python-kerasHow do I use Python and Keras to create a classification example?

The following example uses Python and Keras to create a classification example:

# import necessary libraries
import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# define the data
x_data = np.array([[1,2], [2,3], [3,1], [4,3], [5,3], [6,2]])
y_data = np.array([0,0,0,1,1,1])

# define the model
model = Sequential()
model.add(Dense(1, activation='sigmoid', input_dim=2))
model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])

# train the model
model.fit(x_data, y_data, epochs=200)

# evaluate the model
results = model.evaluate(x_data, y_data)

# print the results
print("Accuracy:", results[1])

Output example

Accuracy: 1.0

The code above creates a classification example using Python and Keras. It first imports the necessary libraries, such as NumPy and the Keras Sequential and Dense layers. Then it defines the data, which consists of a set of input values (x_data) and the corresponding output labels (y_data). Next, the model is defined as a Sequential model with a single Dense layer that uses a sigmoid activation function. The model is then compiled using a stochastic gradient descent (SGD) optimizer and a binary cross entropy loss function. Finally, the model is trained using the x_data and y_data values for 200 epochs and then evaluated using the same data. The output of the evaluation is the accuracy of the model, which in this case is 1.0.

Helpful links

• NumPy
• Keras
• Keras Sequential Model
• Keras Dense Layer

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