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SparkFlow example
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| from sparkflow.graph_utils import build_graph | |
| from sparkflow.tensorflow_async import SparkAsyncDL | |
| import tensorflow as tf | |
| from pyspark.ml.feature import VectorAssembler, OneHotEncoder | |
| from pyspark.ml.pipeline import Pipeline | |
| #simple tensorflow network | |
| def small_model(): | |
| x = tf.placeholder(tf.float32, shape=[None, 784], name='x') | |
| y = tf.placeholder(tf.float32, shape=[None, 10], name='y') | |
| layer1 = tf.layers.dense(x, 256, activation=tf.nn.relu) | |
| layer2 = tf.layers.dense(layer1, 256, activation=tf.nn.relu) | |
| out = tf.layers.dense(layer2, 10) | |
| z = tf.argmax(out, 1, name='out') | |
| loss = tf.losses.softmax_cross_entropy(y, out) | |
| return loss | |
| df = spark.read.option("inferSchema", "true").csv('mnist_train.csv') | |
| #convert graph to json | |
| tensorflow_graph = build_graph(small_model) | |
| #Assemble and one hot encode | |
| va = VectorAssembler(inputCols=df.columns[1:785], outputCol='features') | |
| encoded = OneHotEncoder(inputCol='_c0', outputCol='labels', dropLast=False) | |
| spark_model = SparkAsyncDL( | |
| inputCol='features', | |
| tensorflowGraph=tensorflow_graph, | |
| tfInput='x:0', | |
| tfLabel='y:0', | |
| tfOutput='out:0', | |
| tfLearningRate=.001, | |
| iters=20 | |
| ) | |
| p = Pipeline(stages=[va, encoded, spark_model]).fit(df) |
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