深度学习——DenseNet121

     以DenseNet121为例，具体看以下代码

def dense_block(x, blocks, name):
    for i in range(blocks):
        x = conv_block(x, 32, name=name + '_block' + str(i + 1))
    return x


def conv_block(x, growth_rate, name):
    bn_axis = 3
    x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x)
    x1 = Activation('relu', name=name + '_0_relu')(x1)

    x1 = Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1)
    x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1)
    x1 = Activation('relu', name=name + '_1_relu')(x1)

    x1 = Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1)
    x = Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
    return x


def transition_block(x, reduction, name):
    bn_axis = 3
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x)
    x = Activation('relu', name=name + '_relu')(x)

    x = Conv2D(int(K.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x)
    x = AveragePooling2D(2, strides=2, name=name + '_pool')(x)
    return x


def DenseNet121(input_shape=(224, 224, 3), classes=1000):
    blocks = [6, 12, 24, 16]
    img_input = Input(shape=input_shape)

    bn_axis = 3

    x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)
    x = Activation('relu', name='conv1/relu')(x)
    x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = MaxPooling2D(3, strides=2, name='pool1')(x)

    x = dense_block(x, blocks[0], name='conv2')
    x = transition_block(x, 0.5, name='pool2')

    x = dense_block(x, blocks[1], name='conv3')
    x = transition_block(x, 0.5, name='pool3')

    x = dense_block(x, blocks[2], name='conv4')
    x = transition_block(x, 0.5, name='pool4')
    
    x = dense_block(x, blocks[3], name='conv5')

    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)

    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='fc1000')(x)

    model = Model(img_input, x, name='densenet121')

    return model