Added missing parameters in existing layers #88
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utsavgarg
changed the title
…cale Layers * Added parameters present in either Caffe or Keras * Updated unit tests for these new parameters
virajprabhu
requested changes
Jul 12, 2017
ide/static/js/data.js
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| weight_filler: { | ||
| name: 'Weight filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
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Separate by framework and add comments
ide/static/js/data.js
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| required: false | ||
| }, | ||
| bias_filler: { | ||
| name: 'Bias filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
ide/static/js/data.js
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| }, | ||
| weight_filler: { | ||
| name: 'Weight filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
ide/static/js/data.js
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| required: false | ||
| }, | ||
| bias_filler: { | ||
| name: 'Bias filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
ide/static/js/data.js
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| @@ -761,14 +874,18 @@ export default { | |||
| name: 'Weight filler', | |||
| value: 'constant', | |||
| type: 'select', | |||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | |||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', | |||
ide/static/js/data.js
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| required: false | ||
| }, | ||
| bias_filler: { | ||
| name: 'Bias filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
ide/static/js/data.js
Outdated
| weight_filler: { | ||
| name: 'Weight filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear'], | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
ide/static/js/data.js
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| name: 'Filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
ide/static/js/data.js
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| name: 'Bias Filler', | ||
| value: 'constant', | ||
| type: 'select', | ||
| options: ['constant', 'gaussian', 'positive_unitball', 'uniform', 'xavier', 'msra', 'bilinear', 'Zeros', 'Ones', 'Constant', 'RandomNormal', |
| eps = 0.0001 # In Keras the max epsilon allowed in 1e-5 | ||
| moving_mean_initializer = layer['params']['moving_mean_initializer'] | ||
| moving_variance_initializer = layer['params']['moving_variance_initializer'] | ||
| if (nextLayer['info']['type'] == 'Scale'): |
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I have explained it in export_json.py
virajprabhu
reviewed
Jul 12, 2017
ide/static/js/data.js
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| type: 'number', | ||
| required: false | ||
| }, | ||
| weight_filler: { // Keras(kernel_initializer) |
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Change this to "Maps to: kernel_initializer (Keras)"? Otherwise it might imply that this is a Keras only layer. Follow this convention everywhere.
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Added union of parameters from Keras and Caffe for Conv layer