Fixed minor typo in getting-started/sequential-model-guide (#2499)
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@ -112,7 +112,7 @@ Now you know enough to be able to define *almost* any model with Keras. For comp
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Before training a model, you need to configure the learning process, which is done via the `compile` method. It receives three arguments:
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- an optimizer. This could be the string identifier of an existing optimizer (such as `rmsprop` or `adagrad`), or an instance of the `Optimizer` class. See: [optimizers](/optimizers).
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- a loss function. This is the objective that the model will try to minimize. If can be the string identifier of an existing loss function (such as `categorical_crossentropy` or `mse`), or it can be an objective function. See: [objectives](/objectives).
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- a loss function. This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function (such as `categorical_crossentropy` or `mse`), or it can be an objective function. See: [objectives](/objectives).
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- a list of metrics. For any classification problem you will want to set this to `metrics=['accuracy']`. A metric could be the string identifier of an existing metric (only `accuracy` is supported at this point), or a custom metric function.
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```python
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@ -538,4 +538,4 @@ y_val = np.random.random((100, nb_classes))
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decoder.fit([x_train_a, x_train_b], y_train,
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batch_size=64, nb_epoch=5,
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validation_data=([x_val_a, x_val_b], y_val))
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```
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```
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