You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: README.md
+19-7Lines changed: 19 additions & 7 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -29,41 +29,53 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib
29
29
## More Examples
30
30
The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api).
31
31
32
-
####Basics
32
+
## Basics
33
33
-[Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn.
34
34
-[Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge').
35
35
-[Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model.
36
36
-[Fine-Tuning](https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py). Fine-Tune a pre-trained model on a new task.
37
37
-[Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets.
38
38
-[Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets.
39
39
40
-
#### Computer Vision
40
+
## Extending Tensorflow
41
+
-[Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with Tensorflow.
42
+
-[Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any Tensorflow graph.
43
+
-[Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with Tensorflow.
44
+
-[Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with Tensorflow.
45
+
-[Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with Tensorflow.
46
+
47
+
## Computer Vision
41
48
-[Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task.
42
49
-[Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset.
43
50
-[Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset.
44
51
-[Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset.
45
52
-[Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task.
46
53
-[VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task.
47
54
-[RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images.
48
-
-[Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with shallow bottlenecks applied to MNIST classification task.
49
-
-[Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with deep bottlenecks applied to CIFAR-10 classification task.
55
+
-[Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset.
-[Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with shallow bottlenecks applied to CIFAR-10 classification task.
58
+
-[Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with deep bottlenecks applied to MNIST classification task.
50
59
-[Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits.
51
60
52
-
####Natural Language Processing
61
+
## Natural Language Processing
53
62
-[Reccurent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task.
54
63
-[Bi-Directional LSTM](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task.
55
64
-[City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network.
56
65
-[Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network.
57
66
67
+
## Notebooks
68
+
-[Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n.
69
+
58
70
## Dependencies
59
71
```
60
72
tensorflow
61
73
numpy
62
74
matplotlib
63
-
cuda (to run examples on GPU)
75
+
cuda
64
76
tflearn (if using tflearn examples)
65
77
```
66
-
For more details about TensorFlow installation, you can check [Setup_TensorFlow.md](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/Setup_TensorFlow.md)
78
+
For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md)
67
79
68
80
## Dataset
69
81
Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py).
0 commit comments