美国有线电视新闻网–用发生器进行图像数据预处理

# Importing the ImageDataGenerator for pre-processing 
from keras.preprocessing.image import ImageDataGenerator
# Initialising the generators for train and test data
# The rescale parameter ensures the input range in [0, 1] 
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)
# Creating the generators with each batch of size = 20 images
# The train_dir is the path to train folder which contains input classes
# Here it is 'cat' and 'dog' so class_mode is binary
train_generator = train_datagen.flow_from_directory(
                  train_dir,
                  target_size =(150, 150),  # target_size = input image size
                  batch_size = 20,
                  class_mode ='binary')
test_generator = test_datagen.flow_from_directory(
                    test_dir,
                    target_size =(150, 150),
                    batch_size = 20,
                    class_mode ='binary')


It yields batches of *150 × 150* RGB images of shape *(20, 150, 150, 3)*
and binary labels of shape *(20, )*.


# Your compiled model being trained with fit_generator
history = model.fit_generator(
             train_generator,
             steps_per_epoch = 100,
             epochs = 30,
             validation_data = test_generator,
             validation_steps = 50)
# Note: here the validation steps are necessary because
# the test_genrator also yield batches indefinitely in loops