1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
|
path: "tensorflow.keras.losses"
tf_module {
member_method {
name: "KLD"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "MAE"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "MAPE"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "MSE"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "MSLE"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "binary_crossentropy"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "categorical_crossentropy"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "categorical_hinge"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "cosine"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "cosine_proximity"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "deserialize"
argspec: "args=[\'name\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "get"
argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "hinge"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "kld"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "kullback_leibler_divergence"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "logcosh"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mae"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mape"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mean_absolute_error"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mean_absolute_percentage_error"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mean_squared_error"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mean_squared_logarithmic_error"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "mse"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "msle"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "poisson"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "serialize"
argspec: "args=[\'loss\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "sparse_categorical_crossentropy"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "squared_hinge"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
}
|
