1 files changed, 154 insertions, 0 deletions
diff --git a/tensorflow/models/rnn/rnn_cell_test.py b/tensorflow/models/rnn/rnn_cell_test.py
new file mode 100644
index 0000000000..8b4b209028
--- /dev/null
+++ b/tensorflow/models/rnn/rnn_cell_test.py
@@ -0,0 +1,154 @@
+"""Tests for RNN cells."""
+
+# pylint: disable=g-bad-import-order,unused-import
+import tensorflow.python.platform
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.models.rnn import rnn_cell
+
+
+class RNNCellTest(tf.test.TestCase):
+
+  def testBasicRNNCell(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 2])
+        m = tf.zeros([1, 2])
+        g, _ = rnn_cell.BasicRNNCell(2)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g], {x.name: np.array([[1., 1.]]),
+                             m.name: np.array([[0.1, 0.1]])})
+        self.assertEqual(res[0].shape, (1, 2))
+
+  def testGRUCell(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 2])
+        m = tf.zeros([1, 2])
+        g, _ = rnn_cell.GRUCell(2)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g], {x.name: np.array([[1., 1.]]),
+                             m.name: np.array([[0.1, 0.1]])})
+        # Smoke test
+        self.assertAllClose(res[0], [[0.175991, 0.175991]])
+
+  def testBasicLSTMCell(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 2])
+        m = tf.zeros([1, 8])
+        g, out_m = rnn_cell.MultiRNNCell([rnn_cell.BasicLSTMCell(2)] * 2)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g, out_m], {x.name: np.array([[1., 1.]]),
+                                    m.name: 0.1 * np.ones([1, 8])})
+        self.assertEqual(len(res), 2)
+        # The numbers in results were not calculated, this is just a smoke test.
+        self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
+        expected_mem = np.array([[0.68967271, 0.68967271,
+                                  0.44848421, 0.44848421,
+                                  0.39897051, 0.39897051,
+                                  0.24024698, 0.24024698]])
+        self.assertAllClose(res[1], expected_mem)
+
+  def testLSTMCell(self):
+    with self.test_session() as sess:
+      num_units = 8
+      num_proj = 6
+      state_size = num_units + num_proj
+      batch_size = 3
+      input_size = 2
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([batch_size, input_size])
+        m = tf.zeros([batch_size, state_size])
+        output, state = rnn_cell.LSTMCell(
+            num_units=num_units, input_size=input_size, num_proj=num_proj)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([output, state],
+                       {x.name: np.array([[1., 1.], [2., 2.], [3., 3.]]),
+                        m.name: 0.1 * np.ones((batch_size, state_size))})
+        self.assertEqual(len(res), 2)
+        # The numbers in results were not calculated, this is mostly just a
+        # smoke test.
+        self.assertEqual(res[0].shape, (batch_size, num_proj))
+        self.assertEqual(res[1].shape, (batch_size, state_size))
+        # Different inputs so different outputs and states
+        for i in range(1, batch_size):
+          self.assertTrue(
+              float(np.linalg.norm((res[0][0,:] - res[0][i,:]))) > 1e-6)
+          self.assertTrue(
+              float(np.linalg.norm((res[1][0,:] - res[1][i,:]))) > 1e-6)
+
+  def testOutputProjectionWrapper(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 3])
+        m = tf.zeros([1, 3])
+        cell = rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(3), 2)
+        g, new_m = cell(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g, new_m], {x.name: np.array([[1., 1., 1.]]),
+                                    m.name: np.array([[0.1, 0.1, 0.1]])})
+        self.assertEqual(res[1].shape, (1, 3))
+        # The numbers in results were not calculated, this is just a smoke test.
+        self.assertAllClose(res[0], [[0.231907, 0.231907]])
+
+  def testInputProjectionWrapper(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 2])
+        m = tf.zeros([1, 3])
+        cell = rnn_cell.InputProjectionWrapper(rnn_cell.GRUCell(3), 2)
+        g, new_m = cell(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g, new_m], {x.name: np.array([[1., 1.]]),
+                                    m.name: np.array([[0.1, 0.1, 0.1]])})
+        self.assertEqual(res[1].shape, (1, 3))
+        # The numbers in results were not calculated, this is just a smoke test.
+        self.assertAllClose(res[0], [[0.154605, 0.154605, 0.154605]])
+
+  def testDropoutWrapper(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 3])
+        m = tf.zeros([1, 3])
+        keep = tf.zeros([1]) + 1
+        g, new_m = rnn_cell.DropoutWrapper(rnn_cell.GRUCell(3),
+                                           keep, keep)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g, new_m], {x.name: np.array([[1., 1., 1.]]),
+                                    m.name: np.array([[0.1, 0.1, 0.1]])})
+        self.assertEqual(res[1].shape, (1, 3))
+        # The numbers in results were not calculated, this is just a smoke test.
+        self.assertAllClose(res[0], [[0.154605, 0.154605, 0.154605]])
+
+  def testEmbeddingWrapper(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 1], dtype=tf.int32)
+        m = tf.zeros([1, 2])
+        g, new_m = rnn_cell.EmbeddingWrapper(rnn_cell.GRUCell(2), 3)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run([g, new_m], {x.name: np.array([[1]]),
+                                    m.name: np.array([[0.1, 0.1]])})
+        self.assertEqual(res[1].shape, (1, 2))
+        # The numbers in results were not calculated, this is just a smoke test.
+        self.assertAllClose(res[0], [[0.17139, 0.17139]])
+
+  def testMultiRNNCell(self):
+    with self.test_session() as sess:
+      with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
+        x = tf.zeros([1, 2])
+        m = tf.zeros([1, 4])
+        _, ml = rnn_cell.MultiRNNCell([rnn_cell.GRUCell(2)] * 2)(x, m)
+        sess.run([tf.variables.initialize_all_variables()])
+        res = sess.run(ml, {x.name: np.array([[1., 1.]]),
+                            m.name: np.array([[0.1, 0.1, 0.1, 0.1]])})
+        # The numbers in results were not calculated, this is just a smoke test.
+        self.assertAllClose(res, [[0.175991, 0.175991,
+                                   0.13248, 0.13248]])
+
+
+if __name__ == "__main__":
+  tf.test.main()