tensorflowkeras使用xception进行图像分类并添加注意力机制_用tensorflow框架构建Xception模型

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tensorflow keras使用使用xception进行图像分类并添加注意力机进行图像分类并添加注意力机

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import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

import os

import pandas as pd

import sklearn

import sys

import tensorflow as tf

import time

from tensorflow import keras

from tensorflow.keras import layers

from tensorflow.keras.models import Model

from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add,

Activation, Lambda

print(tf.__version__)

print(sys.version_info)

for module in mpl, np, pd, sklearn, tf, keras:

print(module.__name__, module.__version__)

# %%

train_dir = "./10monkeys/training/training"

valid_dir = "./10monkeys/validation/validation"

label_file = "./10monkeys/monkey_labels.txt"

print(os.path.exists(train_dir))

print(os.path.exists(valid_dir))

print(os.path.exists(label_file))

print(os.listdir(train_dir))

print(os.listdir(valid_dir))

labels = pd.read_csv(label_file, header=0)

print(labels)

height = 224

width = 224

channels = 3

batch_size = 24

num_classes = 10

train_datagen = keras.preprocessing.image.ImageDataGenerator(

preprocessing_function=keras.applications.resnet50.preprocess_input,

rotation_range=40,

width_shift_range=0.2,

height_shift_range=0.2,

shear_range=0.2,

zoom_range=0.2,

horizontal_flip=True,

fill_mode='nearest',

)

train_generator = train_datagen.flow_from_directory(train_dir,

target_size=(height, width),

batch_size=batch_size,

seed=7,

shuffle=True,

class_mode="categorical")

valid_datagen = keras.preprocessing.image.ImageDataGenerator(

preprocessing_function=keras.applications.resnet50.preprocess_input)

valid_generator = valid_datagen.flow_from_directory(valid_dir,

target_size=(height, width),

batch_size=batch_size,

seed=7,

shuffle=False,

class_mode="categorical")

train_num = train_generator.samples

valid_num = valid_generator.samples

print(train_num, valid_num)

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