在TFLearn中处理图片分类任务通常需要以下步骤:
下面是一个简单的示例代码,演示如何使用TFLearn处理图片分类任务:
import tflearn
from tflearn.layers.core import input_data, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
from tflearn.datasets import cifar10
# 数据预处理
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# 数据增强
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
# 加载CIFAR-10数据集
X, Y, testX, testY = cifar10.load_data()
# 构建卷积神经网络模型
network = input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001)
# 训练模型
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=50, validation_set=(testX, testY), show_metric=True, batch_size=64, shuffle=True)
# 评估模型
score = model.evaluate(testX, testY)
print('Test accuracy: %0.4f' % score[0])
# 使用模型进行预测
pred = model.predict(testX)
这是一个简单的使用TFLearn处理图片分类任务的示例代码,你可以根据自己的需求和数据集进行调整和优化。
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