Python keras.applications.DenseNet169() Examples

The following are 2 code examples of keras.applications.DenseNet169(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.applications , or try the search function .
Example #1
Source Project: dsb2018_topcoders   Author: selimsef   File: unets.py    License: MIT License 5 votes vote down vote up
def densenet_fpn(input_shape, channels=1, activation="sigmoid"):
    densenet = DenseNet169(input_shape=input_shape, include_top=False)
    conv1 = densenet.get_layer("conv1/relu").output
    conv2 = densenet.get_layer("pool2_relu").output
    conv3 = densenet.get_layer("pool3_relu").output
    conv4 = densenet.get_layer("pool4_relu").output
    conv5 = densenet.get_layer("bn").output
    conv5 = Activation("relu", name="conv5_relu")(conv5)

    P1, P2, P3, P4, P5 = create_pyramid_features(conv1, conv2, conv3, conv4, conv5)
    x = concatenate(
        [
            prediction_fpn_block(P5, "P5", (8, 8)),
            prediction_fpn_block(P4, "P4", (4, 4)),
            prediction_fpn_block(P3, "P3", (2, 2)),
            prediction_fpn_block(P2, "P2"),
        ]
    )
    x = conv_bn_relu(x, 256, 3, (1, 1), name="aggregation")
    x = decoder_block_no_bn(x, 128, conv1, 'up4')
    x = UpSampling2D()(x)
    x = conv_relu(x, 64, 3, (1, 1), name="up5_conv1")
    x = conv_relu(x, 64, 3, (1, 1), name="up5_conv2")
    if activation == 'softmax':
        name = 'mask_softmax'
        x = Conv2D(channels, (1, 1), activation=activation, name=name)(x)
    else:
        x = Conv2D(channels, (1, 1), activation=activation, name="mask")(x)
    model = Model(densenet.input, x)
    return model 
Example #2
Source Project: vergeml   Author: mme   File: features.py    License: MIT License 4 votes vote down vote up
def get_imagenet_architecture(architecture, variant, size, alpha, output_layer, include_top=False, weights='imagenet'):
    from keras import applications, Model

    if include_top:
        assert output_layer == 'last'

    if size == 'auto':
        size = get_image_size(architecture, variant, size)

    shape = (size, size, 3)

    if architecture == 'densenet':
        if variant == 'auto':
            variant = 'densenet-121'
        if variant == 'densenet-121':
            model = applications.DenseNet121(weights=weights, include_top=include_top, input_shape=shape)
        elif variant == 'densenet-169':
            model = applications.DenseNet169(weights=weights, include_top=include_top, input_shape=shape)
        elif variant == 'densenet-201':
            model = applications.DenseNet201(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'inception-resnet-v2':
        model = applications.InceptionResNetV2(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'mobilenet':
        model = applications.MobileNet(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
    elif architecture == 'mobilenet-v2':
        model = applications.MobileNetV2(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
    elif architecture == 'nasnet':
        if variant == 'auto':
            variant = 'large'
        if variant == 'large':
            model = applications.NASNetLarge(weights=weights, include_top=include_top, input_shape=shape)
        else:
            model = applications.NASNetMobile(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'resnet-50':
        model = applications.ResNet50(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'vgg-16':
        model = applications.VGG16(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'vgg-19':
        model = applications.VGG19(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'xception':
        model = applications.Xception(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'inception-v3':
        model = applications.InceptionV3(weights=weights, include_top=include_top, input_shape=shape)

    if output_layer != 'last':
        try:
            if isinstance(output_layer, int):
                layer = model.layers[output_layer]
            else:
                layer = model.get_layer(output_layer)
        except Exception:
            raise VergeMLError('layer not found: {}'.format(output_layer))
        model = Model(inputs=model.input, outputs=layer.output)

    return model