-
Notifications
You must be signed in to change notification settings - Fork 117
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Added examples for tensorflow types in Datatypes and IO section
Signed-off-by: sumana sree <sumanasree2705@gmail.com>
- Loading branch information
1 parent
a1dde19
commit f6be5ad
Showing
1 changed file
with
85 additions
and
0 deletions.
There are no files selected for viewing
85 changes: 85 additions & 0 deletions
85
examples/data_types_and_io/data_types_and_io/tensorflow_type.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,85 @@ | ||
# Tensorflow Model | ||
import tensorflow as tf | ||
from flytekit import task, workflow | ||
|
||
@task | ||
def train_model() -> tf.keras.Model: | ||
model = tf.keras.Sequential([ | ||
tf.keras.layers.Dense(128, activation='relu'), | ||
tf.keras.layers.Dense(10, activation='softmax') | ||
]) | ||
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) | ||
return model | ||
|
||
@task | ||
def evaluate_model(model: tf.keras.Model, x: tf.Tensor, y: tf.Tensor) -> float: | ||
loss, accuracy = model.evaluate(x, y) | ||
return accuracy | ||
|
||
@workflow | ||
def training_workflow(x: tf.Tensor, y: tf.Tensor) -> float: | ||
model = train_model() | ||
accuracy = evaluate_model(model=model, x=x, y=y) | ||
return accuracy | ||
|
||
|
||
# TensorFlow Record File | ||
from flytekit.types.file import TFRecordFile | ||
from flytekit import task, workflow | ||
|
||
@task | ||
def process_tfrecord(file: TFRecordFile) -> int: | ||
dataset = tf.data.TFRecordDataset(file) | ||
count = 0 | ||
for raw_record in dataset: | ||
example = tf.train.Example() | ||
example.ParseFromString(raw_record.numpy()) | ||
count += 1 | ||
return count | ||
|
||
@workflow | ||
def tfrecord_workflow(file: TFRecordFile) -> int: | ||
return process_tfrecord(file=file) | ||
|
||
|
||
# TensorFlow Records Directory | ||
from flytekit.types.directory import TFRecordsDirectory | ||
from flytekit import task, workflow | ||
import os | ||
import tensorflow as tf | ||
|
||
@task | ||
def process_tfrecords_dir(dir: TFRecordsDirectory) -> int: | ||
files = [f.path for f in os.scandir(dir) if f.is_file()] | ||
dataset = tf.data.TFRecordDataset(files) | ||
count = 0 | ||
for raw_record in dataset: | ||
example = tf.train.Example() | ||
example.ParseFromString(raw_record.numpy()) | ||
count += 1 | ||
return count | ||
|
||
@workflow | ||
def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int: | ||
return process_tfrecords_dir(dir=dir) | ||
|
||
# TFRecordDatasetConfig | ||
from flytekit.types.directory import TFRecordsDirectory | ||
from flytekit import task, workflow | ||
import os | ||
import tensorflow as tf | ||
|
||
@task | ||
def process_tfrecords_dir(dir: TFRecordsDirectory) -> int: | ||
files = [f.path for f in os.scandir(dir) if f.is_file()] | ||
dataset = tf.data.TFRecordDataset(files) | ||
count = 0 | ||
for raw_record in dataset: | ||
example = tf.train.Example() | ||
example.ParseFromString(raw_record.numpy()) | ||
count += 1 | ||
return count | ||
|
||
@workflow | ||
def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int: | ||
return process_tfrecords_dir(dir=dir) |