statestream/examples/test_core_client_trainvaltest.st_graph at master · boschresearch/statestream · GitHub

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
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
# -*- coding: utf-8 -*-
# Copyright (c) 2017 - for information on the respective copyright owner
# see the NOTICE file and/or the repository https://github.com/boschresearch/statestream
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


name: test_core_client_trainvaltest
agents: 256
core_clients:
    lr_scheduler:
        type: lr_scheduler
        plast: h3_loss
        schedule: exponential_decay
        decay: 0.9995
    trainvaltest:
        type: trainvaltest
        target_folder: /local/data/sessions/
        main_interface: mnist
        main_plasticity: h3_loss
        test_after_epochs: 12
        validate_after_epochs: 1
neuron_pools:
    image:
        shape: [3, 28, 28]
        noise: normal
        noise_mean: 0.0
        noise_std: 0.05
        tags: [sensor]
    label:
        shape: [10, 1, 1]
        tags: [sensor]
    hidden1:
        shape: [16, 14, 14]
        act: relu
        tags: [hidden]
        device: gpu:0
    hidden2:
        shape: [32, 7, 7]
        act: relu
        tags: [hidden]
        dropout: 0.1
        device: gpu:0
    hidden3:
        shape: [128, 1, 1]
        act: relu
        tags: [hidden]
        dropout: 0.25
        device: gpu:0
    prediction:
        shape: [10, 1, 1]
        act: softmax
        tags: [prediction]
synapse_pools:
    img_hidden1:
        source: [[image]]
        target: hidden1
        rf: [[5]]
    hidden1_2:
        source: [[hidden1]]
        target: hidden2
        rf: [[5]]
    hidden2_3:
        source: [[hidden2]]
        target: hidden3
    hidden1_pred:
        source: [[hidden1]]
        target: prediction
    hidden2_pred:
        source: [[hidden2]]
        target: prediction
    hidden3_pred:
        source:
        - [hidden3]
        target: prediction
plasticities:
    h3_loss:
        type: loss
        loss_function: negloglikelihood
        source: prediction
        device: gpu:0
        source_t: 4
        target: label
        target_t: 0
        optimizer: rmsprop
        lr: 1e-4
        rho: 0.9
        parameter:
        - [sp, img_hidden1, W_0_0]
        - [sp, hidden1_2, W_0_0]
        - [sp, hidden2_3, W_0_0]
        - [sp, hidden3_pred, W_0_0]
    h2_loss:
        type: loss
        loss_function: negloglikelihood
        source: prediction
        device: gpu:0
        source_t: 3
        target: label
        target_t: 0
        optimizer: rmsprop
        lr: 5e-4
        rho: 0.9
        parameter:
        - [sp, img_hidden1, W_0_0]
        - [sp, hidden1_2, W_0_0]
        - [sp, hidden2_pred, W_0_0]
    h1_loss:
        type: loss
        loss_function: negloglikelihood
        source: prediction
        device: gpu:0
        source_t: 2
        target: label
        target_t: 0
        optimizer: rmsprop
        lr: 5e-4
        rho: 0.9
        parameter:
        - [sp, img_hidden1, W_0_0]
        - [sp, hidden1_pred, W_0_0]
interfaces:
    mnist:
        type: mnist
        in: [mnist_pred]
        out: [mnist_image, mnist_label]
        remap:
            mnist_image: image
            mnist_label: label
            mnist_pred: prediction
        source_file: /opt/dl/data/mnist.pkl.gz
        fading: 0
        train samples: 50000
        valid samples: 10000
        test samples: 10000
        conf-mat window: 8
        conf-mat mean over: 1