inception_resnet_v2.py 文件源码

def create_inception_resnet_v2(nb_classes=1001, scale=True):
    '''
    Creates a inception resnet v2 network

    :param nb_classes: number of classes.txt
    :param scale: flag to add scaling of activations
    :return: Keras Model with 1 input (299x299x3) input shape and 2 outputs (final_output, auxiliary_output)
    '''

    if K.image_dim_ordering() == 'th':
        init = Input((3, 299, 299))
    else:
        init = Input((299, 299, 3))

    # Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
    x = inception_resnet_stem(init)

    # 10 x Inception Resnet A
    for i in range(10):
        x = inception_resnet_v2_A(x, scale_residual=scale)

    # Reduction A
    x = reduction_A(x, k=256, l=256, m=384, n=384)

    # 20 x Inception Resnet B
    for i in range(20):
        x = inception_resnet_v2_B(x, scale_residual=scale)

    # Auxiliary tower
    aux_out = AveragePooling2D((5, 5), strides=(3, 3))(x)
    aux_out = Convolution2D(128, 1, 1, border_mode='same', activation='relu')(aux_out)
    aux_out = Convolution2D(768, 5, 5, activation='relu')(aux_out)
    aux_out = Flatten()(aux_out)
    aux_out = Dense(nb_classes, activation='softmax')(aux_out)

    # Reduction Resnet B
    x = reduction_resnet_v2_B(x)

    # 10 x Inception Resnet C
    for i in range(10):
        x = inception_resnet_v2_C(x, scale_residual=scale)

    # Average Pooling
    x = AveragePooling2D((8,8))(x)

    # Dropout
    x = Dropout(0.8)(x)
    x = Flatten()(x)

    # Output
    out = Dense(output_dim=nb_classes, activation='softmax')(x)

    model = Model(init, output=[out, aux_out], name='Inception-Resnet-v2')
    return model