对猫狗数据集进行数据预处理，增广以及分类对比.zip

""" 不使用数据增强的快速特征提取 """ import os import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.applications import VGG16 conv_base = VGG16(weights='imagenet',include_top=False,input_shape=(150, 150, 3)) base_dir='D:/pic/small_dogvscat' train_dir=os.path.join(base_dir,'train') validation_dir=os.path.join(base_dir,'validation') test_dir=os.path.join(base_dir,'test') data=ImageDataGenerator(rescale=1./255) batch_size=20 def extra_features(dic,sample_count): features=np.zeros(shape=(sample_count,4,4,512)) labels=np.zeros(shape=(sample_count)) generator=data.flow_from_directory( dic, target_size=(150,150), batch_size=batch_size, class_mode='binary' ) i=0 for input_batch,labels_batch in generator: features_batch=conv_base.predict(input_batch) features[i*batch_size:(i+1)*batch_size]=features_batch labels[i*batch_size:(i+1)*batch_size]=labels_batch i=i+1 if i*batch_size>=sample_count: break return features,labels train_features,train_labels=extra_features(train_dir,2000) validation_features,validation_labels=extra_features(validation_dir,1000) test_features,test_labels=extra_features(test_dir,1000) train_features=np.reshape(train_features,(2000,4*4*512)) validation_features=np.reshape(validation_features,(1000,4*4*512)) test_features=np.reshape(test_features,(1000,4*4*512)) from keras import models from keras import layers from keras import optimizers model = models.Sequential() model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512)) model.add(layers.Dropout(0.5)) model.add(layers.Dense(1, activation='sigmoid')) model.compile(optimizer=optimizers.RMSprop(lr=2e-5), loss='binary_crossentropy', metrics=['acc']) history = model.fit(train_features, train_labels, epochs=30, batch_size=20, validation_data=(validation_features, validation_labels)) import matplotlib.pyplot as plt acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(1, len(acc) + 1) plt.plot(epochs, acc, 'bo', label='Training acc') plt.plot(epochs, val_acc, 'r', label='Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure() plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.title('Training and validation loss') plt.legend() plt.show()