diff options
Diffstat (limited to 'tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py')
-rw-r--r-- | tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py | 39 |
1 files changed, 0 insertions, 39 deletions
diff --git a/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py b/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py index a06943b108..ad25eb226c 100644 --- a/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/cudnn_recurrent_test.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import time - from absl.testing import parameterized import numpy as np @@ -33,43 +31,6 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class CuDNNTest(test.TestCase, parameterized.TestCase): @test_util.run_in_graph_and_eager_modes() - def test_cudnn_rnn_timing(self): - if test.is_gpu_available(cuda_only=True): - with self.test_session(use_gpu=True): - input_size = 10 - timesteps = 6 - units = 2 - num_samples = 32 - - for rnn_type in ['lstm', 'gru']: - times = [] - for use_cudnn in [True, False]: - start_time = time.time() - inputs = keras.layers.Input(shape=(None, input_size)) - if use_cudnn: - if rnn_type == 'lstm': - layer = keras.layers.CuDNNLSTM(units) - else: - layer = keras.layers.CuDNNGRU(units) - else: - if rnn_type == 'lstm': - layer = keras.layers.LSTM(units) - else: - layer = keras.layers.GRU(units) - outputs = layer(inputs) - - optimizer = RMSPropOptimizer(learning_rate=0.001) - model = keras.models.Model(inputs, outputs) - model.compile(optimizer, 'mse') - - x = np.random.random((num_samples, timesteps, input_size)) - y = np.random.random((num_samples, units)) - model.fit(x, y, epochs=4, batch_size=32) - - times.append(time.time() - start_time) - self.assertGreater(times[1], times[0]) - - @test_util.run_in_graph_and_eager_modes() def test_cudnn_rnn_basics(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): |