文章作者：Tyan 博客：noahsnail.com | | 简书
本文主要介绍Keras的一些基本用法，主要涉及已有网络的fine tuning，以ResNet50为例。
Demo #!/usr/bin/env python # _*_ coding: utf-8 _*_ from keras.models import Model from keras.layers import Dense from keras.applications.resnet50 import ResNet50 from keras.preprocessing.image import ImageDataGenerator # 训练的batch_size batch_size = 16 # 训练的epoch epochs = 100 # 图像Generator，用来构建输入数据 train_datagen = ImageDataGenerator( width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.2, horizontal_flip=True) # 从文件中读取数据，目录结构应为train下面是各个类别的子目录，每个子目录中为对应类别的图像 train_generator = train_datagen.flow_from_directory('./train', target_size = (224, 224), batch_size = batch_size) # 训练图像的数量 image_numbers = train_generator.samples # 输出类别信息 print train_generator.class_indices # 生成测试数据 test_datagen = ImageDataGenerator() validation_generator = test_datagen.flow_from_directory('./validation', target_size = (224, 224), batch_size = batch_size) # 使用ResNet的结构，不包括最后一层，且加载ImageNet的预训练参数 base_model = ResNet50(weights = 'imagenet', include_top = False, pooling = 'avg') # 构建网络的最后一层，3是自己的数据的类别 predictions = Dense(3, activation='softmax')(base_model.output) # 定义整个模型 model = Model(inputs=base_model.input, outputs=predictions) # 编译模型，loss为交叉熵损失 model.compile(optimizer='rmsprop', loss='categorical_crossentropy') # 训练模型 model.fit_generator(train_generator,steps_per_epoch = image_numbers // batch_size, epochs = epochs, validation_data = validation_generator, validation_steps = batch_size) # 保存训练得到的模型 model.save_weights('weights.h5') 部分结果 {'Type_3': 2, 'Type_2': 1, 'Type_1': 0} Found 761 images belonging to 3 classes. Epoch 1/40 1/16 [>.............................] - ETA: 119s - loss: 1.33922017-06-07 10:18:48.246289: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2521 get requests, put_count=2161 evicted_count=1000 eviction_rate=0.462749 and unsatisfied allocation rate=0.579135 2017-06-07 10:18:48.246348: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 100 to 110 16/16 [==============================] - 120s - loss: 2.3753 - val_loss: 10.8293 Epoch 2/40 1/16 [>.............................] - ETA: 5s - loss: 1.00542017-06-07 10:20:40.464589: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2270 get requests, put_count=2642 evicted_count=1000 eviction_rate=0.378501 and unsatisfied allocation rate=0.286784 2017-06-07 10:20:40.464643: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 256 to 281 16/16 [==============================] - 83s - loss: 1.7988 - val_loss: 11.5219 Epoch 3/40 16/16 [==============================] - 81s - loss: 1.6640 - val_loss: 11.0043 Epoch 4/40 3/16 [====>.........................] - ETA: 4s - loss: 1.87452017-06-07 10:23:26.725923: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 11057 get requests, put_count=11071 evicted_count=1000 eviction_rate=0.0903261 and unsatisfied allocation rate=0.0945103 2017-06-07 10:23:26.725986: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 655 to 720 16/16 [==============================] - 83s - loss: 1.7237 - val_loss: 11.7738 Epoch 5/40 16/16 [==============================] - 83s - loss: 1.6304 - val_loss: 10.6538 Epoch 6/40 16/16 [==============================] - 80s - loss: 1.2182 - val_loss: 4.5027 Epoch 7/40 16/16 [==============================] - 83s - loss: 1.3179 - val_loss: 11.5891 Epoch 8/40 16/16 [==============================] - 82s - loss: 1.1806 - val_loss: 10.5800 Epoch 9/40 16/16 [==============================] - 81s - loss: 1.1935 - val_loss: 11.1477 Epoch 10/40 16/16 [==============================] - 80s - loss: 1.1727 - val_loss: 7.0913 Epoch 11/40 16/16 [==============================] - 83s - loss: 1.2058 - val_loss: 6.4474 Epoch 12/40 16/16 [==============================] - 82s - loss: 1.2702 - val_loss: 7.7678 Epoch 13/40 16/16 [==============================] - 84s - loss: 1.2060 - val_loss: 7.9961 Epoch 14/40 16/16 [==============================] - 83s - loss: 1.0768 - val_loss: 11.2121 Epoch 15/40 16/16 [==============================] - 80s - loss: 1.1401 - val_loss: 13.2052 Epoch 16/40 16/16 [==============================] - 83s - loss: 1.1961 - val_loss: 13.0330