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Diffstat (limited to 'tensorflow/python/training/rmsprop_test.py')
| -rw-r--r-- | tensorflow/python/training/rmsprop_test.py | 125 |
1 files changed, 125 insertions, 0 deletions
diff --git a/tensorflow/python/training/rmsprop_test.py b/tensorflow/python/training/rmsprop_test.py index 541b3e0942..499e452d90 100644 --- a/tensorflow/python/training/rmsprop_test.py +++ b/tensorflow/python/training/rmsprop_test.py @@ -26,6 +26,131 @@ import tensorflow as tf class RMSPropOptimizerTest(tf.test.TestCase): + def _rmsprop_update_numpy(self, var, g, rms, mom, lr, decay, momentum, + epsilon): + rms_t = rms * decay + (1-decay) * g * g + mom_t = momentum * mom + lr * g / np.sqrt(rms_t + epsilon) + var_t = var - mom_t + return var_t, rms_t, mom_t + + def testSparseWithMomentum(self): + for dtype in [tf.half, tf.float32]: + with self.test_session(): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) + + var0 = tf.Variable(var0_np) + var1 = tf.Variable(var1_np) + grads0_np_indices = np.array([0, 1], dtype=np.int32) + grads0 = tf.IndexedSlices(tf.constant(grads0_np), + tf.constant(grads0_np_indices), + tf.constant([2])) + grads1_np_indices = np.array([0, 1], dtype=np.int32) + grads1 = tf.IndexedSlices(tf.constant(grads1_np), + tf.constant(grads1_np_indices), + tf.constant([2])) + opt = tf.train.RMSPropOptimizer(learning_rate=2.0, decay=0.9, + momentum=0.5, epsilon=1e-5) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + tf.initialize_all_variables().run() + + rms0 = opt.get_slot(var0, "rms") + self.assertTrue(rms0 is not None) + rms1 = opt.get_slot(var1, "rms") + self.assertTrue(rms1 is not None) + mom0 = opt.get_slot(var0, "momentum") + self.assertTrue(mom0 is not None) + mom1 = opt.get_slot(var1, "momentum") + self.assertTrue(mom1 is not None) + + rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 4 steps of RMSProp + for t in range(1, 5): + update.run() + + var0_np, rms0_np, mom0_np = self._rmsprop_update_numpy(var0_np, + grads0_np, rms0_np, mom0_np, 2.0, 0.9, 0.5, 1e-5) + var1_np, rms1_np, mom1_np = self._rmsprop_update_numpy(var1_np, + grads1_np, rms1_np, mom1_np, 2.0, 0.9, 0.5, 1e-5) + + # Validate updated params + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + def testSparseWithoutMomentum(self): + for dtype in [tf.half, tf.float32]: + with self.test_session(): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) + + var0 = tf.Variable(var0_np) + var1 = tf.Variable(var1_np) + grads0_np_indices = np.array([0, 1], dtype=np.int32) + grads0 = tf.IndexedSlices(tf.constant(grads0_np), + tf.constant(grads0_np_indices), + tf.constant([2])) + grads1_np_indices = np.array([0, 1], dtype=np.int32) + grads1 = tf.IndexedSlices(tf.constant(grads1_np), + tf.constant(grads1_np_indices), + tf.constant([2])) + opt = tf.train.RMSPropOptimizer(learning_rate=2.0, decay=0.9, + momentum=0.0, epsilon=1.0) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + tf.initialize_all_variables().run() + + rms0 = opt.get_slot(var0, "rms") + self.assertTrue(rms0 is not None) + rms1 = opt.get_slot(var1, "rms") + self.assertTrue(rms1 is not None) + mom0 = opt.get_slot(var0, "momentum") + self.assertTrue(mom0 is not None) + mom1 = opt.get_slot(var1, "momentum") + self.assertTrue(mom1 is not None) + + rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 4 steps of RMSProp + for t in range(1, 5): + update.run() + + var0_np, rms0_np, mom0_np = self._rmsprop_update_numpy(var0_np, + grads0_np, rms0_np, mom0_np, 2.0, 0.9, 0.0, 1.0) + var1_np, rms1_np, mom1_np = self._rmsprop_update_numpy(var1_np, + grads1_np, rms1_np, mom1_np, 2.0, 0.9, 0.0, 1.0) + + # Validate updated params + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + def testWithoutMomentum(self): for dtype in [tf.half, tf.float32]: with self.test_session(): |