 《Efficient Deep Learning Book》[EDL] Chapter 3 - Learning Techniquesupshifted image generated by below code. # Horizontal Shift transform_and_show(image_path, ty=50) Zoom scales an area in an image. One of the techniques to identify whales uses the markings on their pectoral figure 3-6 is a 2x vertical zoom results in more pronounced fin markings. # Vertical Zoom Transformation transform_and_show(image_path, zx=.5) # A value of .5 implies 2X zoom Shear transformation changes0 码力 | 56 页 | 18.93 MB | 1 年前3 《Efficient Deep Learning Book》[EDL] Chapter 3 - Learning Techniquesupshifted image generated by below code. # Horizontal Shift transform_and_show(image_path, ty=50) Zoom scales an area in an image. One of the techniques to identify whales uses the markings on their pectoral figure 3-6 is a 2x vertical zoom results in more pronounced fin markings. # Vertical Zoom Transformation transform_and_show(image_path, zx=.5) # A value of .5 implies 2X zoom Shear transformation changes0 码力 | 56 页 | 18.93 MB | 1 年前3
 Keras: 基于 Python 的深度学习库rotation_range=0.0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False +1.0) 之间的浮点数。 • shear_range: 浮点数。剪切强度(以弧度逆时针方向剪切角度)。 • zoom_range: 浮点数或 [lower, upper]。随机缩放范围。如果是浮点数,[lower, upper] = [1-zoom_range, 1+zoom_range]。 • channel_shift_range: 浮点数。随机通道转换的范围。 • fill_mode: flow_from_directory(directory) 的例子: train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1./255) 数据预处理 128 train_generator0 码力 | 257 页 | 1.19 MB | 1 年前3 Keras: 基于 Python 的深度学习库rotation_range=0.0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False +1.0) 之间的浮点数。 • shear_range: 浮点数。剪切强度(以弧度逆时针方向剪切角度)。 • zoom_range: 浮点数或 [lower, upper]。随机缩放范围。如果是浮点数,[lower, upper] = [1-zoom_range, 1+zoom_range]。 • channel_shift_range: 浮点数。随机通道转换的范围。 • fill_mode: flow_from_directory(directory) 的例子: train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1./255) 数据预处理 128 train_generator0 码力 | 257 页 | 1.19 MB | 1 年前3
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