Keras使用预网络学习，迁移学习,特征提取，数据增强 VGG16
代码
from keras.applications import VGG16
train_dir = os.path.join(base_dir, 'train')
conv_base = VGG16(weights='imagenet',
include_top=False,
input_shape=(150, 150, 3))
from keras import layers
from keras import models
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
conv_base.trainable = False
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest'
)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(2e-5),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=100)
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, 'b', 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()
Found 1998 images belonging to 2 classes. Found 1000 images belonging to 2 classes. Epoch 1/30 100/100 [==============================] - 63s 627ms/step - loss: 0.5820 - acc: 0.7119 - val_loss: 0.4467 - val_acc: 0.8220 Epoch 2/30 100/100 [==============================] - 62s 622ms/step - loss: 0.4714 - acc: 0.7957 - val_loss: 0.3649 - val_acc: 0.8550 Epoch 3/30 100/100 [==============================] - 63s 630ms/step - loss: 0.4236 - acc: 0.8173 - val_loss: 0.3235 - val_acc: 0.8760 Epoch 4/30 100/100 [==============================] - 63s 630ms/step - loss: 0.4076 - acc: 0.8164 - val_loss: 0.3093 - val_acc: 0.8780 Epoch 5/30 100/100 [==============================] - 63s 626ms/step - loss: 0.3893 - acc: 0.8309 - val_loss: 0.2969 - val_acc: 0.8770 Epoch 6/30 100/100 [==============================] - 62s 625ms/step - loss: 0.3626 - acc: 0.8383 - val_loss: 0.2825 - val_acc: 0.8820 Epoch 7/30 100/100 [==============================] - 62s 625ms/step - loss: 0.3753 - acc: 0.8383 - val_loss: 0.2694 - val_acc: 0.8860 Epoch 8/30 100/100 [==============================] - 63s 625ms/step - loss: 0.3414 - acc: 0.8533 - val_loss: 0.2667 - val_acc: 0.8900 Epoch 9/30 100/100 [==============================] - 62s 625ms/step - loss: 0.3469 - acc: 0.8458 - val_loss: 0.2591 - val_acc: 0.8940 Epoch 10/30 100/100 [==============================] - 63s 625ms/step - loss: 0.3455 - acc: 0.8428 - val_loss: 0.2614 - val_acc: 0.8890 Epoch 11/30 100/100 [==============================] - 63s 626ms/step - loss: 0.3221 - acc: 0.8619 - val_loss: 0.2543 - val_acc: 0.8980 Epoch 12/30 100/100 [==============================] - 63s 625ms/step - loss: 0.3227 - acc: 0.8584 - val_loss: 0.2526 - val_acc: 0.8980 Epoch 13/30 100/100 [==============================] - 63s 627ms/step - loss: 0.3280 - acc: 0.8504 - val_loss: 0.2481 - val_acc: 0.9060 Epoch 14/30 100/100 [==============================] - 63s 626ms/step - loss: 0.3243 - acc: 0.8543 - val_loss: 0.2519 - val_acc: 0.9030 Epoch 15/30 100/100 [==============================] - 63s 626ms/step - loss: 0.3035 - acc: 0.8649 - val_loss: 0.2525 - val_acc: 0.8950 Epoch 16/30 100/100 [==============================] - 63s 626ms/step - loss: 0.3204 - acc: 0.8534 - val_loss: 0.2444 - val_acc: 0.9080 Epoch 17/30 100/100 [==============================] - 62s 625ms/step - loss: 0.3148 - acc: 0.8604 - val_loss: 0.2511 - val_acc: 0.8980 Epoch 18/30 100/100 [==============================] - 62s 625ms/step - loss: 0.3078 - acc: 0.8663 - val_loss: 0.2471 - val_acc: 0.9000 Epoch 19/30 100/100 [==============================] - 62s 625ms/step - loss: 0.2878 - acc: 0.8814 - val_loss: 0.2384 - val_acc: 0.9040 Epoch 20/30 100/100 [==============================] - 63s 626ms/step - loss: 0.2925 - acc: 0.8739 - val_loss: 0.2353 - val_acc: 0.9030 Epoch 21/30 100/100 [==============================] - 63s 626ms/step - loss: 0.2980 - acc: 0.8729 - val_loss: 0.2475 - val_acc: 0.8990 Epoch 22/30 100/100 [==============================] - 63s 626ms/step - loss: 0.3013 - acc: 0.8684 - val_loss: 0.2361 - val_acc: 0.9100 Epoch 23/30 100/100 [==============================] - 63s 627ms/step - loss: 0.2995 - acc: 0.8694 - val_loss: 0.2345 - val_acc: 0.8990 Epoch 24/30 100/100 [==============================] - 63s 627ms/step - loss: 0.2985 - acc: 0.8654 - val_loss: 0.2313 - val_acc: 0.9090 Epoch 25/30 100/100 [==============================] - 63s 628ms/step - loss: 0.2883 - acc: 0.8704 - val_loss: 0.2348 - val_acc: 0.9010 Epoch 26/30 100/100 [==============================] - 63s 627ms/step - loss: 0.2829 - acc: 0.8798 - val_loss: 0.2451 - val_acc: 0.8970 Epoch 27/30 100/100 [==============================] - 63s 626ms/step - loss: 0.2825 - acc: 0.8772 - val_loss: 0.2315 - val_acc: 0.9040 Epoch 28/30 100/100 [==============================] - 63s 625ms/step - loss: 0.2875 - acc: 0.8724 - val_loss: 0.2277 - val_acc: 0.9150 Epoch 29/30 100/100 [==============================] - 63s 625ms/step - loss: 0.2819 - acc: 0.8903 - val_loss: 0.2273 - val_acc: 0.9130 Epoch 30/30 100/100 [==============================] - 63s 625ms/step - loss: 0.2756 - acc: 0.8808 - val_loss: 0.2335 - val_acc: 0.9040
