#~ model.add(MaxPooling2D(pool_size=(2, 2))) #~ model.add(Dropout(0.2)) #~ model.add(Flatten()) #~ model.add(Dense(128, activation='relu')) #~ model.add(Dense(base.nb_classes)) #~ model.add(Dense(base.nb_classes, activation='softmax')) print("Compilando...") model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy', top3]) model.summary() #~ model.compile(loss='categorical_crossentropy', #~ optimizer='adadelta', #~ metrics=['accuracy' ]) trainer = base.Trainer('redMarianoVieja', train_data=base.dataset("dataseth5/train.h5", "Train"), valid_data=base.dataset("dataseth5/valid.h5", "Valid"), test_data=base.dataset("dataseth5/test.h5", "Test")) #~ trainer.train(model, nb_epoch=2, samples_per_epoch=10240, nb_val_samples=5000) trainer.train(model, nb_epoch=100, samples_per_epoch=269018) #usa todo el dataset #~ trainer.train(model, nb_epoch=12, samples_per_epoch=269018) #usa todo el dataset #~ trainer.train(model, nb_epoch=3, samples_per_epoch=100) #usa todo el dataset #~ model = load_model('redMarianoPro--01-Nov-2016--10-40--model.h5') trainer.evaluate(model)
import base from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D # number of convolutional filters to use nb_filters = 32 # size of pooling area for max pooling pool_size = (2, 2) # convolution kernel size kernel_size = (3, 3) #~ trainer = base.Trainer('red_mas_densa', train_data=base.dataset("dataset/train", "Train"), #~ valid_data=base.dataset("dataset/valid", "Valid"), #~ test_data=base.dataset("dataset/test", "Test")) trainer = base.Trainer('red_mas_densa', train_data=base.dataset("dataseth5/train.h5", "Train"), valid_data=base.dataset("dataseth5/valid.h5", "Valid"), test_data=base.dataset("dataseth5/test.h5", "Test")) #~ trainer.train_data.preview() print("Armando red...") model = Sequential() model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=base.input_shape)) model.add(Activation('relu')) model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=pool_size)) model.add(Dropout(0.25))