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数组。我试着在

上获得softmax

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

图表，它在这一行上给出了错误：

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

错误是：

InvalidArgumentError（参见上面的回溯）：预期开始[0] == 0   （got -1）和size [0] == 0（得1）当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）]]

我应该如何在tensorflow中获得softmax？

Answer 1

问题出现是因为您在tf.reduce_sum的参数上调用了tf.nn.softmax。因此，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：Tensorflow不提供开箱即用的等效np.dot。如果要计算矩阵和向量的点积，则需要明确地对索引求和：

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

张量流中softmax上的InvalidArgumentError

1 个答案: