MLPClassifier - ~m-label - default - {‘zipmap’: False}#

Fitted on a problem type ~m-label (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.neural_network._multilayer_perceptron.MLPClassifier'>={'zipmap': False}.

MLPClassifier(random_state=0)

index

0

skl_nop

1

onx_size

4048

onx_nnodes

10

onx_ninits

4

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

14

onx_ai.onnx.ml

1

onx_op_Cast

2

onx_op_Identity

1

onx_size_optim

3967

onx_nnodes_optim

9

onx_ninits_optim

4

fit_classes_.shape

3

fit_best_loss_.shape

1

fit_loss_.shape

1
%0 X X float((0, 4)) Cast Cast (Cast) to=1 X->Cast label label int64((0,)) probabilities probabilities float((0, 3)) coefficient coefficient float32((4, 100)) [[-5.23032025e-02  5.47090434e-02  8.11574608e-02 ... MatMul MatMul (MatMul) coefficient->MatMul intercepts intercepts float32((1, 100)) [[-0.1921233   0.12601851 -0.07710242  0.40619114 ... Add Add (Add) intercepts->Add coefficient1 coefficient1 float32((100, 3)) [[ 2.69775018e-02 -2.53212750e-01  1.10127583e-01]... MatMul1 MatMul (MatMul1) coefficient1->MatMul1 intercepts1 intercepts1 float32((1, 3)) [[-0.20008506  0.21500102 -0.1164256 ]] Add1 Add (Add1) intercepts1->Add1 cast_input cast_input cast_input->MatMul Cast->cast_input mul_result mul_result mul_result->Add MatMul->mul_result add_result add_result Relu Relu (Relu) add_result->Relu Add->add_result next_activations next_activations next_activations->MatMul1 Relu->next_activations mul_result1 mul_result1 mul_result1->Add1 MatMul1->mul_result1 add_result1 add_result1 Relu1 Sigmoid (Relu1) add_result1->Relu1 Add1->add_result1 out_activations_result out_activations_result Identity Identity (Identity) out_activations_result->Identity N8 Binarizer (N8) threshold=0.5 out_activations_result->N8 Relu1->out_activations_result Identity->probabilities binariser_output binariser_output Cast1 Cast (Cast1) to=7 binariser_output->Cast1 N8->binariser_output Cast1->label

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MLPClassifier - m-cl - default - {‘zipmap’: False}

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MLPRegressor - b-reg - default -