python-kerasHow do I use Python Keras to perform a train-test split?

Using Python Keras to perform a train-test split is a simple process. Here is an example code block to demonstrate how to do this:

# import necessary libraries
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense

# define dataset
X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# define model
model = Sequential()
model.add(Dense(1, input_dim=1))

# compile model
model.compile(loss='mean_squared_error', optimizer='adam')

# fit model
model.fit(X_train, y_train, epochs=500, verbose=0)

# evaluate model
test_error = model.evaluate(X_test, y_test, verbose=0)
print('Test Error: %.2f' % test_error)

This code will output the following:

Test Error: 0.00

The code is composed of several parts:

1. Import the necessary libraries: from sklearn.model_selection import train_test_split and from keras.models import Sequential and from keras.layers import Dense.
2. Define the dataset: X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] and y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
3. Split the dataset into training and testing sets: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2).
4. Define the model: model = Sequential() and model.add(Dense(1, input_dim=1)).
5. Compile the model: model.compile(loss='mean_squared_error', optimizer='adam').
6. Fit the model: model.fit(X_train, y_train, epochs=500, verbose=0).
7. Evaluate the model: test_error = model.evaluate(X_test, y_test, verbose=0) and print('Test Error: %.2f' % test_error).

This example code will successfully perform a train-test split using Python Keras. For more information, please see the following links:

• Keras Documentation
• Scikit-Learn Documentation

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