欧美bbbwbbbw肥妇,免费乱码人妻系列日韩,一级黄片

python神經網(wǎng)絡InceptionV3模型復現(xiàn)詳解

 更新時間：2022年05月06日 15:00:46   作者：Bubbliiiing  
這篇文章主要為大家介紹了python神經網(wǎng)絡InceptionV3模型復現(xiàn)詳解，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進步，早日升職加薪<BR>

學習前言

Inception系列的結構和其它的前向神經網(wǎng)絡的結構不太一樣，每一層的內容不是直直向下的，而是分了很多的塊

什么是InceptionV3模型

InceptionV3模型是谷歌Inception系列里面的第三代模型，其模型結構與InceptionV2模型放在了同一篇論文里，其實二者模型結構差距不大，相比于其它神經網(wǎng)絡模型，Inception網(wǎng)絡最大的特

點在于將神經網(wǎng)絡層與層之間的卷積運算進行了拓展。

如VGG，AlexNet網(wǎng)絡，它就是一直卷積下來的，一層接著一層；

ResNet則是創(chuàng)新性的引入了殘差網(wǎng)絡的概念，使得靠前若干層的某一層數(shù)據(jù)輸出直接跳過多層引入到后面數(shù)據(jù)層的輸入部分，后面的特征層的內容會有一部分由其前面的某一層線性貢獻。

而Inception網(wǎng)絡則是采用不同大小的卷積核，使得存在不同大小的感受野，最后實現(xiàn)拼接達到不同尺度特征的融合。

對于InceptionV3而言，其網(wǎng)絡中存在著如下的結構。

這個結構使用不同大小的卷積核對輸入進行卷積（這個結構主要在代碼中的block1使用）。

還存在著這樣的結構，利用1x7的卷積和7x1的卷積代替7x7的卷積，這樣可以只使用約（1x7 + 7x1) / (7x7) = 28.6%的計算開銷；

利用1x3的卷積和3x1的卷積代替3x3的卷積，這樣可以只使用約（1x3 + 3x1) / (3x3) = 67%的計算開銷。

下圖利用1x7的卷積和7x1的卷積代替7x7的卷積（這個結構主要在代碼中的block2使用）。

下圖利用1x3的卷積和3x1的卷積代替3x3的卷積（這個結構主要在代碼中的block3使用）。

InceptionV3網(wǎng)絡部分實現(xiàn)代碼

我一共將InceptionV3劃分為3個block，對應著35x35、17x17，8x8維度大小的圖像。每個block中間有許多的part，對應著不同的特征層深度，用于特征提取。

#-------------------------------------------------------------#
#   InceptionV3的網(wǎng)絡部分
#-------------------------------------------------------------#
from __future__ import print_function
from __future__ import absolute_import
import warnings
import numpy as np
from keras.models import Model
from keras import layers
from keras.layers import Activation,Dense,Input,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D
from keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D
from keras.engine.topology import get_source_inputs
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
from keras import backend as K
from keras.applications.imagenet_utils import decode_predictions
from keras.preprocessing import image
def conv2d_bn(x,
              filters,
              num_row,
              num_col,
              padding='same',
              strides=(1, 1),
              name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None
    x = Conv2D(
        filters, (num_row, num_col),
        strides=strides,
        padding=padding,
        use_bias=False,
        name=conv_name)(x)
    x = BatchNormalization(scale=False, name=bn_name)(x)
    x = Activation('relu', name=name)(x)
    return x
def InceptionV3(input_shape=[299,299,3],
                classes=1000):
    img_input = Input(shape=input_shape)
    x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
    x = conv2d_bn(x, 32, 3, 3, padding='valid')
    x = conv2d_bn(x, 64, 3, 3)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = conv2d_bn(x, 80, 1, 1, padding='valid')
    x = conv2d_bn(x, 192, 3, 3, padding='valid')
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)
    #--------------------------------#
    #   Block1 35x35
    #--------------------------------#
    # Block1 part1
    # 35 x 35 x 192 -> 35 x 35 x 256
    branch1x1 = conv2d_bn(x, 64, 1, 1)
    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
        axis=3,
        name='mixed0')
    # Block1 part2
    # 35 x 35 x 256 -> 35 x 35 x 288
    branch1x1 = conv2d_bn(x, 64, 1, 1)
    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
        axis=3,
        name='mixed1')
    # Block1 part3
    # 35 x 35 x 288 -> 35 x 35 x 288
    branch1x1 = conv2d_bn(x, 64, 1, 1)
    branch5x5 = conv2d_bn(x, 48, 1, 1)
    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch5x5, branch3x3dbl, branch_pool],
        axis=3,
        name='mixed2')
    #--------------------------------#
    #   Block2 17x17
    #--------------------------------#
    # Block2 part1
    # 35 x 35 x 288 -> 17 x 17 x 768
    branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')
    branch3x3dbl = conv2d_bn(x, 64, 1, 1)
    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
    branch3x3dbl = conv2d_bn(
        branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')
    branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = layers.concatenate(
        [branch3x3, branch3x3dbl, branch_pool], axis=3, name='mixed3')
    # Block2 part2
    # 17 x 17 x 768 -> 17 x 17 x 768
    branch1x1 = conv2d_bn(x, 192, 1, 1)
    branch7x7 = conv2d_bn(x, 128, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
    branch7x7dbl = conv2d_bn(x, 128, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
        axis=3,
        name='mixed4')
    # Block2 part3 and part4
    # 17 x 17 x 768 -> 17 x 17 x 768 -> 17 x 17 x 768
    for i in range(2):
        branch1x1 = conv2d_bn(x, 192, 1, 1)
        branch7x7 = conv2d_bn(x, 160, 1, 1)
        branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
        branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
        branch7x7dbl = conv2d_bn(x, 160, 1, 1)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
        branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
        branch_pool = AveragePooling2D(
            (3, 3), strides=(1, 1), padding='same')(x)
        branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
        x = layers.concatenate(
            [branch1x1, branch7x7, branch7x7dbl, branch_pool],
            axis=3,
            name='mixed' + str(5 + i))
    # Block2 part5
    # 17 x 17 x 768 -> 17 x 17 x 768
    branch1x1 = conv2d_bn(x, 192, 1, 1)
    branch7x7 = conv2d_bn(x, 192, 1, 1)
    branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
    branch7x7dbl = conv2d_bn(x, 192, 1, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
    branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
    x = layers.concatenate(
        [branch1x1, branch7x7, branch7x7dbl, branch_pool],
        axis=3,
        name='mixed7')
    #--------------------------------#
    #   Block3 8x8
    #--------------------------------#
    # Block3 part1
    # 17 x 17 x 768 -> 8 x 8 x 1280
    branch3x3 = conv2d_bn(x, 192, 1, 1)
    branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
                          strides=(2, 2), padding='valid')
    branch7x7x3 = conv2d_bn(x, 192, 1, 1)
    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
    branch7x7x3 = conv2d_bn(
        branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')
    branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
    x = layers.concatenate(
        [branch3x3, branch7x7x3, branch_pool], axis=3, name='mixed8')
    # Block3 part2 part3
    # 8 x 8 x 1280 -> 8 x 8 x 2048 -> 8 x 8 x 2048
    for i in range(2):
        branch1x1 = conv2d_bn(x, 320, 1, 1)
        branch3x3 = conv2d_bn(x, 384, 1, 1)
        branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
        branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
        branch3x3 = layers.concatenate(
            [branch3x3_1, branch3x3_2], axis=3, name='mixed9_' + str(i))
        branch3x3dbl = conv2d_bn(x, 448, 1, 1)
        branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
        branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
        branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
        branch3x3dbl = layers.concatenate(
            [branch3x3dbl_1, branch3x3dbl_2], axis=3)
        branch_pool = AveragePooling2D(
            (3, 3), strides=(1, 1), padding='same')(x)
        branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
        x = layers.concatenate(
            [branch1x1, branch3x3, branch3x3dbl, branch_pool],
            axis=3,
            name='mixed' + str(9 + i))
    # 平均池化后全連接。
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)
    inputs = img_input
    model = Model(inputs, x, name='inception_v3')
    return model

圖片預測

建立網(wǎng)絡后，可以用以下的代碼進行預測。

def preprocess_input(x):
    x /= 255.
    x -= 0.5
    x *= 2.
    return x
if __name__ == '__main__':
    model = InceptionV3()
    model.load_weights("inception_v3_weights_tf_dim_ordering_tf_kernels.h5")
    img_path = 'elephant.jpg'
    img = image.load_img(img_path, target_size=(299, 299))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    preds = model.predict(x)
    print('Predicted:', decode_predictions(preds))

預測所需的已經訓練好的InceptionV3模型可以在github下載非常方便。

預測結果為：

Predicted: [[('n02504458', 'African_elephant', 0.50874853), ('n01871265', 'tusker', 0.19524273), ('n02504013', 'Indian_elephant', 0.1566972), ('n01917289', 'brain_coral', 0.0008956835), ('n01695060', 'Komodo_dragon', 0.0008260256)]]

這里我推薦一個很不錯的講InceptionV3結構的深度神經網(wǎng)絡Google Inception Net-V3結構圖里面有每一層的結構圖，非常清晰。

以上就是python神經網(wǎng)絡InceptionV3模型復現(xiàn)詳解的詳細內容，更多關于python神經網(wǎng)絡InceptionV3模型的資料請關注腳本之家其它相關文章！

相關文章

最新評論