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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for embedding layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.eager import backprop
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
from tensorflow.python.training import adagrad
class EmbeddingTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes(use_gpu=False)
def test_embedding(self):
testing_utils.layer_test(
keras.layers.Embedding,
kwargs={'output_dim': 4,
'input_dim': 10,
'input_length': 2},
input_shape=(3, 2),
input_dtype='int32',
expected_output_dtype='float32')
testing_utils.layer_test(
keras.layers.Embedding,
kwargs={'output_dim': 4,
'input_dim': 10,
'mask_zero': True},
input_shape=(3, 2),
input_dtype='int32',
expected_output_dtype='float32')
testing_utils.layer_test(
keras.layers.Embedding,
kwargs={'output_dim': 4,
'input_dim': 10,
'mask_zero': True},
input_shape=(3, 4, 2),
input_dtype='int32',
expected_output_dtype='float32')
testing_utils.layer_test(
keras.layers.Embedding,
kwargs={'output_dim': 4,
'input_dim': 10,
'mask_zero': True,
'input_length': (None, 2)},
input_shape=(3, 4, 2),
input_dtype='int32',
expected_output_dtype='float32')
def test_embedding_correctness(self):
with self.cached_session():
layer = keras.layers.Embedding(output_dim=2, input_dim=2)
layer.build((None, 2))
matrix = np.array([[1, 1], [2, 2]])
layer.set_weights([matrix])
inputs = keras.backend.constant([[0, 1, 0]], dtype='int32')
outputs = keras.backend.eval(layer(inputs))
self.assertAllClose(outputs, [[[1, 1], [2, 2], [1, 1]]])
@tf_test_util.run_in_graph_and_eager_modes()
def test_eager_gpu_cpu(self):
l = keras.layers.Embedding(output_dim=2, input_dim=2)
l.build((None, 2))
inputs = keras.backend.constant([[0, 1, 0]], dtype='int32')
with backprop.GradientTape() as tape:
output = l(inputs)
gs = tape.gradient(output, l.weights)
opt = adagrad.AdagradOptimizer(0.1)
opt.apply_gradients(zip(gs, l.weights))
self.assertAllEqual(len(gs), 1)
if __name__ == '__main__':
test.main()
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