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91 lines
2.9 KiB
91 lines
2.9 KiB
5 months ago
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import os
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import numpy as np
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from PIL import Image
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import tensorflow as tf
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from tensorflow.keras import layers
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, RandomFlip, RandomRotation, RandomZoom, Attention
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from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, Callback
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from sklearn.utils import class_weight
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# 数据路径
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data_dir = 'dataset/flowers' # 数据集根目录
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batch_size = 32
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# 图片生成器，用于从文件夹加载图片数据
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datagen = ImageDataGenerator(
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rescale=1./255,
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validation_split=0.2,
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rotation_range=10,
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width_shift_range=0.2,
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height_shift_range=0.2,
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shear_range=0.15,
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zoom_range=0.2,
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horizontal_flip=True,
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)
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generator = datagen.flow_from_directory(
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data_dir,
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target_size=(180, 180),
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batch_size=batch_size,
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class_mode='categorical',
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subset='training'
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)
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validation_generator = datagen.flow_from_directory(
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data_dir,
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target_size=(180, 180),
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batch_size=batch_size,
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class_mode='categorical',
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subset='validation'
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)
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# 计算样本权重
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class_weights = class_weight.compute_sample_weight('balanced', generator.classes)
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# 配置早停
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early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)
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# # 数据增强
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# data_augmentation = tf.keras.Sequential(
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# [
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# RandomFlip("horizontal", input_shape=(180, 180, 3)),
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# RandomRotation(0.1),
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# RandomZoom(0.1),
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# ]
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# )
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# 配置GPU加速
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strategy = tf.distribute.MirroredStrategy()
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print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
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with strategy.scope():
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model = tf.keras.Sequential([
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# data_augmentation,
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Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(180, 180, 3)),
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MaxPooling2D(),
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Conv2D(64, (3, 3), padding='same', activation='relu'),
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MaxPooling2D(),
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Conv2D(128, (3, 3), padding='same', activation='relu'),
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MaxPooling2D(),
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Flatten(),
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Dense(128, activation='relu'),
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Dense(len(generator.class_indices), activation='softmax')
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])
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# 编译模型
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optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
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model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
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# # 配置模型检查点，保存最优模型
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# checkpoint_path = "./model/animal_model.h5"
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# model_checkpoint = ModelCheckpoint(checkpoint_path, monitor='val_loss',
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# save_best_only=True, save_weights_only=False, verbose=1)
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# 训练模型
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model.fit(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stopping])
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# 保存模型为 .h5 文件
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model.save("./model/flower_model.h5", save_format='h5')
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