import os import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from keras.layers.core import Dropout

定义训练集和测试集路径

train_dir = 'C:/Users/28938/Desktop/shengdu/Data/train' test_dir = 'C:/Users/28938/Desktop/shengdu/Data/test'

定义类别标签

class_names = ['MEN_Coats', 'MEN_Hood', 'MEN_Suits', 'WOMEN_Dress', 'WOMEN_Hood']

定义图像尺寸和批次大小

img_height = 224 img_width = 224 batch_size = 32

从目录中读取训练集和测试集

train_ds = keras.preprocessing.image_dataset_from_directory( train_dir, validation_split=0.2, subset='training', seed=42, image_size=(img_height, img_width), batch_size=batch_size ) val_ds = keras.preprocessing.image_dataset_from_directory( train_dir, validation_split=0.2, subset='validation', seed=42, image_size=(img_height, img_width), batch_size=batch_size ) test_ds = keras.preprocessing.image_dataset_from_directory( test_dir, seed=42, image_size=(img_height, img_width), batch_size=batch_size )

定义函数以展示图像

def plotImages(img_arr): fig, axes = plt.subplots(1, 5, figsize=(20,20)) axes = axes.flatten() for img, ax in zip( img_arr, axes): ax.imshow(img) ax.axis('off') plt.tight_layout() plt.show()

定义数据增强器

data_augmentation = keras.Sequential( [ layers.experimental.preprocessing.RandomFlip('horizontal', input_shape=(img_height, img_width, 3)), layers.experimental.preprocessing.RandomRotation(0.1), layers.experimental.preprocessing.RandomZoom(0.1), ] )

定义模型输入

input_shape = (img_height, img_width, 3)

建立模型

model = keras.Sequential([ data_augmentation, layers.experimental.preprocessing.Rescaling(1./255), layers.Conv2D(32, 3, padding='same', activation='relu', input_shape=input_shape), layers.MaxPooling2D(), layers.Dropout(0.2), layers.Conv2D(64, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Dropout(0.2), layers.Conv2D(128, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Dropout(0.2), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(len(class_names)) ])

编译模型

model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])

打印模型结构

model.summary()

显示前5张图像

plt.figure(figsize=(10, 10)) for images, labels in train_ds.take(1): for i in range(5): ax = plt.subplot(5, 5, i + 1) plt.imshow(images[i].numpy().astype('uint8')) plt.title(class_names[labels[i]]) plt.axis('off')

设定训练参数

epochs = 15

开始训练模型

#history = model.fit(train_ds, validation_data=val_ds, epochs=epochs) batch_size = 16 history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size)

对模型进行评估

test_loss, test_acc = model.evaluate(test_ds) print('Test accuracy:', test_acc)

对模型进行预测

predictions = model.predict(test_ds)

绘制模型准确度和损失随时间变化的曲线

acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) plt.figure(figsize=(8, 8)) plt.subplot(2, 1, 1) plt.plot(epochs_range, acc, label='Training Accuracy') plt.plot(epochs_range, val_acc, label='Validation Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.subplot(2, 1, 2) plt.plot(epochs_range, loss, label='Training Loss') plt.plot(epochs_range, val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show()