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 File: unets.py    From dsb2018_topcoders with 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 File: features.py    From vergeml with 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