From 5910278ca8583a1e5fb4104c2419212595d5e69e Mon Sep 17 00:00:00 2001 From: TobyPDE Date: Mon, 25 Apr 2016 18:14:18 +0200 Subject: [PATCH] Fixed minor typo in getting-started/sequential-model-guide (#2499) --- docs/templates/getting-started/sequential-model-guide.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/templates/getting-started/sequential-model-guide.md b/docs/templates/getting-started/sequential-model-guide.md index 6efb545dc..77d776fc7 100644 --- a/docs/templates/getting-started/sequential-model-guide.md +++ b/docs/templates/getting-started/sequential-model-guide.md @@ -112,7 +112,7 @@ Now you know enough to be able to define *almost* any model with Keras. For comp Before training a model, you need to configure the learning process, which is done via the `compile` method. It receives three arguments: - 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). -- 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). +- 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). - 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. ```python @@ -538,4 +538,4 @@ y_val = np.random.random((100, nb_classes)) decoder.fit([x_train_a, x_train_b], y_train, batch_size=64, nb_epoch=5, validation_data=([x_val_a, x_val_b], y_val)) -``` \ No newline at end of file +```