To split features from labels in Keras, you can use the `train_test_split()` function from the `sklearn.model_selection` module. This function takes three arguments:
- The data: This is the dataset that you want to split.
- The target: This is the column that you want to predict.
- The test_size: This is the percentage of the data that you want to use for testing. The remaining data will be used for training.
For example, the following code splits the features and labels from the `boston_housing` dataset:
from sklearn.model_selection import train_test_split
(features_train, features_test, labels_train, labels_test) = train_test_split(
boston_housing, boston_housing['MEDV'], test_size=0.25)
The `features_train` and `labels_train` variables contain the training data, and the `features_test` and `labels_test` variables contain the testing data.
Once you have split the features and labels, you can train a regression model on the training data. You can then evaluate the model on the testing data to see how well it performs.
Here is an example of how to train a regression model in Keras:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(12, input_dim=13))
model.add(Dense(1))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(features_train, labels_train, epochs=100)
model.evaluate(features_test, labels_test)
This code creates a simple regression model with one hidden layer. The model is trained for 100 epochs using the `rmsprop` optimizer and the `mse` loss function. The model is then evaluated on the testing data.
The output of the `model.evaluate()` function will be a tuple containing the loss and the accuracy of the model. The loss is a measure of how well the model fits the data, and the accuracy is a measure of how often the model predicts the correct label.
You can use the loss and accuracy to tune the hyperparameters of your model and improve its performance.
