Keras Utilities¶
1.模型可视化(Model plotting utilities)¶
1.1 plot_model()¶
- Converts a Keras model to dot format and save to a file.
import tensorflow as tf tf.keras.utils.plot_model( model, to_file = "model.png", show_shapes = False, show_dtype = False, show_layer_names = True, rankdir = "TB", expand_nested = False, dpi = 96, )
1.2 model_to_dot()¶
- Convert a Keras model to dot format.
import tensorflow as tf tf.keras.utils.model_to_dot( model, show_shapes = False, show_dtype = False, show_layer_names = True, rankdir = "TB", # "TB": a vertical plot; "LR": a horizontal plot expand_nested = False, dpi = 96, subgraph = False, )
2.序列化工具(Serialization utilities)¶
- custom_object_scope()
- get_custom_objects()
- register_keras_serializable()
- serialize_keras_object()
- daserialize_keras_object()
2.1 CustomObjectScope class¶
作用
- 将自定义类/函数 暴露给 Keras 反序列化内部组件
- 在范围
with custom_object_scope(object_dict)，Keras 方法将能够反序列化已保存的配置引用的任何自定义对象
语法
import tensorflow as tf tf.keras.utils.custom_object_scope(*args)
示例
# 一个自定义的正则化器 `my_regularizer` my_regularizer = None # a layer layer = Dense(3, kernel_regularizer = my_regularizer) # Config contains a reference to "my_regularizer" config = layer.get_config() ... # Later with custom_object_scope({"my_regularizer": my_regularizer}): layer = Dense.from_config(config)
2.2 get_custom_objects()¶
作用
- 额，下次一定
语法
import tensorflow as tf tf.keras.utils.get_custom_objects()
示例
get_custom_objects().clear() get_custom_objects()["MyObject"] = MyObject
2.3 register_keras_serializable()¶
作用
- 额，下次一定
语法
import tensorflow as tf tf.keras.utils.register_keras.serializable(package = "Custom", name = None)
2.4 serialize_keras_object()¶
作用
- 将 Keras 对象序列化为 Json 兼容的表示形式
语法
import tensorflow as tf tf.keras.utils.serialize_keras_object(instance)
2.5 daserialize_keras_object()¶
作用
- 将 Keras 对象的序列化形式转换回实际对象
语法
import tensorflow as tf tf.keras.utils.deserialize_keras_object( identifier, module_objects = None, custom_objects = None, printable_module_name = "object" )
3.Python & Numpy utilities¶
3.1 to_categorical()¶
作用
- 将一个类别型向量(整数)转换为 二元类别矩阵
- 类似于 one-hot
语法
import tensorflow as tf utils.to_categorical(y, num_classes = None, dtypes = "float32")
示例
# example 1 a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes = 4) a = tf.constant(a, shape = [4, 4]) print(a) # example 2 b = tf.constant([.9, .04, .03, .03, .3, .45, .15, .13, .04, .01, .94, .05, .12, .21, .5, .17], shape = [4, 4]) loss = tf.keras.backend.categorical_crossentropy(a, b) print(np.around(loss, 5)) # example 3 loss = tf.keras.backend.categorical_crossentropy(a, a) print(np.around(loss, 5))
3.2 normalize()¶
作用
- 标准化一个 Numpy 数组
语法
import tensorflow as tf tf.keras.utils.normalize(x, axis = -1, order = 2)
3.3 get_file()¶
作用
- Downloads a file from a URL if it not already in the cache.
- By default the file at the url
originis downloaded to the cache_dir~/.keras, placed in the cache_subdir datasets, and given the filenamefname. The final location of a fileexample.txtwould therefore be~/.keras/datasets/example.txt. - Files in tar, tar.gz, tar.bz, and zip formats can also be extracted. Passing a hash will verify the file after download. The command line programs shasum and sha256sum can compute the hash.
语法
tf.keras.utils.get_file( fname, origin, untar=False, md5_hash=None, file_hash=None, cache_subdir="datasets", hash_algorithm="auto", extract=False, archive_format="auto", cache_dir=None, )
示例
import tensorflow path_to_downloaded_file = tf.keras.utils.get_file( "flower_photos", "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz", untar = True )
3.4 Progbar class¶
作用
- 显示进度条
语法
import tensorflow as tf tf.keras.utils.Progbar( target, width = 30, verbose = 1, interval = 0.05, stateful_metrics = None, unit_name = "step" )
3.5 Sequence class¶
作用
- 用于拟合数据序列（如数据集）的基础对象
- 每个人都Sequence必须实现__getitem__和__len__方法。如果您想在各个时期之间修改数据集，则可以实现 on_epoch_end。该方法__getitem__应返回完整的批次
- Sequence是进行多处理的更安全方法。这种结构保证了网络在每个时期的每个样本上只会训练一次，而生成器则不会
语法
import tensorflow as tf tf.keras.utils.Sequence()
示例
from skimage.io import imread from skimage.transform import resize import numpy as np import math # Here, `x_set` is list of path to the images # and `y_set` are the associated classes. class CIFAR10Sequence(Sequence): def __init__(self, x_set, y_set, batch_size): self.x, self.y = x_set, y_set self.batch_size = batch_size def __len__(self): return math.ceil(len(self.x) / self.batch_size) def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] return np.array([ resize(imread(file_name), (200, 200)) for file_name in batch_x]), np.array(batch_y)
4.Backend utilities¶
clear_session()floatx()set_floatx()image_data_format()set_image_data_format()epsilon()set_epsilon()is_keras_tensor()get_uid()rnn()
4.1 clear_session()¶
4.1 floatx()¶
作用
- 返回默认的 float 类型
语法
import tensorflow as tf tf.keras.backend.floatx()
4.1 set_floatx()¶
作用
- 设置 float 类型
语法
import tensorflow as tf tf.keras.backend.set_floatx()
示例
import tensorflow as tf tf.keras.backend.floatx() tf.keras.backend.set_floatx("float64") tf.keras.backend.floatx() tf.keras.backend.set_floatx("float32")
4.1 image_data_format()¶
作用
- 返回设置图像数据格式约定的值
语法
import tensorflow as tf tf.keras.backend.image_data_format(data_format)
4.1 set_image_data_format()¶
作用
- 设置图像数据格式约定的值
语法
import tensorflow as tf tf.keras.backend.set_image_data_format(data_format)
示例
tf.keras.backend.image_data_format() tf.keras.backend.set_image_data_format("channels_first") tf.keras.backend.set_image_data_format("channels_last")
4.1 epsilon()¶
作用
- 返回数字表达式中使用的模糊因子的值
语法
tf.keras.backend.epsilon()
4.1 set_epsilon()¶
作用
- 设置数字表达式中使用的模糊因子的值
语法
tf.keras.backend.set_epsilon()