MultiLabel classifier example using Keras + Tensorflow

sample_keras_multilabel.py

#!/usr/bin/env python

''' MultiLabel classifier example using Keras + Tensorflow.

Based on

* https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/
* https://www.depends-on-the-definition.com/classifying-genres-of-movies-by-looking-at-the-poster-a-neural-approach/
* https://keras.io/getting-started/sequential-model-guide/#examples
'''


from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import random

nn = Sequential()
nn.add(Dense(10, activation="relu", input_shape=(10,)))
nn.add(Dense(5, activation='sigmoid'))

nn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

x_train = np.random.random((1000, 10))
y_train = np.random.randint(2, size=(1000, 5))

x_test = np.random.random((100, 10))
y_test = np.random.randint(2, size=(100, 5))

nn.fit(x_train, y_train, batch_size=16, epochs=5, verbose=1, validation_split=0.1)

score = nn.evaluate(x_test, y_test, batch_size=128)
print("Evaluation score:", score)

x, y = x_test[:1], y_test[:1]
print("X:\n\t", x[0])
print("Y (Actual):\n\t", y[0])
prediction = nn.predict(x)
print("Prediction:\n\t", prediction)