Add deprecation notice for BasicRNNCell, which will be replaced by keras.SimpleRNNCell.

PiperOrigin-RevId: 215249611

1 files changed, 39 insertions, 0 deletions

diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py
index 05ad9f6336..2f6963f6b8 100644
--- a/tensorflow/python/kernel_tests/rnn_test.py
+++ b/tensorflow/python/kernel_tests/rnn_test.py
@@ -535,6 +535,45 @@ class RNNTest(test.TestCase):
     self.assertAllClose(tf_out, k_out)
     self.assertAllClose(tf_state, k_state)
 
+  def testSimpleRNNCellAndBasicRNNCellComparison(self):
+    input_shape = 10
+    output_shape = 5
+    timestep = 4
+    batch = 20
+    (x_train, _), _ = testing_utils.get_test_data(
+        train_samples=batch,
+        test_samples=0,
+        input_shape=(timestep, input_shape),
+        num_classes=output_shape)
+    fix_weights_generator = keras.layers.SimpleRNNCell(output_shape)
+    fix_weights_generator.build((None, input_shape))
+    # The SimpleRNNCell contains 3 weights: kernel, recurrent_kernel, and bias
+    # The BasicRNNCell contains 2 weight: kernel and bias, where kernel is
+    # zipped [kernel, recurrent_kernel] in SimpleRNNCell.
+    keras_weights = fix_weights_generator.get_weights()
+    kernel, recurrent_kernel, bias = keras_weights
+    tf_weights = [np.concatenate((kernel, recurrent_kernel)), bias]
+
+    with self.test_session(graph=ops_lib.Graph()) as sess:
+      inputs = array_ops.placeholder(
+          dtypes.float32, shape=(None, timestep, input_shape))
+      cell = keras.layers.SimpleRNNCell(output_shape)
+      k_out, k_state = rnn.dynamic_rnn(
+          cell, inputs, dtype=dtypes.float32)
+      cell.set_weights(keras_weights)
+      [k_out, k_state] = sess.run([k_out, k_state], {inputs: x_train})
+    with self.test_session(graph=ops_lib.Graph()) as sess:
+      inputs = array_ops.placeholder(
+          dtypes.float32, shape=(None, timestep, input_shape))
+      cell = rnn_cell_impl.BasicRNNCell(output_shape)
+      tf_out, tf_state = rnn.dynamic_rnn(
+          cell, inputs, dtype=dtypes.float32)
+      cell.set_weights(tf_weights)
+      [tf_out, tf_state] = sess.run([tf_out, tf_state], {inputs: x_train})
+
+    self.assertAllClose(tf_out, k_out)
+    self.assertAllClose(tf_state, k_state)
+
   def testBasicLSTMCellInterchangeWithLSTMCell(self):
     with self.session(graph=ops_lib.Graph()) as sess:
       basic_cell = rnn_cell_impl.BasicLSTMCell(1)