import pickle import numpy as np import pytest import tensorflow as tf import tensorflow_probability as tfp from garage.tf.models import CategoricalMLPModel from tests.fixtures import TfGraphTestCase class TestCategoricalMLPModel(TfGraphTestCase): def setup_method(self): super().setup_method() self.input_var = tf.compat.v1.placeholder(tf.float32, shape=(None, 5)) self.obs = np.ones((1, 5)) def test_dist(self): model = CategoricalMLPModel(output_dim=1) dist = model.build(self.input_var).dist assert isinstance(dist, tfp.distributions.OneHotCategorical) @pytest.mark.parametrize('output_dim', [1, 2, 5, 10]) def test_output_normalized(self, output_dim): model = CategoricalMLPModel(output_dim=output_dim) obs_ph = tf.compat.v1.placeholder(tf.float32, shape=(None, output_dim)) obs = np.ones((1, output_dim)) dist = model.build(obs_ph).dist probs = tf.compat.v1.get_default_session().run(tf.reduce_sum( dist.probs), feed_dict={obs_ph: obs}) assert np.isclose(probs, 1.0) # yapf: disable @pytest.mark.parametrize('output_dim, hidden_sizes', [ (1, (1, )), (1, (2, )), (2, (3, )), (2, (1, 1)), (3, (2, 2)), ]) # yapf: enable def test_is_pickleable(self, output_dim, hidden_sizes): model = CategoricalMLPModel(output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=None, hidden_w_init=tf.ones_initializer(), output_w_init=tf.ones_initializer()) dist = model.build(self.input_var).dist # assign bias to all one with tf.compat.v1.variable_scope('CategoricalMLPModel/mlp', reuse=True): bias = tf.compat.v1.get_variable('hidden_0/bias') bias.load(tf.ones_like(bias).eval()) output1 = self.sess.run(dist.probs, feed_dict={self.input_var: self.obs}) h = pickle.dumps(model) with tf.compat.v1.Session(graph=tf.Graph()) as sess: input_var = tf.compat.v1.placeholder(tf.float32, shape=(None, 5)) model_pickled = pickle.loads(h) dist2 = model_pickled.build(input_var).dist output2 = sess.run(dist2.probs, feed_dict={input_var: self.obs}) assert np.array_equal(output1, output2)