提交 08d5d2e9 编写于 作者: C Chen Weihang

08 Style: polish formula and print format

上级 d4414cbb
......@@ -85,7 +85,7 @@
2. 将$z_{i+1}$通过`softmax`归一化,得到目标语言序列的第$i+1$个单词的概率分布$p_{i+1}$。概率分布公式如下:
$$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
$$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
其中$W_sz_{i+1}+b_z$是对每个可能的输出单词进行打分,再用softmax归一化就可以得到第$i+1$个词的概率$p_{i+1}$。
......@@ -132,6 +132,7 @@
下面我们开始根据输入数据的形式配置模型。首先引入所需的库函数以及定义全局变量。
```python
from __future__ import print_function
import contextlib
import numpy as np
......@@ -437,10 +438,13 @@ for data in test_data():
result_scores = np.array(results[1])
print("Original sentence:")
print(" ".join([src_dict[w] for w in feed_data[0][0]]))
print("Translated sentence:")
print(" ".join([trg_dict[w] for w in result_ids]))
print("Corresponding score: ", result_scores)
print(" ".join([src_dict[w] for w in feed_data[0][0][1:-1]]))
print("Translated score and sentence:")
for i in xrange(beam_size):
start_pos = result_ids_lod[1][i] + 1
end_pos = result_ids_lod[1][i+1]
print("%d\t%.4f\t%s\n" % (i+1, result_scores[end_pos-1],
" ".join([trg_dict[w] for w in result_ids[start_pos:end_pos]])))
break
```
......
......@@ -114,7 +114,7 @@ The goal of the decoder is to maximize the probability of the next correct word
2. Calculate the probability $p_{i+1}$ for the $i+1$-th word in the target language sequence by normalizing $z_{i+1}$ using `softmax` as follows
$$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
$$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
where $W_sz_{i+1}+b_z$ scores each possible words and is then normalized via softmax to produce the probability $p_{i+1}$ for the $i+1$-th word.
......@@ -169,6 +169,7 @@ This subset has 193319 instances of training data and 6003 instances of test dat
Our program starts with importing necessary packages and initializing some global variables:
```python
from __future__ import print_function
import contextlib
import numpy as np
......@@ -485,10 +486,13 @@ for data in test_data():
result_scores = np.array(results[1])
print("Original sentence:")
print(" ".join([src_dict[w] for w in feed_data[0][0]]))
print("Translated sentence:")
print(" ".join([trg_dict[w] for w in result_ids]))
print("Corresponding score: ", result_scores)
print(" ".join([src_dict[w] for w in feed_data[0][0][1:-1]]))
print("Translated score and sentence:")
for i in xrange(beam_size):
start_pos = result_ids_lod[1][i] + 1
end_pos = result_ids_lod[1][i+1]
print("%d\t%.4f\t%s\n" % (i+1, result_scores[end_pos-1],
" ".join([trg_dict[w] for w in result_ids[start_pos:end_pos]])))
break
```
......
......@@ -127,7 +127,7 @@
2. 将$z_{i+1}$通过`softmax`归一化,得到目标语言序列的第$i+1$个单词的概率分布$p_{i+1}$。概率分布公式如下:
$$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
$$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
其中$W_sz_{i+1}+b_z$是对每个可能的输出单词进行打分再用softmax归一化就可以得到第$i+1$个词的概率$p_{i+1}$。
......@@ -174,6 +174,7 @@
下面我们开始根据输入数据的形式配置模型。首先引入所需的库函数以及定义全局变量。
```python
from __future__ import print_function
import contextlib
import numpy as np
......@@ -479,10 +480,13 @@ for data in test_data():
result_scores = np.array(results[1])
print("Original sentence:")
print(" ".join([src_dict[w] for w in feed_data[0][0]]))
print("Translated sentence:")
print(" ".join([trg_dict[w] for w in result_ids]))
print("Corresponding score: ", result_scores)
print(" ".join([src_dict[w] for w in feed_data[0][0][1:-1]]))
print("Translated score and sentence:")
for i in xrange(beam_size):
start_pos = result_ids_lod[1][i] + 1
end_pos = result_ids_lod[1][i+1]
print("%d\t%.4f\t%s\n" % (i+1, result_scores[end_pos-1],
" ".join([trg_dict[w] for w in result_ids[start_pos:end_pos]])))
break
```
......
......@@ -156,7 +156,7 @@ The goal of the decoder is to maximize the probability of the next correct word
2. Calculate the probability $p_{i+1}$ for the $i+1$-th word in the target language sequence by normalizing $z_{i+1}$ using `softmax` as follows
$$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
$$p\left ( u_{i+1}|u_{<i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
where $W_sz_{i+1}+b_z$ scores each possible words and is then normalized via softmax to produce the probability $p_{i+1}$ for the $i+1$-th word.
......@@ -211,6 +211,7 @@ This subset has 193319 instances of training data and 6003 instances of test dat
Our program starts with importing necessary packages and initializing some global variables:
```python
from __future__ import print_function
import contextlib
import numpy as np
......@@ -527,10 +528,13 @@ for data in test_data():
result_scores = np.array(results[1])
print("Original sentence:")
print(" ".join([src_dict[w] for w in feed_data[0][0]]))
print("Translated sentence:")
print(" ".join([trg_dict[w] for w in result_ids]))
print("Corresponding score: ", result_scores)
print(" ".join([src_dict[w] for w in feed_data[0][0][1:-1]]))
print("Translated score and sentence:")
for i in xrange(beam_size):
start_pos = result_ids_lod[1][i] + 1
end_pos = result_ids_lod[1][i+1]
print("%d\t%.4f\t%s\n" % (i+1, result_scores[end_pos-1],
" ".join([trg_dict[w] for w in result_ids[start_pos:end_pos]])))
break
```
......
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
......@@ -187,10 +188,14 @@ def decode_main(use_cuda):
result_scores = np.array(results[1])
print("Original sentence:")
print(" ".join([src_dict[w] for w in feed_data[0][0]]))
print("Translated sentence:")
print(" ".join([trg_dict[w] for w in result_ids]))
print("Corresponding score: ", result_scores)
print(" ".join([src_dict[w] for w in feed_data[0][0][1:-1]]))
print("Translated score and sentence:")
for i in xrange(beam_size):
start_pos = result_ids_lod[1][i] + 1
end_pos = result_ids_lod[1][i + 1]
print("%d\t%.4f\t%s\n" % (
i + 1, result_scores[end_pos - 1],
" ".join([trg_dict[w] for w in result_ids[start_pos:end_pos]])))
break
......
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