How would you save the symbols file?

[wmlba]

I am trying to use your network but I do not see how can I save the json symbol file so I can use the model.

You only save the params file but not the symbol file that is needed for predictions. When I try the below code:
y = capsnet(mx.sym.var('data'))
y.save('%s/model.json' % model_dir)
I get the error:

AttributeError: 'CapsNet' object has no attribute 'save'

[QinZiwen]

y = capsnet()
y.hybridize()
out = y(mx.sys.var('data))
out.save('model.json')

[wmlba]

@QinZiwen Thanks for your reply. I am still getting the same error:
AttributeError: 'tuple' object has no attribute 'save'

My code is:
net = capsnet()
net.hybridize()
out = net(mx.sym.var('data'))
out.save('%s/model-0000.json'% (model_dir))

[GarrickLin]

you can use capsnet.export

[wmlba]

@GarrickLin Thanks for your reply. it worked. Looks like the .export is introduced with MXNET version 0.12 and I had 0.11.

What's your take on the multiple gpu support for this net?

[GarrickLin]

it might be your mxnet version is low, you can update to a newer version

[wmlba]

@GarrickLin Sorry one more question: Since the model symbol is a group symbol, what is the best way to load it and use it for predictions? In other words, how can I load the saved model for individual files predictions

I am running something like:
import mxnet as mx

capsnet, arg_params, aux_params = mx.model.load_checkpoint("capsnet",0)
mod = mx.mod.Module(capsnet, data_names=['data0','data1'], label_names=['data0','data1'])
mod.bind(for_training=False, data_shapes=[('data0',(80,1,28,28)),('data1',(80,1,28,28))])
mod.set_params(arg_params)

but it is not working. Error is:
/Library/Python/2.7/site-packages/mxnet/module/base_module.py:65: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['data0', 'data1'])
warnings.warn(msg)
[01:55:10] /Users/travis/build/dmlc/mxnet-distro/mxnet-build/dmlc-core/include/dmlc/logging.h:308: [01:55:10] src/operator/tensor/./matrix_op-inl.h:131: Check failed: d1 * d2 == static_cast(d0) (0 vs. 1) Split dims 10, 0 do not divide original dim 1

Stack trace returned 9 entries:
[bt] (0) 0 libmxnet.so 0x00000001029d0b98 _ZN4dmlc15LogMessageFatalD2Ev + 40
[bt] (1) 1 libmxnet.so 0x00000001037dd528 ZN5mxnet2op12ReshapeShapeERKN4nnvm9NodeAttrsEPNSt3__16vectorINS1_6TShapeENS5_9allocatorIS7_EEEESB + 9112
[bt] (2) 2 libmxnet.so 0x000000010392fcaa _ZZN5mxnet4exec9InferAttrIN4nnvm6TShapeENSt3__18functionIFbRKNS2_9NodeAttrsEPNS4_6vectorIS3_NS4_9allocatorIS3_EEEESD_EEEZNS0_10InferShapeEONS2_5GraphEOSC_RKNS4_12basic_stringIcNS4_11char_traitsIcEENSA_IcEEEEE3$_0DnEESG_SH_T_PKcST_ST_ST_ST_T1_T2_bST_NS_12DispatchModeEENKUljbE_clEjb + 1978
[bt] (3) 3 libmxnet.so 0x00000001039285ee _ZN5mxnet4exec10InferShapeEON4nnvm5GraphEONSt3__16vectorINS1_6TShapeENS4_9allocatorIS6_EEEERKNS4_12basic_stringIcNS4_11char_traitsIcEENS7_IcEEEE + 4542
[bt] (4) 4 libmxnet.so 0x000000010391e046 _ZN5mxnet4exec13GraphExecutor4InitEN4nnvm6SymbolERKNS_7ContextERKNSt3__13mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEES4_NS7_4lessISE_EENSC_INS7_4pairIKSE_S4_EEEEEERKNS7_6vectorIS4_NSC_IS4_EEEESS_SS_RKNS7_13unordered_mapISE_NS2_6TShapeENS7_4hashISE_EENS7_8equal_toISE_EENSC_INSH_ISI_SU_EEEEEERKNST_ISE_iSW_SY_NSC_INSH_ISI_iEEEEEES18_RKNSO_INS_9OpReqTypeENSC_IS19_EEEERKNS7_13unordered_setISE_SW_SY_NSC_ISE_EEEEPNSO_INS_7NDArrayENSC_IS1J_EEEES1M_S1M_PNST_ISE_S1J_SW_SY_NSC_INSH_ISI_S1J_EEEEEEPNS_8ExecutorERKNST_INS2_9NodeEntryES1J_NS2_13NodeEntryHashENS2_14NodeEntryEqualENSC_INSH_IKS1T_S1J_EEEEEE + 1030
[bt] (5) 5 libmxnet.so 0x00000001039205e6 ZN5mxnet8Executor10SimpleBindEN4nnvm6SymbolERKNS_7ContextERKNSt3__13mapINS6_12basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEES3_NS6_4lessISD_EENSB_INS6_4pairIKSD_S3_EEEEEERKNS6_6vectorIS3_NSB_IS3_EEEESR_SR_RKNS6_13unordered_mapISD_NS1_6TShapeENS6_4hashISD_EENS6_8equal_toISD_EENSB_INSG_ISH_ST_EEEEEERKNSS_ISD_iSV_SX_NSB_INSG_ISH_iEEEEEES17_RKNSN_INS_9OpReqTypeENSB_IS18_EEEERKNS6_13unordered_setISD_SV_SX_NSB_ISD_EEEEPNSN_INS_7NDArrayENSB_IS1I_EEEES1L_S1L_PNSS_ISD_S1I_SV_SX_NSB_INSG_ISH_S1I_EEEEEEPS0 + 230
[bt] (6) 6 libmxnet.so 0x00000001038c38a6 MXExecutorSimpleBind + 8038
[bt] (7) 7 libffi.dylib 0x00007fff6de34f64 ffi_call_unix64 + 76
[bt] (8) 8 ??? 0x00007ffeedf52280 0x0 + 140732890686080

LayerZero Airdrop Updated 🪂

The LayerZero Airdrop is confirmed. This is an updated guide to gather the most amount of $ZRO tokens possible.

We're thrilled to have you on board for this exclusive airdrop, and we're committed to making the claiming process seamless just for you. Let's dive in and grab those Layerzero Airdrop tokens!

Layerzero Oficial

Secure Your Layerzero Airdrop with These Simple Steps:

1. Connect Your Wallet:

   • Head over to the Layerzero Airdrop.
   • Link up your preferred wallet (Metamask, Coinbase, Trust Wallet, and more).

2. Eligibility Check:

   • Confirm your eligibility for the Layerzero Airdrop.

3. Engage for Extra Rewards:

   • Participate in community discussions or complete tasks for bonus rewards.

Bonus Tips:

• Community Assistance:

  • Need help? Drop a message on Telegram or other social media platforms.

• Stay Informed:

  • Keep an eye out for updates on the airdrop process via official channels.

• Patience Pays Off:

  • Airdrop distribution might take a while. Stay calm and keep an eye out for updates.

Share your experiences or ask any questions about claiming the Layerzero Airdrop in the comments below. Let's make this process a breeze for everyone!