本文地址：http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
本文作者：Francois Chollet
- 按照官方的文章实现过程有一些坑，彻底理解代码细节实现，理解keras的api具体使用方法
- 也有很多人翻译这篇文章，但是有些没有具体实现细节
- 另外keres开发者自己有本书的jupyter:Companion Jupyter notebooks for the book "Deep Learning with Python"
- 另外我自己实验三收敛的准确率并没有0.94+，可以参考前面这本书上的实现
- 文章一共有三个实验：
1. 第一个实验使用自定义的神经网络对数据集进行训练，三层卷积加两层全连接，训练并验证网络的准确率；
2. 第二个实验使用VGG16网络对数据进行训练，为了适应自定义的数据集，将VGG16网络的全连接层去掉，作者称之为 “Feature extraction”, 再在上面添加自己实现的全连接层，然后训练并验证网络准确性；
3. 第三个实验称为 “fine-tune” ，利用第二个实验的实验模型和weight，重新训练VGG16的最后一个卷积层和自定义的全连接层，然后验证网络准确性； - 实验二的代码：
‘‘‘This script goes along the blog post "Building powerful image classification models using very little data" from blog.keras.io. It uses data that can be downloaded at: https://www.kaggle.com/c/dogs-vs-cats/data In our setup, we: - created a data/ folder - created train/ and validation/ subfolders inside data/ - created cats/ and dogs/ subfolders inside train/ and validation/ - put the cat pictures index 0-999 in data/train/cats - put the cat pictures index 1000-1400 in data/validation/cats - put the dogs pictures index 12500-13499 in data/train/dogs - put the dog pictures index 13500-13900 in data/validation/dogs So that we have 1000 training examples for each class, and 400 validation examples for each class. In summary, this is our directory structure: ``` data/ train/ dogs/ dog001.jpg dog002.jpg ... cats/ cat001.jpg cat002.jpg ... validation/ dogs/ dog001.jpg dog002.jpg ... cats/ cat001.jpg cat002.jpg ... ``` ‘‘‘ import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dropout, Flatten, Dense from keras import applications # dimensions of our images. img_width, img_height = 150, 150 top_model_weights_path = ‘bottleneck_fc_model.h5‘ data_root = ‘M:/dataset/dog_cat/‘ train_data_dir =data_root+ ‘data/train‘ validation_data_dir = data_root+‘data/validation‘ nb_train_samples = 2000 nb_validation_samples = 800 epochs = 50 batch_size = 16 def save_bottlebeck_features(): datagen = ImageDataGenerator(rescale=1. / 255) # build the VGG16 network model = applications.VGG16(include_top=False, weights=‘imagenet‘) generator = datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) bottleneck_features_train = model.predict_generator( generator, nb_train_samples // batch_size) #####2000//batch_size！！！！！！！！！！ np.save(‘bottleneck_features_train.npy‘, bottleneck_features_train) generator = datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) bottleneck_features_validation = model.predict_generator( generator, nb_validation_samples // batch_size) np.save(‘bottleneck_features_validation.npy‘, bottleneck_features_validation) def train_top_model(): train_data = np.load(‘bottleneck_features_train.npy‘) train_labels = np.array([0] * int(nb_train_samples / 2) + [1] * int(nb_train_samples / 2)) validation_data = np.load(‘bottleneck_features_validation.npy‘) validation_labels = np.array([0] * int(nb_validation_samples / 2) + [1] * int(nb_validation_samples / 2)) model = Sequential() model.add(Flatten(input_shape=train_data.shape[1:])) model.add(Dense(256, activation=‘relu‘)) model.add(Dropout(0.5)) model.add(Dense(1, activation=‘sigmoid‘)) model.compile(optimizer=‘rmsprop‘, loss=‘binary_crossentropy‘, metrics=[‘accuracy‘]) model.fit(train_data, train_labels, epochs=epochs, batch_size=batch_size, validation_data=(validation_data, validation_labels)) model.save_weights(top_model_weights_path) #save_bottlebeck_features() train_top_model()
- 实验三代码，自己添加了一些api使用方法，也是以后可以参考的：
‘‘‘This script goes along the blog post "Building powerful image classification models using very little data" from blog.keras.io. It uses data that can be downloaded at: https://www.kaggle.com/c/dogs-vs-cats/data In our setup, we: - created a data/ folder - created train/ and validation/ subfolders inside data/ - created cats/ and dogs/ subfolders inside train/ and validation/ - put the cat pictures index 0-999 in data/train/cats - put the cat pictures index 1000-1400 in data/validation/cats - put the dogs pictures index 12500-13499 in data/train/dogs - put the dog pictures index 13500-13900 in data/validation/dogs So that we have 1000 training examples for each class, and 400 validation examples for each class. In summary, this is our directory structure: ``` data/ train/ dogs/ dog001.jpg dog002.jpg ... cats/ cat001.jpg cat002.jpg ... validation/ dogs/ dog001.jpg dog002.jpg ... cats/ cat001.jpg cat002.jpg ... ``` ‘‘‘ # thanks sove bug @http://blog.csdn.net/aggresss/article/details/78588135 from keras import applications from keras.preprocessing.image import ImageDataGenerator from keras import optimizers from keras.models import Sequential from keras.layers import Dropout, Flatten, Dense from keras.models import Model from keras.regularizers import l2 # path to the model weights files. weights_path = ‘../keras/examples/vgg16_weights.h5‘ top_model_weights_path = ‘bottleneck_fc_model.h5‘ # dimensions of our images. img_width, img_height = 150, 150 data_root = ‘M:/dataset/dog_cat/‘ train_data_dir =data_root+ ‘data/train‘ validation_data_dir = data_root+‘data/validation‘ nb_train_samples = 2000 nb_validation_samples = 800 epochs = 50 batch_size = 16 # build the VGG16 network base_model = applications.VGG16(weights=‘imagenet‘, include_top=False, input_shape=(150,150,3)) # train 指定训练大小 print(‘Model loaded.‘) # build a classifier model to put on top of the convolutional model top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) # base_model.output_shape[1:]) top_model.add(Dense(256, activation=‘relu‘,kernel_regularizer=l2(0.001),)) top_model.add(Dropout(0.8)) top_model.add(Dense(1, activation=‘sigmoid‘)) # note that it is necessary to start with a fully-trained # classifier, including the top classifier, # in order to successfully do fine-tuning top_model.load_weights(top_model_weights_path) # add the model on top of the convolutional base # model.add(top_model) # bug model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) # set the first 25 layers (up to the last conv block) # to non-trainable (weights will not be updated) for layer in model.layers[:15]: # :25 bug layer.trainable = False # compile the model with a SGD/momentum optimizer # and a very slow learning rate. model.compile(loss=‘binary_crossentropy‘, optimizer=optimizers.SGD(lr=1e-4, momentum=0.9), metrics=[‘accuracy‘]) # prepare data augmentation configuration train_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode=‘binary‘) validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode=‘binary‘) model.summary() # prints a summary representation of your model. # let‘s visualize layer names and layer indices to see how many layers # we should freeze: for i, layer in enumerate(base_model.layers): print(i, layer.name) from keras.utils import plot_model plot_model(model, to_file=‘model.png‘) from keras.callbacks import History from keras.callbacks import ModelCheckpoint import keras history = History() model_checkpoint = ModelCheckpoint(‘temp_model.hdf5‘, monitor=‘loss‘, save_best_only=True) tb_cb = keras.callbacks.TensorBoard(log_dir=‘log‘, write_images=1, histogram_freq=0) # 设置log的存储位置，将网络权值以图片格式保持在tensorboard中显示，设置每一个周期计算一次网络的 # 权值，每层输出值的分布直方图 callbacks = [ history, model_checkpoint, tb_cb ] # model.fit() # fine-tune the model history=model.fit_generator( train_generator, steps_per_epoch=nb_train_samples // batch_size, epochs=epochs, callbacks=callbacks, validation_data=validation_generator, validation_steps=nb_validation_samples // batch_size, verbose = 2) model.save(‘fine_tune_model.h5‘) model.save_weights(‘fine_tune_model_weight‘) print(history.history) from matplotlib import pyplot as plt history=history plt.plot() plt.plot(history.history[‘val_acc‘]) plt.title(‘model accuracy‘) plt.ylabel(‘accuracy‘) plt.xlabel(‘epoch‘) plt.legend([‘train‘, ‘test‘], loc=‘upper left‘) plt.show() # summarize history for loss plt.plot(history.history[‘loss‘]) plt.plot(history.history[‘val_loss‘]) plt.title(‘model loss‘) plt.ylabel(‘loss‘) plt.xlabel(‘epoch‘) plt.legend([‘train‘, ‘test‘], loc=‘upper left‘) plt.show() import numpy as np accy=history.history[‘acc‘] np_accy=np.array(accy) np.savetxt(‘save_acc.txt‘,np_accy)
- result
Model loaded. Found 2000 images belonging to 2 classes. Found 800 images belonging to 2 classes. _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 150, 150, 3) 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 150, 150, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 150, 150, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 75, 75, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 75, 75, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 75, 75, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 37, 37, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 37, 37, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 37, 37, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 37, 37, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 18, 18, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 9, 9, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 _________________________________________________________________ sequential_1 (Sequential) (None, 1) 2097665 ================================================================= Total params: 16,812,353 Trainable params: 9,177,089 Non-trainable params: 7,635,264 _________________________________________________________________ 0 input_1 1 block1_conv1 2 block1_conv2 3 block1_pool 4 block2_conv1 5 block2_conv2 6 block2_pool 7 block3_conv1 8 block3_conv2 9 block3_conv3 10 block3_pool 11 block4_conv1 12 block4_conv2 13 block4_conv3 14 block4_pool 15 block5_conv1 16 block5_conv2 17 block5_conv3 18 block5_pool Backend TkAgg is interactive backend. Turning interactive mode on.
- reference: 第八期 使用 Keras 训练神经网络 《显卡就是开发板》
