def mturk_test():
embedding_dim = 300
Y = get_mturk_outcomes()
Y = (Y - np.mean(Y, axis=0)) / np.std(Y, axis=0)
X = np.load('../data/mturk_embedded.npz')
# X0 = X['arr_0']
# X1 = X['arr_1']
X2 = X['arr_2']
# X3 = X['arr_3']
m = SWEM(embedding_dimension=embedding_dim,
num_outputs=2,
learning_rate=1e-4,
activation_fn=tf.nn.elu,
embedding_mlp_depth=2,
prediction_mlp_layers=(120, 24))
# m.train(X0, Y, plotfile='../img/X0_training.png')
m.train(X2,
Y[:, :2],
plotfile='../img/X2_Y01_training.png',
batch_size=100,
epochs=20) # m.train(X2, Y, plotfile='../img/X2_training.png')
def random_noise_test():
embedding_dim = 300
data_size = 1000
X = [
np.random.randn(np.random.randint(10, 100), embedding_dim)
for i in range(data_size)
]
Y = .2 * np.random.randn(data_size) + .5
m = SWEM(embedding_dimension=embedding_dim)
m.train(X, Y, plotfile='../img/test_training.png')