180529 Vgg16的Keras模型结构参数理解

• 模型定义

def FCN_Vgg16_32s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21):
    if batch_shape:
        img_input = Input(batch_shape=batch_shape)
        image_size = batch_shape[1:3]
    else:
        img_input = Input(shape=input_shape)
        image_size = input_shape[0:2]
    # Block 1
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', kernel_regularizer=l2(weight_decay))(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer=l2(weight_decay))(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer=l2(weight_decay))(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer=l2(weight_decay))(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer=l2(weight_decay))(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer=l2(weight_decay))(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer=l2(weight_decay))(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    # Convolutional layers transfered from fully-connected layers
    x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1', kernel_regularizer=l2(weight_decay))(x)
    x = Dropout(0.5)(x)
    x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(x)
    x = Dropout(0.5)(x)
    #classifying layer
    x = Conv2D(classes, (1, 1), kernel_initializer='he_normal', activation='linear', padding='valid', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x)

    x = BilinearUpSampling2D(size=(32, 32))(x)

    model = Model(img_input, x)

#    weights_path = os.path.expanduser(os.path.join('~', '.keras/models/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5'))
#    model.load_weights(weights_path, by_name=True)
    return model

• 模型调用

model=FCN_VGG16_32s()

• 模型总结

print(model.summary())

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 224, 224, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
fc1 (Conv2D)                 (None, 7, 7, 4096)        102764544 
_________________________________________________________________
dropout_3 (Dropout)          (None, 7, 7, 4096)        0         
_________________________________________________________________
fc2 (Conv2D)                 (None, 7, 7, 4096)        16781312  
_________________________________________________________________
dropout_4 (Dropout)          (None, 7, 7, 4096)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 7, 7, 21)          86037     
_________________________________________________________________
bilinear_up_sampling2d_2 (Bi (None, 224, 224, 21)      0         
=================================================================
Total params: 134,346,581
Trainable params: 134,346,581
Non-trainable params: 0
_________________________________________________________________
None