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  1. OUTPUTS/cifnet-18-tiny-lr0.01-attention/all_results.json +1 -0
  2. OUTPUTS/cifnet-18-tiny-lr0.01-attention/config.json +67 -0
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  12. OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/image_classification_no_trainer/1712011004.1220295/hparams.yml +30 -0
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  14. OUTPUTS/cifnet-18-tiny-lr0.01-bottleneck/image_classification_no_trainer/events.out.tfevents.1712011004.ids-ws-06.3571203.0 +3 -0
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  17. OUTPUTS/cifnet-18-tiny-lr0.1-baseline/all_results.json +1 -0
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  29. OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/config.json +67 -0
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  31. OUTPUTS/cifnet-18-tiny_attention--lr0.001--prenorm/image_classification_no_trainer/1712166496.724743/hparams.yml +30 -0
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171
+ Conv2d-73 [4, 128, 28, 28] 147,456
172
+ BatchNorm2d-74 [4, 128, 28, 28] 256
173
+ SiLU-75 [4, 128, 28, 28] 0
174
+ CifNetConvLayer-76 [4, 128, 28, 28] 0
175
+ Identity-77 [4, 128, 28, 28] 0
176
+ CifNetSelfAttentionLayer-78 [4, 128, 28, 28] 0
177
+ Conv2d-79 [4, 128, 28, 28] 147,456
178
+ BatchNorm2d-80 [4, 128, 28, 28] 256
179
+ SiLU-81 [4, 128, 28, 28] 0
180
+ CifNetConvLayer-82 [4, 128, 28, 28] 0
181
+ CifNetRMSNorm-83 [4, 28, 28, 128] 128
182
+ Conv2d-84 [4, 32, 28, 28] 4,128
183
+ Conv2d-85 [4, 32, 28, 28] 4,128
184
+ Conv2d-86 [4, 32, 28, 28] 4,128
185
+ Conv2d-87 [4, 128, 28, 28] 4,224
186
+ CifNetSelfAttention-88 [4, 128, 28, 28] 0
187
+ SiLU-89 [4, 128, 28, 28] 0
188
+ Conv2d-90 [4, 128, 28, 28] 147,456
189
+ BatchNorm2d-91 [4, 128, 28, 28] 256
190
+ SiLU-92 [4, 128, 28, 28] 0
191
+ CifNetConvLayer-93 [4, 128, 28, 28] 0
192
+ Identity-94 [4, 128, 28, 28] 0
193
+ CifNetSelfAttentionLayer-95 [4, 128, 28, 28] 0
194
+ CifNetStage-96 [4, 128, 28, 28] 0
195
+ Conv2d-97 [4, 128, 28, 28] 147,456
196
+ BatchNorm2d-98 [4, 128, 28, 28] 256
197
+ SiLU-99 [4, 128, 28, 28] 0
198
+ CifNetConvLayer-100 [4, 128, 28, 28] 0
199
+ CifNetRMSNorm-101 [4, 28, 28, 128] 128
200
+ Conv2d-102 [4, 32, 28, 28] 4,128
201
+ Conv2d-103 [4, 32, 28, 28] 4,128
202
+ Conv2d-104 [4, 32, 28, 28] 4,128
203
+ Conv2d-105 [4, 128, 28, 28] 4,224
204
+ CifNetSelfAttention-106 [4, 128, 28, 28] 0
205
+ SiLU-107 [4, 128, 28, 28] 0
206
+ Conv2d-108 [4, 128, 28, 28] 147,456
207
+ BatchNorm2d-109 [4, 128, 28, 28] 256
208
+ SiLU-110 [4, 128, 28, 28] 0
209
+ CifNetConvLayer-111 [4, 128, 28, 28] 0
210
+ Identity-112 [4, 128, 28, 28] 0
211
+ CifNetSelfAttentionLayer-113 [4, 128, 28, 28] 0
212
+ Conv2d-114 [4, 128, 28, 28] 147,456
213
+ BatchNorm2d-115 [4, 128, 28, 28] 256
214
+ SiLU-116 [4, 128, 28, 28] 0
215
+ CifNetConvLayer-117 [4, 128, 28, 28] 0
216
+ CifNetRMSNorm-118 [4, 28, 28, 128] 128
217
+ Conv2d-119 [4, 32, 28, 28] 4,128
218
+ Conv2d-120 [4, 32, 28, 28] 4,128
219
+ Conv2d-121 [4, 32, 28, 28] 4,128
220
+ Conv2d-122 [4, 128, 28, 28] 4,224
221
+ CifNetSelfAttention-123 [4, 128, 28, 28] 0
222
+ SiLU-124 [4, 128, 28, 28] 0
223
+ Conv2d-125 [4, 128, 28, 28] 147,456
224
+ BatchNorm2d-126 [4, 128, 28, 28] 256
225
+ SiLU-127 [4, 128, 28, 28] 0
226
+ CifNetConvLayer-128 [4, 128, 28, 28] 0
227
+ Identity-129 [4, 128, 28, 28] 0
228
+ CifNetSelfAttentionLayer-130 [4, 128, 28, 28] 0
229
+ CifNetStage-131 [4, 128, 28, 28] 0
230
+ CifNetEncoder-132 [[-1, 128, 28, 28]] 0
231
+ AdaptiveAvgPool2d-133 [4, 128, 1, 1] 0
232
+ CifNetModel-134 [[-1, 128, 28, 28], [-1, 128, 1, 1]] 0
233
+ Flatten-135 [4, 128] 0
234
+ Linear-136 [4, 10] 1,290
235
+ ================================================================
236
+ Total params: 2,130,666
237
+ Trainable params: 2,130,666
238
+ Non-trainable params: 0
239
+ ----------------------------------------------------------------
240
+ Input size (MB): 2.30
241
+ Forward/backward pass size (MB): 542.07
242
+ Params size (MB): 8.13
243
+ Estimated Total Size (MB): 552.50
244
+ ----------------------------------------------------------------