如下所示：

def softmax(logits,axis=None,name=None,dim=None):
 """Computes softmax activations.
 This function performs the equivalent of
  softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits),axis)
 Args:
 logits: A non-empty `Tensor`. Must be one of the following types: `half`,`float32`,`float64`.
 axis: The dimension softmax would be performed on. The default is -1 which
  indicates the last dimension.
 name: A name for the operation (optional).
 dim: Deprecated alias for `axis`.
 Returns:
 A `Tensor`. Has the same type and shape as `logits`.
 Raises:
 InvalidArgumentError: if `logits` is empty or `axis` is beyond the last
  dimension of `logits`.
 """
 axis = deprecation.deprecated_argument_lookup("axis",axis,"dim",dim)
 if axis is None:
 axis = -1
 return _softmax(logits,gen_nn_ops.softmax,name)

softmax函式的返回結果和輸入的tensor有相同的shape，既然沒有改變tensor的形狀，那麼softmax究竟對tensor做了什麼？

答案就是softmax會以某一個軸的下標為索引，對這一軸上其他維度的值進行 啟用 + 歸一化處理。

一般來說，這個索引軸都是表示類別的那個維度（tf.nn.softmax中預設為axis=-1,也就是最後一個維度）

舉例：

def softmax(X,theta = 1.0,axis = None):
 """
 Compute the softmax of each element along an axis of X.
 Parameters
 ----------
 X: ND-Array. Probably should be floats.
 theta (optional): float parameter,used as a multiplier
  prior to exponentiation. Default = 1.0
 axis (optional): axis to compute values along. Default is the
  first non-singleton axis.
 Returns an array the same size as X. The result will sum to 1
 along the specified axis.
 """
 
 # make X at least 2d
 y = np.atleast_2d(X)
 
 # find axis
 if axis is None:
  axis = next(j[0] for j in enumerate(y.shape) if j[1] > 1)
 
 # multiply y against the theta parameter,y = y * float(theta)
 
 # subtract the max for numerical stability
 y = y - np.expand_dims(np.max(y,axis = axis),axis)
 
 # exponentiate y
 y = np.exp(y)
 
 # take the sum along the specified axis
 ax_sum = np.expand_dims(np.sum(y,axis)
 
 # finally: divide elementwise
 p = y / ax_sum
 
 # flatten if X was 1D
 if len(X.shape) == 1: p = p.flatten()
 
 return p
c = np.random.randn(2,3)
print(c)
# 假設第0維是類別，一共有裡兩種類別
cc = softmax(c,axis=0)
# 假設最後一維是類別，一共有3種類別
ccc = softmax(c,axis=-1)
print(cc)
print(ccc)

結果：

c:
[[-1.30022268 0.59127472 1.21384177]
 [ 0.1981082 -0.83686108 -1.54785864]]
cc:
[[0.1826746 0.80661068 0.94057075]
 [0.8173254 0.19338932 0.05942925]]
ccc:
[[0.0500392 0.33172426 0.61823654]
 [0.65371718 0.23222472 0.1140581 ]]

可以看到，對axis=0的軸做softmax時，輸出結果在axis=0軸上和為1(eg: 0.1826746+0.8173254)，同理在axis=1軸上做的話結果的axis=1軸和也為1(eg: 0.0500392+0.33172426+0.61823654)。

這些值是怎麼得到的呢？

以cc為例（沿著axis=0做softmax）：

以ccc為例（沿著axis=1做softmax）：

知道了計算方法，現在我們再來討論一下這些值的實際意義：

cc[0,0]實際上表示這樣一種概率： P( label = 0 | value = [-1.30022268 0.1981082] = c[*,0] ) = 0.1826746

cc[1,0]實際上表示這樣一種概率： P( label = 1 | value = [-1.30022268 0.1981082] = c[*,0] ) = 0.8173254

ccc[0,0]實際上表示這樣一種概率： P( label = 0 | value = [-1.30022268 0.59127472 1.21384177] = c[0]) = 0.0500392

ccc[0,1]實際上表示這樣一種概率： P( label = 1 | value = [-1.30022268 0.59127472 1.21384177] = c[0]) = 0.33172426

ccc[0,2]實際上表示這樣一種概率： P( label = 2 | value = [-1.30022268 0.59127472 1.21384177] = c[0]) = 0.61823654

將他們擴充套件到更多維的情況：假設c是一個[batch_size,timesteps,categories]的三維tensor

output = tf.nn.softmax(c,axis=-1)

那麼 output[1,2,3] 則表示 P(label =3 | value = c[1,2] )

以上這篇關於tensorflow softmax函式用法解析就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。