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Update nnframes.md (#7808)

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Xin Qiu 2023-03-08 15:29:00 +08:00 committed by GitHub
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commit 633668c882
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docs/readthedocs/source/doc/DLlib/Overview/nnframes.md
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@ -131,7 +131,7 @@ This example trains a model with 3 inputs. And users can use VectorAssembler fro
from bigdl.dllib.utils.common import * from bigdl.dllib.utils.common import *
from bigdl.dllib.nnframes.nn_classifier import * from bigdl.dllib.nnframes.nn_classifier import *
from bigdl.dllib.feature.common import * from bigdl.dllib.feature.common import *
from bigdl.dllib.keras.objectives import CategoricalCrossEntropy from bigdl.dllib.keras.objectives import SparseCategoricalCrossEntropy
from bigdl.dllib.keras.optimizers import Adam from bigdl.dllib.keras.optimizers import Adam
from bigdl.dllib.keras.layers import * from bigdl.dllib.keras.layers import *
from bigdl.dllib.nncontext import * from bigdl.dllib.nncontext import *
@ -147,9 +147,9 @@ spark = SparkSession\
.getOrCreate() .getOrCreate()
df = spark.createDataFrame( df = spark.createDataFrame(
[(1, 35, 109.0, Vectors.dense([2.0, 5.0, 0.5, 0.5]), 1.0), [(1, 35, 109.0, Vectors.dense([2.0, 5.0, 0.5, 0.5]), 0.0),
(2, 58, 2998.0, Vectors.dense([4.0, 10.0, 0.5, 0.5]), 2.0), (2, 58, 2998.0, Vectors.dense([4.0, 10.0, 0.5, 0.5]), 1.0),
(3, 18, 123.0, Vectors.dense([3.0, 15.0, 0.5, 0.5]), 1.0)], (3, 18, 123.0, Vectors.dense([3.0, 15.0, 0.5, 0.5]), 0.0)],
["user", "age", "income", "history", "label"]) ["user", "age", "income", "history", "label"])
assembler = VectorAssembler( assembler = VectorAssembler(
@ -171,7 +171,7 @@ merged = merge([flatten, dense1, gru], mode="concat")
zy = Dense(2)(merged) zy = Dense(2)(merged)
zmodel = Model([x1, x2, x3], zy) zmodel = Model([x1, x2, x3], zy)
criterion = CategoricalCrossEntropy() criterion = SparseCategoricalCrossEntropy()
classifier = NNEstimator(zmodel, criterion, [[1], [2], [2, 2]]) \ classifier = NNEstimator(zmodel, criterion, [[1], [2], [2, 2]]) \
.setOptimMethod(Adam()) \ .setOptimMethod(Adam()) \
.setLearningRate(0.1)\ .setLearningRate(0.1)\