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High level network definitions with pre-trained weights in TensorFlow
| Date | Stars |
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| 2026-07-24 | 996 |
| 2026-07-25 | 996 |
| 2026-07-28 | 996 |
| 2026-07-30 | 996 |
| 2026-08-06 | 996 |
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# TensorNets [](https://travis-ci.org/taehoonlee/tensornets)
High level network definitions with pre-trained weights in [TensorFlow](https://github.com/tensorflow/tensorflow) (tested with `2.1.0 >=` TF `>= 1.4.0`).
## Guiding principles
- **Applicability.** Many people already have their own ML workflows, and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes.
- **Manageability.** Models are written in `tf.contrib.layers`, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and end-point. Also, it is easy to deploy and expand a collection of pre-processing and pre-trained weights.
- **Readability.** With recent TensorFlow APIs, more factoring and less indenting can be possible. For example, all the inception variants are implemented as about 500 lines of code in [TensorNets](tensornets/inceptions.py) while 2000+ lines in [official TensorFlow models](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py).
- **Reproducibility.** You can always reproduce the original results with [simple APIs](#utilities) including feature extractions. Furthermore, you don't need to care about a version of TensorFlow beacuse compatibilities with various releases of TensorFlow have been checked with [Travis](https://travis-ci.org/taehoonlee/tensornets/builds).
## Installation
You can install TensorNets from PyPI (`pip install tensornets`) or directly from GitHub (`pip install git+https://github.com/taehoonlee/tensornets.git`).
## A quick example
Each network (see [full list](#image-classification)) is not a custom class but a function that takes and returns `tf.Tensor` as its input and output. Here is an example of `ResNet50`:
```python
import tensorflow as tf
# import tensorflow.compat.v1 as tf # for TF 2
import tensornets as nets
# tf.disable_v2_behavior() # for TF 2
inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
model = nets.ResNet50(inputs)
assert isinstance(model, tf.Tensor)
```
You can load an example image by using `utils.load_img` returning a `np.ndarray` as the NHWC format:
```python
img = nets.utils.load_img('cat.png', target_size=256, crop_size=224)
assert img.shape == (1, 224, 224, 3)
```
Once your network is created, you can run with regular TensorFlow APIs 😊 because all the networks in TensorNets always return `tf.Tensor`. Using pre-trained weights and pre-processing are as easy as [`pretrained()`](tensornets/pretrained.py) and [`preprocess()`](tensornets/preprocess.py) to reproduce the original results:
```python
with tf.Session() as sess:
img = model.preprocess(img) # equivalent to img = nets.preprocess(model, img)
sess.run(model.pretrained()) # equivalent to nets.pretrained(model)
preds = sess.run(model, {inputs: img})
```
You can see the most probable classes:
```python
print(nets.utils.decode_predictions(preds, top=2)[0])
[(u'n02124075', u'Egyptian_cat', 0.28067636), (u'n02127052', u'lynx', 0.16826575)]
```
You can also easily obtain values of intermediate layers with `middles()` and `outputs()`:
```python
with tf.Session() as sess:
img = model.preprocess(img)
sess.run(model.pretrained())
middles = sess.run(model.middles(), {inputs: img})
outputs = sess.run(model.outputs(), {inputs: img})
model.print_middles()
assert middles[0].shape == (1, 56, 56, 256)
assert middles[-1].shape == (1, 7, 7, 2048)
model.print_outputs()
assert sum(sum((outputs[-1] - preds) ** 2)) < 1e-8
```
With `load()` and `save()`, your weight values can be restorable:
```python
with tf.Session() as sess:
model.init()
# ... your training ...
model.save('test.npz')
with tf.Session() as sess:
model.load('test.npz')
# ... your deployment ...
```
TensorNets enables us to deploy well-known architectures and benchmark tExcerpt of 32,082 characters
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Taehoon Lee · @Desilo · South Korea
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4037fa87580e840d, topic:object-detection, topic:yolo, readme:image classification
matched fp:4037fa87580e840d, topic:deep-learning, topic:tensorflow