的組成結構

 Conv Block 的架構：

def conv_block(input_tensor, kernel_size, filters, stage, block, strides):

    filters1, filters2, filters3 = filters  # filters1 64, filters3 256  將數值傳入到filters。。。中
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
    x = BatchNormalization(name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(name=bn_name_base + '2c')(x)

    shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor)
    shortcut = BatchNormalization(name=bn_name_base + '1')(shortcut)

    x = layers.add([x, shortcut])
    x = Activation("relu")(x)
    return x

Identity Block 的架構：

def identity_block(input_tensor, kernel_size, filters, stage, block):
    filters1, filters2, filters3 = filters

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(input_tensor)
    x = BatchNormalization(name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(input_tensor)
    x = BatchNormalization(name=bn_name_base + '2c')(x)

    x = layers.add([x, input_tensor])
    x = Activation('relu')(x)
    return x　　

附上理論連結 Resnet-50網路結構詳解  https://www.cnblogs.com/qianchaomoon/p/12315906.html

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