Question

使用过的CNN已经显示在Image prediction using Keras中，但我想跟踪另一个目标。我显然使用了KERAS教程的以下CNN“使用很少的数据构建强大的图像分类模型”（Francois Chollet）。我想将错误的预测作为额外的输出，以查看图像是否很难分类（如果CNN出现问题）-是否有任何简单的方法将其包含在代码中？

from keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
       rotation_range=40,
       width_shift_range=0.2,
       height_shift_range=0.2,
       rescale=1./255,
       shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

from keras.preprocessing.image import ImageDataGenerator,
array_to_img, img_to_array, load_img

datagen = ImageDataGenerator(
        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')

img = load_img('data/train/cats/cat.0.jpg')  # this is a PIL image x =
img_to_array(img)  # this is a Numpy array with shape (3, 150, 150) x
= x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)

i = 0 for batch in datagen.flow(x, batch_size=1,
                          save_to_dir='preview', save_prefix='cat', save_format='jpeg'):
    i += 1
    if i > 20:
        break  # otherwise the generator would loop indefinitely

from keras.models import Sequential from keras.layers import Conv2D,
MaxPooling2D from keras.layers import Activation, Dropout, Flatten,
Dense

model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=(3, 150,
150))) model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3))) model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3))) model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())  # this converts our 3D feature maps to 1D
feature vectors model.add(Dense(64)) model.add(Activation('relu'))
model.add(Dropout(0.5)) model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy']) batch_size = 16

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(
        'data/train',  # this is the target directory
        target_size=(150, 150),  # all images will be resized to 150x150
        batch_size=batch_size,
        class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels

validation_generator = test_datagen.flow_from_directory(
        'data/validation',
        target_size=(150, 150),
        batch_size=batch_size,
        class_mode='binary')

model.fit_generator(
        train_generator,
        steps_per_epoch=2000 // batch_size,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=800 // batch_size)
model.save_weights('first_try.h5')

获取错误预测的索引keras

0 个答案: