InvalidArgumentError上SOFTMAX在tensorflow

Question

我有以下功能：

def forward_propagation(self, x): 
       # The total number of time steps 
       T = len(x) 
       # During forward propagation we save all hidden states in s because need them later. 
       # We add one additional element for the initial hidden, which we set to 0 
       s = tf.zeros([T+1, self.hidden_dim]) 
       # The outputs at each time step. Again, we save them for later. 
       o = tf.zeros([T, self.word_dim]) 


       a = tf.placeholder(tf.float32) 
       b = tf.placeholder(tf.float32) 
       c = tf.placeholder(tf.float32) 

       s_t = tf.nn.tanh(a + tf.reduce_sum(tf.multiply(b, c))) 
       o_t = tf.nn.softmax(tf.reduce_sum(tf.multiply(a, b))) 
       # For each time step... 
       with tf.Session() as sess: 
         s = sess.run(s) 
         o = sess.run(o) 
         for t in range(T): 
           # Note that we are indexing U by x[t]. This is the same as multiplying U with a one-hot vector. 
           s[t] = sess.run(s_t, feed_dict={a: self.U[:, x[t]], b: self.W, c: s[t-1]}) 
           o[t] = sess.run(o_t, feed_dict={a: self.V, b: s[t]}) 
       return [o, s] 

self.U，self.V，和self.W是numpy的阵列。我试图让SOFTMAX上

o_t = tf.nn.softmax(tf.reduce_sum(tf.multiply(a, b))) 

图，它给我的错误在这条线：

o[t] = sess.run(o_t, feed_dict={a: self.V, b: s[t]}) 

的错误是：

InvalidArgumentError (see above for traceback): Expected begin[0] == 0 (got -1) and size[0] == 0 (got 1) when input.dim_size(0) == 0
[[Node: Slice = Slice[Index=DT_INT32, T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](Shape_1, Slice/begin, Slice/size)]]

如何，我应该得到SOFTMAX在tensorflow？

Answer 1

问题出现是因为您在tf.nn.softmax的参数上致电tf.reduce_sum。结果，softmax函数失败，因为标量不是有效的输入参数。您的意思是使用tf.matmul而不是tf.reduce_sum和tf.multiply的组合吗？

编辑：开箱即用的Tensorflow不提供等效的np.dot。如果要计算矩阵和向量的点积，则需要明确地总和索引：

# equivalent to np.dot(a, b) if a.ndim == 2 and b.ndim == 1 
c = tf.reduce_sum(a * b, axis=1)