data = np.random.random((1000, 100))
labels = np.random.randint(10, size=(1000, 1))
one_hot_labels = keras.utils.to_categorical(labels, num_classes=10)
model.fit(data, one_hot_labels, epochs=10, batch_size=32)
dataset = np.loadtxt(os.path.join("..", "data", "pima-indians-diabetes.data"), delimiter=',')
X = dataset[:, 0:8]
Y = dataset[:, 8]
model.fit(X, Y, nb_epoch=150, batch_size=10)
x_train = np.random.random((1000, 20))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)
model.fit(x_train, y_train,
epochs=20,
batch_size=128)