Flow
mxnet.gluon.nn.basic_layers.HybridSequential.e59b914b

mxnet.gluon.nn.basic_layers.HybridSequential.e59b914b

Visibility: public Uploaded 03-06-2019 by Adrian-Stefan Mares mxnet==1.5.0 numpy>=1.6.1 scipy>=0.9 2 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • mxnet mxnet_1.5.0 openml-python python
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Automatically created scikit-learn flow.

Parameters

00_datadefault: {"inputs": [], "name": "data", "op": "null"}
01_hybridsequential0_batchnorm0_gammadefault: {"attrs": {"__dtype__": "0", "__init__": "ones", "__lr_mult__": "1.0", "__shape__": "(0,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_batchnorm0_gamma", "op": "null"}
02_hybridsequential0_batchnorm0_betadefault: {"attrs": {"__dtype__": "0", "__init__": "zeros", "__lr_mult__": "1.0", "__shape__": "(0,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_batchnorm0_beta", "op": "null"}
03_hybridsequential0_batchnorm0_running_meandefault: {"attrs": {"__dtype__": "0", "__init__": "zeros", "__lr_mult__": "1.0", "__shape__": "(0,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_batchnorm0_running_mean", "op": "null"}
04_hybridsequential0_batchnorm0_running_vardefault: {"attrs": {"__dtype__": "0", "__init__": "ones", "__lr_mult__": "1.0", "__shape__": "(0,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_batchnorm0_running_var", "op": "null"}
05_hybridsequential0_batchnorm0_fwddefault: {"attrs": {"axis": "1", "eps": "1e-05", "fix_gamma": "False", "momentum": "0.9", "use_global_stats": "False"}, "inputs": [[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 1], [4, 0, 1]], "name": "hybridsequential0_batchnorm0_fwd", "op": "BatchNorm"}
06_hybridsequential0_dense0_weightdefault: {"attrs": {"__dtype__": "0", "__lr_mult__": "1.0", "__shape__": "(256, 0)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_dense0_weight", "op": "null"}
07_hybridsequential0_dense0_biasdefault: {"attrs": {"__dtype__": "0", "__init__": "zeros", "__lr_mult__": "1.0", "__shape__": "(256,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_dense0_bias", "op": "null"}
08_hybridsequential0_dense0_fwddefault: {"attrs": {"flatten": "True", "no_bias": "False", "num_hidden": "256"}, "inputs": [[5, 0, 0], [6, 0, 0], [7, 0, 0]], "name": "hybridsequential0_dense0_fwd", "op": "FullyConnected"}
09_hybridsequential0_dense0_relu_fwddefault: {"attrs": {"act_type": "relu"}, "inputs": [[8, 0, 0]], "name": "hybridsequential0_dense0_relu_fwd", "op": "Activation"}
10_hybridsequential0_dropout0_fwddefault: {"attrs": {"axes": "()", "cudnn_off": "False", "p": "0.25"}, "inputs": [[9, 0, 0]], "name": "hybridsequential0_dropout0_fwd", "op": "Dropout"}
11_hybridsequential0_dense1_weightdefault: {"attrs": {"__dtype__": "0", "__lr_mult__": "1.0", "__shape__": "(256, 0)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_dense1_weight", "op": "null"}
12_hybridsequential0_dense1_biasdefault: {"attrs": {"__dtype__": "0", "__init__": "zeros", "__lr_mult__": "1.0", "__shape__": "(256,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_dense1_bias", "op": "null"}
13_hybridsequential0_dense1_fwddefault: {"attrs": {"flatten": "True", "no_bias": "False", "num_hidden": "256"}, "inputs": [[10, 0, 0], [11, 0, 0], [12, 0, 0]], "name": "hybridsequential0_dense1_fwd", "op": "FullyConnected"}
14_hybridsequential0_dense1_relu_fwddefault: {"attrs": {"act_type": "relu"}, "inputs": [[13, 0, 0]], "name": "hybridsequential0_dense1_relu_fwd", "op": "Activation"}
15_hybridsequential0_dropout1_fwddefault: {"attrs": {"axes": "()", "cudnn_off": "False", "p": "0.35"}, "inputs": [[14, 0, 0]], "name": "hybridsequential0_dropout1_fwd", "op": "Dropout"}
16_hybridsequential0_dense2_weightdefault: {"attrs": {"__dtype__": "0", "__lr_mult__": "1.0", "__shape__": "(2, 0)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_dense2_weight", "op": "null"}
17_hybridsequential0_dense2_biasdefault: {"attrs": {"__dtype__": "0", "__init__": "zeros", "__lr_mult__": "1.0", "__shape__": "(2,)", "__storage_type__": "0", "__wd_mult__": "1.0"}, "inputs": [], "name": "hybridsequential0_dense2_bias", "op": "null"}
18_hybridsequential0_dense2_fwddefault: {"attrs": {"flatten": "True", "no_bias": "False", "num_hidden": "2"}, "inputs": [[15, 0, 0], [16, 0, 0], [17, 0, 0]], "name": "hybridsequential0_dense2_fwd", "op": "FullyConnected"}
miscdefault: {"arg_nodes": [0, 1, 2, 3, 4, 6, 7, 11, 12, 16, 17], "attrs": {"mxnet_version": ["int", 10500]}, "heads": [[18, 0, 0]], "node_row_ptr": [0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 22, 23]}

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table