python教程分享python神经网络ResNet50模型的复现详解

#-------------------------------------------------------------#  #   resnet50的网络部分  #-------------------------------------------------------------#  from __future__ import print_function    import numpy as np  from keras import layers    from keras.layers import input  from keras.layers import dense,conv2d,maxpooling2d,zeropadding2d,averagepooling2d  from keras.layers import activation,batchnormalization,flatten  from keras.models import model    from keras.preprocessing import image  import keras.backend as k  from keras.utils.data_utils import get_file  from keras.applications.imagenet_utils import decode_predictions  from keras.applications.imagenet_utils import preprocess_input      def identity_block(input_tensor, kernel_size, filters, stage, block):        filters1, filters2, filters3 = filters        conv_name_base = 'res' + str(stage) + block + '_branch'      bn_name_base = 'bn' + str(stage) + block + '_branch'        x = conv2d(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)      x = batchnormalization(name=bn_name_base + '2a')(x)      x = activation('relu')(x)        x = conv2d(filters2, kernel_size,padding='same', name=conv_name_base + '2b')(x)        x = batchnormalization(name=bn_name_base + '2b')(x)      x = activation('relu')(x)        x = conv2d(filters3, (1, 1), name=conv_name_base + '2c')(x)      x = batchnormalization(name=bn_name_base + '2c')(x)        x = layers.add([x, input_tensor])      x = activation('relu')(x)      return x      def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):        filters1, filters2, filters3 = filters        conv_name_base = 'res' + str(stage) + block + '_branch'      bn_name_base = 'bn' + str(stage) + block + '_branch'        x = conv2d(filters1, (1, 1), strides=strides,                 name=conv_name_base + '2a')(input_tensor)      x = batchnormalization(name=bn_name_base + '2a')(x)      x = activation('relu')(x)        x = conv2d(filters2, kernel_size, padding='same',                 name=conv_name_base + '2b')(x)      x = batchnormalization(name=bn_name_base + '2b')(x)      x = activation('relu')(x)        x = conv2d(filters3, (1, 1), name=conv_name_base + '2c')(x)      x = batchnormalization(name=bn_name_base + '2c')(x)        shortcut = conv2d(filters3, (1, 1), strides=strides,                        name=conv_name_base + '1')(input_tensor)      shortcut = batchnormalization(name=bn_name_base + '1')(shortcut)        x = layers.add([x, shortcut])      x = activation('relu')(x)      return x      def resnet50(input_shape=[224,224,3],classes=1000):        img_input = input(shape=input_shape)      x = zeropadding2d((3, 3))(img_input)        x = conv2d(64, (7, 7), strides=(2, 2), name='conv1')(x)      x = batchnormalization(name='bn_conv1')(x)      x = activation('relu')(x)      x = maxpooling2d((3, 3), strides=(2, 2))(x)        x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))      x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')      x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')        x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')      x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')      x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')      x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')        x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')      x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')      x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')      x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')      x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')      x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')      x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')      x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')        x = averagepooling2d((7, 7), name='avg_pool')(x)        x = flatten()(x)      x = dense(classes, activation='softmax', name='fc1000')(x)        model = model(img_input, x, name='resnet50')        model.load_weights("resnet50_weights_tf_dim_ordering_tf_kernels.h5")        return model  

if __name__ == '__main__':      model = resnet50()      model.summary()      img_path = 'elephant.jpg'      img = image.load_img(img_path, target_size=(224, 224))      x = image.img_to_array(img)      x = np.expand_dims(x, axis=0)      x = preprocess_input(x)        print('input image shape:', x.shape)      preds = model.predict(x)      print('predicted:', decode_predictions(preds))  

predicted: [[('n01871265', 'tusker', 0.41107917), ('n02504458', 'african_elephant', 0.39015812), ('n02504013', 'indian_elephant', 0.12260196), ('n03000247', 'chain_mail', 0.023176488), ('n02437312', 'arabian_camel', 0.020982226)]]