The tensorflow_iris_tfrecord_and_estimator from zofuthan

TensorFlow_iris_estimator

The main purpose of this resource is to show how to use tfrecord data and customize TensorFlow estimator.

Of course, you can modify the code as much as you want. You can also go to my blog.

tfrecord basic explanation

For example, see the code

tensorflow official sample code

blog_estimators_dataset.py

code explainCreating Custom Estimators in TensorFlow

Producte tfrecord files

1. Open TFRecordWriter

writer = tf.python_io.TFRecordWriter(tfrecord_store_path)

2. prepater to feature_dict

for index, row in pandas_datafram_data.iterrows():
    dict_row = dict(row)
    feature_dict = {}
    # prepater to feature_dict
    for k,v in dict_row.items():
        if k in ['feature1', 'feature2',...,'featureN']:
            feature_dict[k] = tf.train.Feature(float_list=tf.train.FloatList(value=[v]))
        elif k in ['feature1', 'feature2',...,'featureN']:
            feature_dict[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=[v])
        else:
            feature_dict[k] = tf.train.Feature(int64_list=tf.train.BytesList(value=[v)])

3. producte data example

    example = tf.train.Example(features=tf.train.Features(feature=feature_dict))

4. serialize to string

    serialized = example.SerializeToString()

5. write a example to tfrecord file

    writer.write(serialized)

6. Close TFRecordWriter

writer.close()

Using tfrecord files

1. Open TFRecordDataset

filenames = ['tfrecord_file_name1','tfrecord_file_name2']
tf_dataset = tf.data.TFRecordDataset(filenames)

2. Parse dataset

def _parse_function(record):
    features = {"feature1": tf.FixedLenFeature((), tf.float32, default_value=0.0),
                "feature2": tf.FixedLenFeature((), tf.int64, default_value=0)}
    parsed_features = tf.parse_single_example(record, features)
    return {"feature1": parsed_features["feature1"]}, parsed_features["Species"]
dataset = tf_dataset.map(_parse_function)

3. Shuffle, repeat, and batch the examples

tf_dataset = dataset.shuffle(1000).repeat().batch(batch_size)

4. Iterate dataset

tf_iterator = tf_dataset.make_one_shot_iterator()
next_element = tf_iterator.get_next()
with tf.Session() as sess:
    for i in range(show_numbers):
        a_data = sess.run(next_element)
        print(a_data)

About the repo

Rely on

python 3.6
TensorFlow 1.12.0

Usage method

python run_iris_estimator.py

Possible output

Producte data files

store data to data/iris_training.tfrecord
store data to data/iris_test.tfrecord

Create model

INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': 'model/tf_record_iris', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f2b56401278>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.

Training model

INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into model/tf_record_iris/model.ckpt.
INFO:tensorflow:loss = 1.9349309, step = 1
INFO:tensorflow:global_step/sec: 84.0476
INFO:tensorflow:loss = 0.13681199, step = 101 (1.195 sec)
INFO:tensorflow:global_step/sec: 73.8782
INFO:tensorflow:loss = 0.06917934, step = 201 (1.349 sec)
INFO:tensorflow:global_step/sec: 80.3087
INFO:tensorflow:loss = 0.07953491, step = 301 (1.246 sec)
INFO:tensorflow:global_step/sec: 82.9652
INFO:tensorflow:loss = 0.08498424, step = 401 (1.205 sec)
INFO:tensorflow:global_step/sec: 82.1202
INFO:tensorflow:loss = 0.06799353, step = 501 (1.218 sec)
INFO:tensorflow:global_step/sec: 90.6455
INFO:tensorflow:loss = 0.064572886, step = 601 (1.103 sec)
INFO:tensorflow:global_step/sec: 80.6642
INFO:tensorflow:loss = 0.046872936, step = 701 (1.239 sec)
INFO:tensorflow:global_step/sec: 88.4239
INFO:tensorflow:loss = 0.041766435, step = 801 (1.131 sec)
INFO:tensorflow:global_step/sec: 82.1831
INFO:tensorflow:loss = 0.055104688, step = 901 (1.217 sec)

Storage model

INFO:tensorflow:Saving checkpoints for 1000 into model/tf_record_iris/model.ckpt.

Evaluation model

INFO:tensorflow:Restoring parameters from model/tf_record_iris/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2018-12-20-12:49:27
INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.96666664, global_step = 1000, loss = 0.06140284
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: model/tf_record_iris/model.ckpt-1000

Test set accuracy: 0.967

Model prediction

INFO:tensorflow:Restoring parameters from model/tf_record_iris/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.

Prediction is "Setosa" (99.8%), expected "Setosa"

Prediction is "Versicolor" (99.9%), expected "Versicolor"

Prediction is "Virginica" (98.1%), expected "Virginica"

Main References

iris_data.py

custom_estimator.py

zofuthan / tensorflow_iris_tfrecord_and_estimator Goto Github PK

tensorflow_iris_tfrecord_and_estimator's Introduction

tensorflow_iris_tfrecord_and_estimator's People

Contributors

Watchers

Recommend Projects

Recommend Topics

Recommend Org