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15 changed files
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3 additions
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54 deletions
1 | --9.385425355283757431e-01 9.385428909124332586e-01 | ||
2 | --8.245905442081006287e-01 8.245868110779478455e-01 | ||
3 | -1.506354362247568801e+00 -1.506354422782807623e+00 | ||
4 | --8.966897450368520595e-01 8.966885905296639869e-01 | ||
5 | --7.359716864356123933e-01 7.359695653336255639e-01 | ||
6 | --8.973836099857436244e-01 8.973827087061271301e-01 | ||
7 | --6.733239870313620923e-01 6.733260030121203110e-01 | ||
8 | -3.117922695462188809e+00 -3.117922475732808341e+00 | ||
9 | --7.482786237648014760e-01 7.482774501668290057e-01 |
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model/all_species/SOMncRNA/perf.txt
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100644 → 0
16.8 KB
... | @@ -26,10 +26,6 @@ class SLSOM(object): | ... | @@ -26,10 +26,6 @@ class SLSOM(object): |
26 | self.tf_object = som.tf_object | 26 | self.tf_object = som.tf_object |
27 | self.ulen = som.ulen | 27 | self.ulen = som.ulen |
28 | self.nb_label = nb_label | 28 | self.nb_label = nb_label |
29 | - self.k = k | ||
30 | - self.verbose=verbose | ||
31 | - if self.k is None: | ||
32 | - self.k = int(self.ulen / 2) | ||
33 | self.som = som | 29 | self.som = som |
34 | self.loss_type = loss_type | 30 | self.loss_type = loss_type |
35 | with self.tf_object.graph.as_default(): | 31 | with self.tf_object.graph.as_default(): |
... | @@ -43,25 +39,14 @@ class SLSOM(object): | ... | @@ -43,25 +39,14 @@ class SLSOM(object): |
43 | self.it_max = tf.placeholder(tf.int32) | 39 | self.it_max = tf.placeholder(tf.int32) |
44 | self.it = tf.Variable(0,dtype=tf.int32) | 40 | self.it = tf.Variable(0,dtype=tf.int32) |
45 | self.update_it = self.it.assign_add(1) | 41 | self.update_it = self.it.assign_add(1) |
46 | - #self.data = tf.placeholder(tf.float64,shape=[None,self.ulen]) | 42 | + self.data = self.som.sim2units(self.som.data2pred) |
47 | - if self.k < self.som.ulen: | ||
48 | - self.data = tf.map_fn( | ||
49 | - lambda x: tf.where(x >= tf.nn.top_k(x,self.k,sorted=True)[0][-1], x, tf.zeros_like(x)), | ||
50 | - self.som.sim2units(self.som.data2pred) | ||
51 | - ) | ||
52 | - else: | ||
53 | - self.data = self.som.sim2units(self.som.data2pred) | ||
54 | 43 | ||
55 | -# self.datapred = tf.map_fn( | 44 | + |
56 | -# lambda x: tf.where(x >= tf.nn.top_k(x,4,sorted=True)[0][-1], x, tf.zeros_like(x)), | ||
57 | -# self.data | ||
58 | -# ) | ||
59 | self.datapred = tf.one_hot( | 45 | self.datapred = tf.one_hot( |
60 | self.som.bmu_finder(self.som.data2pred,self.som.units), | 46 | self.som.bmu_finder(self.som.data2pred,self.som.units), |
61 | self.som.ulen, | 47 | self.som.ulen, |
62 | dtype=tf.float64 | 48 | dtype=tf.float64 |
63 | - ) #* self.data | 49 | + ) |
64 | -# self.datapred = self.data | ||
65 | 50 | ||
66 | self.data_size = tf.placeholder(tf.int32,shape=[1]) | 51 | self.data_size = tf.placeholder(tf.int32,shape=[1]) |
67 | self.lambda_penality = tf.placeholder(tf.float64,shape=[1]) | 52 | self.lambda_penality = tf.placeholder(tf.float64,shape=[1]) |
... | @@ -74,8 +59,6 @@ class SLSOM(object): | ... | @@ -74,8 +59,6 @@ class SLSOM(object): |
74 | 59 | ||
75 | def learning_rate(self,it): | 60 | def learning_rate(self,it): |
76 | return 1.0-tf.cast(self.it,tf.float64)/(tf.cast(self.it_max,tf.float64)) | 61 | return 1.0-tf.cast(self.it,tf.float64)/(tf.cast(self.it_max,tf.float64)) |
77 | -# def learning_rate(self,it): | ||
78 | -# return tf.cast(self.it_max,tf.float64)/(tf.cast(self.it,tf.float64)) | ||
79 | 62 | ||
80 | def save(self,path): | 63 | def save(self,path): |
81 | W = self.get_W() | 64 | W = self.get_W() | ... | ... |
-
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