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README.md
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README.md
@ -37,7 +37,7 @@ Keras is compatible with: __Python 2.7-3.5__.
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## Getting started: 30 seconds to Keras
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## Getting started: 30 seconds to Keras
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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]().
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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).
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Here's the `Sequential` model:
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Here's the `Sequential` model:
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@ -81,7 +81,7 @@ model.train_on_batch(X_batch, Y_batch)
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Evaluate your performance in one line:
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Evaluate your performance in one line:
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```python
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```python
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objective_score = model.evaluate(X_test, Y_test, batch_size=32)
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loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
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```
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```
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Or generate predictions on new data:
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Or generate predictions on new data:
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@ -90,13 +90,13 @@ classes = model.predict_classes(X_test, batch_size=32)
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proba = model.predict_proba(X_test, batch_size=32)
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proba = model.predict_proba(X_test, batch_size=32)
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```
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```
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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?
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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?
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For a more in-depth tutorial about Keras, you can check out:
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For a more in-depth tutorial about Keras, you can check out:
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- [Getting started with the Sequential model]()
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- [Getting started with the Sequential model](http://keras.io/getting-started/sequential-model-guide)
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- [Getting started with the functional API]()
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- [Getting started with the functional API](http://keras.io/getting-started/functional-api-guide)
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- [Starter examples]()
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- [Starter examples](http://keras.io/examples)
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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.
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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.
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