代码和其他资料在 github
一、tf.data模块
- 数据分割
import tensorflow as tf
dataset = tf.data.Dataset.from_tensor_slices([1,2,3,4,5,6,7]) #1维
dataset2 = tf.data.Dataset.from_tensor_slices([[1,2],[3,4],[5,6]]) #2维
dataset_dic = tf.data.Dataset.from_tensor_slices({'a':[1,2,3,4],'b':[6,7,8,9], 'c':[12,13,14,15]}) #字典
tf.data.Dataset.from_tensor_slices() 数据切割，并且转化为 Tensor 类型。
dataset
for ele in dataset:
print(ele)
输入：
tf.Tensor(1, shape=(), dtype=int32)
tf.Tensor(2, shape=(), dtype=int32)
tf.Tensor(3, shape=(), dtype=int32)
tf.Tensor(4, shape=(), dtype=int32)
tf.Tensor(5, shape=(), dtype=int32)
tf.Tensor(6, shape=(), dtype=int32)
tf.Tensor(7, shape=(), dtype=int32)
for ele in dataset:
print(ele.numpy())
输入：
1
2
3
4
5
6
7
dataset2
for ele2 in dataset2:
print(ele2.numpy())
输入：
[1 2]
[3 4]
[5 6]
dataset_dic
for ele_dic in dataset_dic:
print(ele_dic)
输入：
{'a': , 'b': , 'c': }
{'a': , 'b': , 'c': }
{'a': , 'b': , 'c': }
{'a': , 'b': , 'c': }
- 其他常用操作
for ele_np in dataset_np.take(4): # 取出前四个
print(ele_np)
dataset_np = dataset_np.shuffle(7) # 打乱顺序
dataset_np = dataset_np.repeat(count = 3) #重复3次，为None无限循环
dataset = dataset.map(tf.square) # 取平方
二、手写识别实例
import tensorflow as tf
(train_images,train_labels),(test_images,test_labels) = tf.keras.datasets.mnist.load_data()
train_images = train_images / 255
test_images = test_images / 255
ds_train_img = tf.data.Dataset.from_tensor_slices(train_images)
ds_train_lab = tf.data.Dataset.from_tensor_slices(train_labels)
ds_train = tf.data.Dataset.zip((ds_train_img,ds_train_lab)) # 数据合并
ds_train = ds_train.shuffle(buffer_size = 10000).repeat().batch(64)
ds_test = tf.data.Dataset.from_tensor_slices((test_images,test_labels))
ds_test = ds_test.batch(64)
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape = (28,28)),tf.keras.layers.Dense(128,activation = 'relu'),tf.keras.layers.Dense(10,activation = 'softmax')])
model.compile(optimizer = 'adam',loss = 'sparse_categorical_crossentropy',metrics = ['accuracy'])
steps_per_epoch = train_images.shape[0] // 64 # 每个epoch的步数
model.fit(ds_train,epochs = 5,steps_per_epoch = steps_per_epoch,validation_data = ds_test,validation_steps = 10000 // 64)