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Francois Chollet 2016-04-06 17:33:39 -07:00
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@ -37,7 +37,7 @@ Keras is compatible with: __Python 2.7-3.5__.
## Getting started: 30 seconds to Keras
The core data structure of Keras is a __model__, a way to organize layers. The main type of model is the [`Sequential`](http://keras.io/models/#sequential) model, a linear stack of layers. For more complex architectures, you should use the [Keras function API]().
The core data structure of Keras is a __model__, a way to organize layers. The main type of model is the [`Sequential`](http://keras.io/getting-started/sequential-model-guide) model, a linear stack of layers. For more complex architectures, you should use the [Keras function API](http://keras.io/getting-started/functional-api-guide).
Here's the `Sequential` model:
@ -81,7 +81,7 @@ model.train_on_batch(X_batch, Y_batch)
Evaluate your performance in one line:
```python
objective_score = model.evaluate(X_test, Y_test, batch_size=32)
loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
```
Or generate predictions on new data:
@ -90,13 +90,13 @@ classes = model.predict_classes(X_test, batch_size=32)
proba = model.predict_proba(X_test, batch_size=32)
```
Building a network of LSTMs, a deep CNN, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
Building a question answering system, an image classification model, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
For a more in-depth tutorial about Keras, you can check out:
- [Getting started with the Sequential model]()
- [Getting started with the functional API]()
- [Starter examples]()
- [Getting started with the Sequential model](http://keras.io/getting-started/sequential-model-guide)
- [Getting started with the functional API](http://keras.io/getting-started/functional-api-guide)
- [Starter examples](http://keras.io/examples)
In the [examples folder](https://github.com/fchollet/keras/tree/master/examples) of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.