本文整理匯總了Python中keras.applications.DenseNet169方法的典型用法代碼示例。如果您正苦於以下問題：Python applications.DenseNet169方法的具體用法？Python applications.DenseNet169怎麽用？Python applications.DenseNet169使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.applications的用法示例。
在下文中一共展示了applications.DenseNet169方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: densenet_fpn
# 需要導入模塊: from keras import applications [as 別名]
# 或者: from keras.applications import DenseNet169 [as 別名]
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 示例2: get_imagenet_architecture
# 需要導入模塊: from keras import applications [as 別名]
# 或者: from keras.applications import DenseNet169 [as 別名]
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