从keras的keras_applications的文件夹内可以找到内置模型的源代码
Kera的应用模块Application提供了带有预训练权重的Keras模型，这些模型可以用来进行预测、特征提取和finetune
应用于图像分类的模型,权重训练自ImageNet： Xception VGG16 VGG19 ResNet50 InceptionV3InceptionResNetV2 * MobileNet densenet
densenet的keras源代码如下：
"""DenseNet models for Keras. # Reference paper - [Densely Connected Convolutional Networks] (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award) # Reference implementation - [Torch DenseNets] (https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua) - [TensorNets] (https://github.com/taehoonlee/tensornets/blob/master/tensornets/densenets.py) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from . import get_keras_submodule backend = get_keras_submodule('backend') engine = get_keras_submodule('engine') layers = get_keras_submodule('layers') models = get_keras_submodule('models') keras_utils = get_keras_submodule('utils') from . import imagenet_utils from .imagenet_utils import decode_predictions from .imagenet_utils import _obtain_input_shape BASE_WEIGTHS_PATH = ( 'https://github.com/fchollet/deep-learning-models/' 'releases/download/v0.8/') DENSENET121_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5') DENSENET121_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET169_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5') DENSENET169_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET201_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5') DENSENET201_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5') def dense_block(x, blocks, name): """A dense block. # Arguments x: input tensor. blocks: integer, the number of building blocks. name: string, block label. # Returns output tensor for the block. """ for i in range(blocks): x = conv_block(x, 32, name=name + '_block' + str(i + 1)) return x def transition_block(x, reduction, name): """A transition block. # Arguments x: input tensor. reduction: float, compression rate at transition layers. name: string, block label. # Returns output tensor for the block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x) return x def conv_block(x, growth_rate, name): """A building block for a dense block. # Arguments x: input tensor. growth_rate: float, growth rate at dense layers. name: string, block label. # Returns Output tensor for the block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x def DenseNet(blocks, include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the DenseNet architecture. Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at `~/.keras/keras.json`. # Arguments blocks: numbers of building blocks for the four dense layers. include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). It should have exactly 3 inputs channels. pooling: optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. # Returns A Keras model instance. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. """ if not (weights in {'imagenet', None} or os.path.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as imagenet with `include_top`' ' as true, `classes` should be 1000') # Determine proper input shape input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=221, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x) x = layers.Activation('relu', name='conv1/relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x) x = layers.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 = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='bn')(x) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='fc1000')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D(name='avg_pool')(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D(name='max_pool')(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = engine.get_source_inputs(input_tensor) else: inputs = img_input # Create model. if blocks == [6, 12, 24, 16]: model = models.Model(inputs, x, name='densenet121') elif blocks == [6, 12, 32, 32]: model = models.Model(inputs, x, name='densenet169') elif blocks == [6, 12, 48, 32]: model = models.Model(inputs, x, name='densenet201') else: model = models.Model(inputs, x, name='densenet') # Load weights. if weights == 'imagenet': if include_top: if blocks == [6, 12, 24, 16]: weights_path = keras_utils.get_file( 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', DENSENET121_WEIGHT_PATH, cache_subdir='models', file_hash='0962ca643bae20f9b6771cb844dca3b0') elif blocks == [6, 12, 32, 32]: weights_path = keras_utils.get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', DENSENET169_WEIGHT_PATH, cache_subdir='models', file_hash='bcf9965cf5064a5f9eb6d7dc69386f43') elif blocks == [6, 12, 48, 32]: weights_path = keras_utils.get_file( 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', DENSENET201_WEIGHT_PATH, cache_subdir='models', file_hash='7bb75edd58cb43163be7e0005fbe95ef') else: if blocks == [6, 12, 24, 16]: weights_path = keras_utils.get_file( 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET121_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3') elif blocks == [6, 12, 32, 32]: weights_path = keras_utils.get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET169_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='50662582284e4cf834ce40ab4dfa58c6') elif blocks == [6, 12, 48, 32]: weights_path = keras_utils.get_file( 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET201_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='1c2de60ee40562448dbac34a0737e798') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model def DenseNet121(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor, input_shape, pooling, classes) def DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor, input_shape, pooling, classes) def DenseNet201(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor, input_shape, pooling, classes) def preprocess_input(x, data_format=None): """Preprocesses a numpy array encoding a batch of images. # Arguments x: a 3D or 4D numpy array consists of RGB values within [0, 255]. data_format: data format of the image tensor. # Returns Preprocessed array. """ return imagenet_utils.preprocess_input(x, data_format, mode='torch') setattr(DenseNet121, '__doc__', DenseNet.__doc__) setattr(DenseNet169, '__doc__', DenseNet.__doc__) setattr(DenseNet201, '__doc__', DenseNet.__doc__)
从keras导入densenet模型需要以下代码
from keras.applications.densenet import DenseNet201,preprocess_input #base_model = DenseNet(weights='imagenet', include_top=False) base_model = DenseNet201(weights=None, include_top=False) x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(6941, activation='sigmoid')(x) model = Model(inputs=base_model.input, outputs=predictions) model.summary()
本站文章如无特殊说明，均为本站原创，如若转载，请注明出处：使用keras导入densenet模型 - Python技术站
微信扫一扫 
支付宝扫一扫 
