1.matrix = tf.get_variable("matrix", [output_size, input_size], dtype=input_.dtype)
=matrix = variable(torch.randn(output_size, input_size))
2.self.d_l2_loss = tf.constant(0.0)
=self.d_l2_loss = torch.tensor(0.0)
3.losses = tf.nn.softmax_cross_entropy_with_logits(logits=d_scores, labels=self.d_input_y)
=losses = torch.nn.crossentropyloss(d_scores, d_input_y)
4.tf.reduce_mean(losses)
=torch.mean(losses)
5.embedded_chars = tf.nn.embedding_lookup(w_fe, feature_input + 1)
=embedded_chars = torch.index_select(w_fe, 0, feature_input + 1)
6.embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)
=embedded_chars_expanded = torch.unsqueeze(embedded_chars, -1)
7.h_pool = tf.concat(pooled_outputs, 3)
=h_pool = torch.cat(pooled_outputs, 3)
8.tf.transpose(matrix)
=matrix.transpose(1, 0)
9.tensorarray可以看做是具有動態size功能的tensor陣列。通常都是跟while_loop或map_fn結合使用。
10.tf.tensorarray簡單教程
ta.stack(name=none) 將tensorarray中元素疊起來當做乙個tensor輸出
ta.unstack(value, name=none) 可以看做是stack的反操作，輸入tensor，輸出乙個新的tensorarray物件
ta.write(index, value, name=none) 指定index位置寫入tensor
ta.read(index, name=none) 讀取指定index位置的tensor
11.torch.squeeze
=？？？？？不確定
ta.stack
12.torch.matmul
=tf.matmul
13.tf.expand_dims
=torch.unsqueeze
14.torch.split( torch.cat([torch.split(input_x, i, 1)[0], self.padding_array], 1),
self.sequence_length, 1)[0]
=有一點不一樣
tf.split(tf.concat([tf.split(input_x, [i, self.sequence_length - i], 1)[0], self.padding_array], 1),[self.sequence_length, i], 1)[0]
14.tf.squeeze()
=torch.squeeze
16，sub_goal = tf.nn.l2_normalize(sub_goal, 1)
=sub_goal = sklearn.preprocessing.normalize(sub_goal , norm='l2')
17.tf.log(tf.nn.softmax(）
=f.log_softmax（）
18.tf.multinomial(log_prob, 1)
=torch.multinomial(log_prob, 1, replacement=true)
19.tf.multiply
=*或者torch.mul
20.tf.one_hot
=？？？
onehot_encoder = onehotencoder(sparse=false)
onehot_encoder.fit_transform(next_token)
21.tf.reduce_sum（a, 0）
=a.sum(0)
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