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fix pipeline example; delete commented test #530

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Jul 27, 2020
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fix pipeline example; delete commented test #530

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6 changes: 3 additions & 3 deletions examples/mnist/estimator/mnist_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,8 +57,8 @@ def scale(image, label):
return ds.map(scale).batch(BATCH_SIZE)

def serving_input_receiver_fn():
features = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 28, 28, 1], name='features')
receiver_tensors = {'features': features}
features = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 28, 28, 1], name='conv2d_input')
receiver_tensors = {'conv2d_input': features}
return tf.estimator.export.ServingInputReceiver(receiver_tensors, receiver_tensors)

def model_fn(features, labels, mode):
Expand Down Expand Up @@ -179,7 +179,7 @@ def parse(ln):
else: # args.mode == 'inference':
# using a trained/exported model
model = TFModel(args) \
.setInputMapping({'image': 'features'}) \
.setInputMapping({'image': 'conv2d_input'}) \
.setOutputMapping({'logits': 'prediction'}) \
.setSignatureDefKey('serving_default') \
.setExportDir(args.export_dir) \
Expand Down
76 changes: 0 additions & 76 deletions test/test_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,82 +171,6 @@ def rdd_generator():
expected = np.sum(self.weights)
self.assertAlmostEqual(pred, expected, 2)

# def test_spark_sparse_tensor(self):
# """InputMode.SPARK feeding sparse tensors"""
# def sparse_train(args, ctx):
# import tensorflow as tf
#
# # reset graph in case we're re-using a Spark python worker (during tests)
# tf.compat.v1.reset_default_graph()
#
# cluster, server = ctx.start_cluster_server(ctx)
# if ctx.job_name == "ps":
# server.join()
# elif ctx.job_name == "worker":
# with tf.device(tf.compat.v1.train.replica_device_setter(
# worker_device="/job:worker/task:%d" % ctx.task_index,
# cluster=cluster)):
# y_ = tf.compat.v1.placeholder(tf.float32, name='y_label')
# label = tf.identity(y_, name='label')
#
# row_indices = tf.compat.v1.placeholder(tf.int64, name='x_row_indices')
# col_indices = tf.compat.v1.placeholder(tf.int64, name='x_col_indices')
# values = tf.compat.v1.placeholder(tf.float32, name='x_values')
# indices = tf.stack([row_indices[0], col_indices[0]], axis=1)
# data = values[0]
#
# x = tf.SparseTensor(indices=indices, values=data, dense_shape=[args.batch_size, 10])
# w = tf.Variable(tf.random.truncated_normal([10, 1]), name='w')
# y = tf.sparse.sparse_dense_matmul(x, w, name='y')
#
# global_step = tf.compat.v1.train.get_or_create_global_step()
# cost = tf.reduce_mean(input_tensor=tf.square(y_ - y), name='cost')
# optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1).minimize(cost, global_step)
#
# with tf.compat.v1.train.MonitoredTrainingSession(master=server.target,
# is_chief=(ctx.task_index == 0),
# checkpoint_dir=args.model_dir,
# save_checkpoint_steps=20) as sess:
# tf_feed = ctx.get_data_feed(input_mapping=args.input_mapping)
# while not sess.should_stop() and not tf_feed.should_stop():
# batch = tf_feed.next_batch(args.batch_size)
# if len(batch) > 0:
# print("batch: {}".format(batch))
# feed = {y_: batch['y_label'],
# row_indices: batch['x_row_indices'],
# col_indices: batch['x_col_indices'],
# values: batch['x_values']}
# _, pred, trained_weights = sess.run([optimizer, y, w], feed_dict=feed)
# print("trained_weights: {}".format(trained_weights))
# sess.close()
#
# # wait for MonitoredTrainingSession to save last checkpoint
# time.sleep(10)
#
# args = {}
# estimator = TFEstimator(sparse_train, args) \
# .setInputMapping({'labels': 'y_label', 'row_indices': 'x_row_indices', 'col_indices': 'x_col_indices', 'values': 'x_values'}) \
# .setInputMode(TFCluster.InputMode.SPARK) \
# .setModelDir(self.model_dir) \
# .setClusterSize(self.num_workers) \
# .setNumPS(1) \
# .setBatchSize(1)
#
# model_weights = np.array([[1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0]]).T
# examples = [scipy.sparse.random(1, 10, density=0.5,) for i in range(200)]
# rdd = self.sc.parallelize(examples).map(lambda e: ((e * model_weights).tolist()[0][0], e.row.tolist(), e.col.tolist(), e.data.tolist()))
# df = rdd.toDF(["labels", "row_indices", "col_indices", "values"])
# df.show(5)
# model = estimator.fit(df)
#
# model.setOutputMapping({'label': 'label', 'y/SparseTensorDenseMatMul': 'predictions'})
# test_examples = [scipy.sparse.random(1, 10, density=0.5,) for i in range(50)]
# test_rdd = self.sc.parallelize(test_examples).map(lambda e: ((e * model_weights).tolist()[0][0], e.row.tolist(), e.col.tolist(), e.data.tolist()))
# test_df = test_rdd.toDF(["labels", "row_indices", "col_indices", "values"])
# test_df.show(5)
# preds = model.transform(test_df)
# preds.show(5)


if __name__ == '__main__':
unittest.main()