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Fix dllib nnframes doc (#7806)

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* Update nnframes.md

* Update nnframes.md

* Update nnframes.md

* Update nnframes.md

* Update nnframes.md

* Update nnframes.md

* Update nnframes.md
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Xin Qiu 2023-03-08 15:13:27 +08:00 committed by GitHub
parent 57125dfcd4
commit a1a1f914bb
1 changed files with 38 additions and 25 deletions
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docs/readthedocs/source/doc/DLlib/Overview/nnframes.md
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@ -127,7 +127,20 @@ res = nnModel.transform(df)
This example trains a model with 3 inputs. And users can use VectorAssembler from Spark MLlib to combine different fields. With the specified sizes for each model input, NNEstiamtor and NNClassifer will split the input features data and send tensors to corresponding inputs.
```python
sparkConf = init_spark_conf().setAppName("testNNClassifer").setMaster('local[1]')
from bigdl.dllib.utils.common import *
from bigdl.dllib.nnframes.nn_classifier import *
from bigdl.dllib.feature.common import *
from bigdl.dllib.keras.objectives import CategoricalCrossEntropy
from bigdl.dllib.keras.optimizers import Adam
from bigdl.dllib.keras.layers import *
from bigdl.dllib.nncontext import *
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
sparkConf = init_spark_conf().setAppName("testNNEstimator").setMaster('local[1]')
sc = init_nncontext(sparkConf)
spark = SparkSession\
.builder\
@ -145,21 +158,21 @@ assembler = VectorAssembler(
df = assembler.transform(df)
x1 = ZLayer.Input(shape=(1,))
x2 = ZLayer.Input(shape=(2,))
x3 = ZLayer.Input(shape=(2, 2,))
x1 = Input(shape=(1,))
x2 = Input(shape=(2,))
x3 = Input(shape=(2, 2,))
user_embedding = ZLayer.Embedding(5, 10)(x1)
flatten = ZLayer.Flatten()(user_embedding)
dense1 = ZLayer.Dense(2)(x2)
gru = ZLayer.LSTM(4, input_shape=(2, 2))(x3)
user_embedding = Embedding(5, 10)(x1)
flatten = Flatten()(user_embedding)
dense1 = Dense(2)(x2)
gru = LSTM(4, input_shape=(2, 2))(x3)
merged = ZLayer.merge([flatten, dense1, gru], mode="concat")
zy = ZLayer.Dense(2)(merged)
merged = merge([flatten, dense1, gru], mode="concat")
zy = Dense(2)(merged)
zmodel = ZModel([x1, x2, x3], zy)
criterion = ZooClassNLLCriterion()
classifier = NNClassifier(zmodel, criterion, [[1], [2], [2, 2]]) \
zmodel = Model([x1, x2, x3], zy)
criterion = CategoricalCrossEntropy()
classifier = NNEstimator(zmodel, criterion, [[1], [2], [2, 2]]) \
.setOptimMethod(Adam()) \
.setLearningRate(0.1)\
.setBatchSize(2) \
@ -274,28 +287,28 @@ dlModel.transform(df).show(false)
**Python Example:**
```python
from bigdl.nn.layer import *
from bigdl.nn.criterion import *
from bigdl.util.common import *
from bigdl.dlframes.dl_classifier import *
from bigdl.dllib.nn.layer import *
from bigdl.dllib.nn.criterion import *
from bigdl.dllib.utils.common import *
from bigdl.dllib.nnframes.nn_classifier import *
from pyspark.sql.types import *
#Logistic Regression with BigDL layers and NNClassifier
model = Sequential().add(Linear(2, 2)).add(LogSoftMax())
criterion = ZooClassNLLCriterion()
criterion = ClassNLLCriterion()
estimator = NNClassifier(model, criterion, [2]).setBatchSize(4).setMaxEpoch(10)
data = sc.parallelize([
((0.0, 1.0), [1.0]),
((1.0, 0.0), [2.0]),
((0.0, 1.0), [1.0]),
((1.0, 0.0), [2.0])])
((0.0, 1.0), 1.0),
((1.0, 0.0), 2.0),
((0.0, 1.0), 1.0),
((1.0, 0.0), 2.0)])
schema = StructType([
StructField("features", ArrayType(DoubleType(), False), False),
StructField("label", ArrayType(DoubleType(), False), False)])
df = sqlContext.createDataFrame(data, schema)
StructField("label", DoubleType(), False)])
df = spark.createDataFrame(data, schema)
dlModel = estimator.fit(df)
dlModel.transform(df).show(False)
res = dlModel.transform(df).collect()
```
### 2.4 NNClassifierModel ##