@keras_test
def test_vector_regression():
 np.random.seed(1337)
 nb_hidden = 10
 ((X_train, y_train), (X_test, y_test)) = get_test_data(nb_train=500, nb_test=200, input_shape=(20,), output_shape=(2,), classification=False)
 model = Sequential([Dense(nb_hidden, input_shape=(X_train.shape[(-1)],), activation='tanh'), Dense(y_train.shape[(-1)])])
 model.compile(loss='hinge', optimizer='adagrad')
 history = model.fit(X_train, y_train, nb_epoch=20, batch_size=16, validation_data=(X_test, y_test), verbose=0)
 assert (history.history['val_loss'][(-1)] < 0.9)

@ keras_test def test_vector_regression ( ) : np . random . seed ( 1337 ) nb_hidden = 10 ( ( X_train , y_train ) , ( X_test , y_test ) ) = get_test_data ( nb_train = 500 , nb_test = 200 , input_shape = ( 20 , ) , output_shape = ( 2 , ) , classification = False ) model = Sequential ( [ Dense ( nb_hidden , input_shape = ( X_train . shape [ ( - 1 ) ] , ) , activation = 'tanh' ) , Dense ( y_train . shape [ ( - 1 ) ] ) ] ) model . compile ( loss = 'hinge' , optimizer = 'adagrad' ) history = model . fit ( X_train , y_train , nb_epoch = 20 , batch_size = 16 , validation_data = ( X_test , y_test ) , verbose = 0 ) assert ( history . history [ 'val_loss' ] [ ( - 1 ) ] < 0.9 )
perform float data prediction using 2 layer mlp with tanh and sigmoid activations .