将数据标签变为类似MNIST的one-hot编码形式

def one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None): """Returns a one-hot tensor. The locations represented by indices in `indices` take value `on_value`, while all other locations take value `off_value`. `on_value` and `off_value` must have matching data types. If `dtype` is also provided, they must be the same data type as specified by `dtype`. If `on_value` is not provided, it will default to the value `1` with type `dtype` If `off_value` is not provided, it will default to the value `0` with type `dtype` If the input `indices` is rank `N`, the output will have rank `N+1`. The new axis is created at dimension `axis` (default: the new axis is appended at the end). If `indices` is a scalar the output shape will be a vector of length `depth` If `indices` is a vector of length `features`, the output shape will be: ``` features x depth if axis == -1 depth x features if axis == 0 ``` If `indices` is a matrix (batch) with shape `[batch, features]`, the output shape will be: ``` batch x features x depth if axis == -1 batch x depth x features if axis == 1 depth x batch x features if axis == 0 ``` If `dtype` is not provided, it will attempt to assume the data type of `on_value` or `off_value`, if one or both are passed in. If none of `on_value`, `off_value`, or `dtype` are provided, `dtype` will default to the value `tf.float32`. Note: If a non-numeric data type output is desired (`tf.string`, `tf.bool`, etc.), both `on_value` and `off_value` _must_ be provided to `one_hot`. For example: ```python indices = [0, 1, 2] depth = 3 tf.one_hot(indices, depth) # output: [3 x 3] # [[1., 0., 0.], # [0., 1., 0.], # [0., 0., 1.]] indices = [0, 2, -1, 1] depth = 3 tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1) # output: [4 x 3] # [[5.0, 0.0, 0.0], # one_hot(0) # [0.0, 0.0, 5.0], # one_hot(2) # [0.0, 0.0, 0.0], # one_hot(-1) # [0.0, 5.0, 0.0]] # one_hot(1) indices = [[0, 2], [1, -1]] depth = 3 tf.one_hot(indices, depth, on_value=1.0, off_value=0.0, axis=-1) # output: [2 x 2 x 3] # [[[1.0, 0.0, 0.0], # one_hot(0) # [0.0, 0.0, 1.0]], # one_hot(2) # [[0.0, 1.0, 0.0], # one_hot(1) # [0.0, 0.0, 0.0]]] # one_hot(-1) ``` Args: indices: A `Tensor` of indices. depth: A scalar defining the depth of the one hot dimension. on_value: A scalar defining the value to fill in output when `indices[j] = i`. (default: 1) off_value: A scalar defining the value to fill in output when `indices[j] != i`. (default: 0) axis: The axis to fill (default: -1, a new inner-most axis). dtype: The data type of the output tensor. Returns: output: The one-hot tensor. Raises: TypeError: If dtype of either `on_value` or `off_value` don't match `dtype` TypeError: If dtype of `on_value` and `off_value` don't match one another """ with ops.name_scope(name, "one_hot", [indices, depth, on_value, off_value, axis, dtype]) as name: on_exists = on_value is not None off_exists = off_value is not None on_dtype = ops.convert_to_tensor(on_value).dtype.base_dtype if on_exists else None off_dtype = ops.convert_to_tensor(off_value).dtype. base_dtype if off_exists else None if on_exists or off_exists: if dtype is not None: # Ensure provided on_value and/or off_value match dtype if (on_exists and on_dtype != dtype): raise TypeError("dtype {0} of on_value does not match " "dtype parameter {1}".format(on_dtype, dtype)) if (off_exists and off_dtype != dtype): raise TypeError("dtype {0} of off_value does not match " "dtype parameter {1}".format(off_dtype, dtype)) else: # dtype not provided: automatically assign it dtype = on_dtype if on_exists else off_dtype elif dtype is None: # None of on_value, off_value, or dtype provided. Default dtype to float32 dtype = dtypes.float32 if not on_exists: # on_value not provided: assign to value 1 of type dtype on_value = ops.convert_to_tensor(1, dtype, name=" on_value") on_dtype = dtype if not off_exists: # off_value not provided: assign to value 0 of type dtype off_value = ops.convert_to_tensor(0, dtype, name=" off_value") off_dtype = dtype if on_dtype != off_dtype: raise TypeError("dtype {0} of on_value does not match " "dtype {1} of off_value".format(on_dtype, off_dtype)) return gen_array_ops._one_hot(indices, depth, on_value, off_value, axis, name) Enter: apply completion. + Ctrl: remove arguments and replace current word (no Pop- up focus). + Shift: remove arguments (requires Pop-up focus).import tensorflow as tfimport numpy as npdata = np.linspace(0,9,10)label = tf.one_hot(data,10)with tf.Session() as sess: print(data) print(sess.run(label))

补充知识：数据清洗—制作one-hot

使用pandas进行one-hot编码

pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)

pandas中get_dummies()函数可以将字段进行编码，转换为01形式，其中prefix可以为每个新展开的列名添加前缀。

但是，笔者发现它较易使用在数据为每一列为单独的字符：

df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'], 'C': [1, 2, 3]})## one-hotdf_dumm = pd.get_dummies(df)

my_one_hot

但是对于数据为下面形式的可就不能直接转换了，需要先预处理一下，之后转换为one-hot形式：

我的做法是:

## tqdm_notebook可以导入tqdm包来使用def one_hot_my(dataframe, attri): sample_attri_list = [] sample_attri_loc_dic = {} loc = 0 dataframe[attri] = dataframe[attri].astype(str) for attri_id in tqdm_notebook(dataframe[attri]): attri_id_pro = attri_id.strip().split(',') for key in attri_id_pro: if key not in sample_attri_loc_dic.keys(): sample_attri_loc_dic[key] = loc loc+=1 sample_attri_list.append(attri_id_pro) print("开始完成one-hot.......") one_hot_attri = [] for attri_id in tqdm_notebook(sample_attri_list): array = [0 for _ in range(len(sample_attri_loc_dic.keys()))] for key in attri_id: array[sample_attri_loc_dic[key]] = 1 one_hot_attri.append(array) print("封装成dataframe.......") ## 封装成dataframe columns = [attri+x for x in sample_attri_loc_dic.keys()] one_hot_rig_id_df = pd.DataFrame(one_hot_attri,columns=columns) return one_hot_rig_id_df

对属性二值化可以采用：

## 对属性进行二值化def binary_apply(key, attri, dataframe): key_modify = 'is_' + ''.join(lazy_pinyin(key)) + '_' + attri print(key_modify) dataframe[key_modify] = dataframe.apply(lambda x:1 if x[attri]== key else 0, axis=1) return dataframe

对字符进行编码,将字符转换为0，1，2…：

## 对字符进行编码# columns = ['job', 'marital', 'education','default','housing' ,'loan','contact', 'poutcome']def encode_info(dataframe, columns): for col in columns: print(col) dataframe[col] = pd.factorize(dataframe[col])[0] return dataframe

以上这篇Tensorflow实现将标签变为one-hot形式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。