pre-release 0.10.0

rl_coach/tests/architectures/tensorflow_components/embedders/test_identity_embedder.py

@@ -0,0 +1,45 @@
+import os
+import sys
+
+from rl_coach.base_parameters import EmbedderScheme
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+import pytest
+import numpy as np
+from rl_coach.architectures.tensorflow_components.embedders.vector_embedder import VectorEmbedder
+import tensorflow as tf
+from tensorflow import logging
+
+logging.set_verbosity(logging.INFO)
+
+@pytest.fixture
+def reset():
+    tf.reset_default_graph()
+
+
+@pytest.mark.unit_test
+def test_embedder(reset):
+    embedder = VectorEmbedder(np.array([10, 10]), name="test", scheme=EmbedderScheme.Empty)
+
+    # make sure the ops where not created yet
+    assert len(tf.get_default_graph().get_operations()) == 0
+
+    # call the embedder
+    input_ph, output_ph = embedder()
+
+    # make sure that now the ops were created
+    assert len(tf.get_default_graph().get_operations()) > 0
+
+    # try feeding a batch of one example  # TODO: consider auto converting to batch
+    input = np.random.rand(1, 10, 10)
+    sess = tf.Session()
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert output.shape == (1, 100)  # should have flattened the input
+
+    # now make sure the returned placeholders behave the same
+    output = sess.run(output_ph, {input_ph: input})
+    assert output.shape == (1, 100)  # should have flattened the input
+
+    # make sure the naming is correct
+    assert embedder.get_name() == "test"

rl_coach/tests/architectures/tensorflow_components/embedders/test_image_embedder.py

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+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+import pytest
+import numpy as np
+from rl_coach.architectures.tensorflow_components.embedders.image_embedder import ImageEmbedder, EmbedderScheme
+import tensorflow as tf
+from tensorflow import logging
+
+logging.set_verbosity(logging.INFO)
+
+@pytest.fixture
+def reset():
+    tf.reset_default_graph()
+
+
+@pytest.mark.unit_test
+def test_embedder(reset):
+    # creating an embedder with a non-image input
+    with pytest.raises(ValueError):
+        embedder = ImageEmbedder(np.array([100]), name="test")
+    with pytest.raises(ValueError):
+        embedder = ImageEmbedder(np.array([100, 100]), name="test")
+    with pytest.raises(ValueError):
+        embedder = ImageEmbedder(np.array([10, 100, 100, 100]), name="test")
+
+    # creating a simple image embedder
+    embedder = ImageEmbedder(np.array([100, 100, 10]), name="test")
+
+    # make sure the ops where not created yet
+    assert len(tf.get_default_graph().get_operations()) == 0
+
+    # call the embedder
+    input_ph, output_ph = embedder()
+
+    # make sure that now the ops were created
+    assert len(tf.get_default_graph().get_operations()) > 0
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 100, 100, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert output.shape == (1, 5184)
+
+    # now make sure the returned placeholders behave the same
+    output = sess.run(output_ph, {input_ph: input})
+    assert output.shape == (1, 5184)
+
+    # make sure the naming is correct
+    assert embedder.get_name() == "test"
+
+
+@pytest.mark.unit_test
+def test_complex_embedder(reset):
+    # creating a deep vector embedder
+    embedder = ImageEmbedder(np.array([100, 100, 10]), name="test", scheme=EmbedderScheme.Deep)
+
+    # call the embedder
+    embedder()
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 100, 100, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert output.shape == (1, 256)  # should have flattened the input
+
+
+@pytest.mark.unit_test
+def test_activation_function(reset):
+    # creating a deep image embedder with relu
+    embedder = ImageEmbedder(np.array([100, 100, 10]), name="relu", scheme=EmbedderScheme.Deep,
+                             activation_function=tf.nn.relu)
+
+    # call the embedder
+    embedder()
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 100, 100, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert np.all(output >= 0)  # should have flattened the input
+
+    # creating a deep image embedder with tanh
+    embedder_tanh = ImageEmbedder(np.array([100, 100, 10]), name="tanh", scheme=EmbedderScheme.Deep,
+                                  activation_function=tf.nn.tanh)
+
+    # call the embedder
+    embedder_tanh()
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 100, 100, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder_tanh.output, {embedder_tanh.input: input})
+    assert np.all(output >= -1) and np.all(output <= 1)

rl_coach/tests/architectures/tensorflow_components/embedders/test_vector_embedder.py

@@ -0,0 +1,95 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+import pytest
+import numpy as np
+from rl_coach.architectures.tensorflow_components.embedders.vector_embedder import VectorEmbedder, EmbedderScheme
+import tensorflow as tf
+from tensorflow import logging
+
+logging.set_verbosity(logging.INFO)
+
+@pytest.fixture
+def reset():
+    tf.reset_default_graph()
+
+
+@pytest.mark.unit_test
+def test_embedder(reset):
+    # creating a vector embedder with a matrix
+    with pytest.raises(ValueError):
+        embedder = VectorEmbedder(np.array([10, 10]), name="test")
+
+    # creating a simple vector embedder
+    embedder = VectorEmbedder(np.array([10]), name="test")
+
+    # make sure the ops where not created yet
+    assert len(tf.get_default_graph().get_operations()) == 0
+
+    # call the embedder
+    input_ph, output_ph = embedder()
+
+    # make sure that now the ops were created
+    assert len(tf.get_default_graph().get_operations()) > 0
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert output.shape == (1, 256)
+
+    # now make sure the returned placeholders behave the same
+    output = sess.run(output_ph, {input_ph: input})
+    assert output.shape == (1, 256)
+
+    # make sure the naming is correct
+    assert embedder.get_name() == "test"
+
+
+@pytest.mark.unit_test
+def test_complex_embedder(reset):
+    # creating a deep vector embedder
+    embedder = VectorEmbedder(np.array([10]), name="test", scheme=EmbedderScheme.Deep)
+
+    # call the embedder
+    embedder()
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert output.shape == (1, 128)  # should have flattened the input
+
+
+@pytest.mark.unit_test
+def test_activation_function(reset):
+    # creating a deep vector embedder with relu
+    embedder = VectorEmbedder(np.array([10]), name="relu", scheme=EmbedderScheme.Deep,
+                              activation_function=tf.nn.relu)
+
+    # call the embedder
+    embedder()
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder.output, {embedder.input: input})
+    assert np.all(output >= 0)  # should have flattened the input
+
+    # creating a deep vector embedder with tanh
+    embedder_tanh = VectorEmbedder(np.array([10]), name="tanh", scheme=EmbedderScheme.Deep,
+                                   activation_function=tf.nn.tanh)
+
+    # call the embedder
+    embedder_tanh()
+
+    # try feeding a batch of one example
+    input = np.random.rand(1, 10)
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+    output = sess.run(embedder_tanh.output, {embedder_tanh.input: input})
+    assert np.all(output >= -1) and np.all(output <= 1)