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attention_layer_test.py
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attention_layer_test.py
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'''
Created on Jul 11, 2016
@author: lxh5147
'''
import unittest
from keras.layers import Input
from attention_layer import Attention, SequenceToSequenceEncoder, SequenceToVectorEncoder, shape, HierarchicalAttention, MLPClassifierLayer, ClassifierWithHierarchicalAttention
from keras import backend as K
import numpy as np
from attention_exp import faked_dataset
class AttentionLayerTest(unittest.TestCase):
def test_attention(self):
attention = Attention(attention_weight_vector_dim=5)
x = Input(shape=(5, 10))
y = attention(x)
self.assertEqual(shape(y), (None, 10), "y")
self.assertEqual(hasattr(y, '_keras_history'), True, "y")
def test_transform_sequence_to_sequence(self):
tensor_input = Input(shape=(5, 10))
output_dim = 20
sequence_to_sequence_encoder = SequenceToSequenceEncoder(output_dim)
output_sequence = sequence_to_sequence_encoder(tensor_input)
self.assertEqual(shape(output_sequence), (None, 5, 40), "output_sequence")
self.assertEqual(output_sequence._keras_shape, (None, 5, 40), "output_sequence")
def test_transform_sequence_to_vector_encoder(self):
output_dim = 20
sequence_to_vector_encoder = SequenceToVectorEncoder(output_dim)
tensor_input = Input(shape=(5, 10))
output_vector = sequence_to_vector_encoder(tensor_input)
self.assertEqual(shape(output_vector), (None, 20), "output_vector")
self.assertEqual(output_vector._keras_shape, (None, 20), "output_vector")
def test_build_hierarchical_attention_layer_inputs(self):
# time_steps* documents * sections* sentences * words
input_shape = (7, 8, 5, 6, 9)
# record, document,section,sentence,word
input_feature_dims = (20, 10, 50, 60, 30)
# document, section, sentence, word
inputs = HierarchicalAttention.build_inputs(input_shape, input_feature_dims)
self.assertEqual(len(inputs) , len(input_feature_dims) + 1, "inputs")
self.assertEqual(shape(inputs[0]), (None, 7, 8, 5, 6, 9), "inputs") # original input
self.assertEqual(shape(inputs[1]), (None, 7, 8, 5, 6, 9, 30), "inputs") # word features
self.assertEqual(shape(inputs[2]), (None, 7, 8, 5, 6, 60), "inputs") # sentence features
self.assertEqual(shape(inputs[3]), (None, 7, 8, 5, 50), "inputs") # section features
self.assertEqual(shape(inputs[4]), (None, 7, 8, 10), "inputs") # document features
self.assertEqual(shape(inputs[5]), (None, 7, 20), "inputs") # snapshot features
def test_hierarchical_attention_layer_inputs(self):
# snapshots* documents * sections* sentences * words
input_shape = (7, 8, 5, 6, 9)
# snapshot, document,section,sentence,word
input_feature_dims = (20, 10, 50, 60, 30)
# document, section, sentence, word
attention_output_dims = (45, 35, 25, 65)
# document, section, sentence, word
attention_weight_vector_dims = (82, 72, 62, 52)
# embedding
embedding_rows = 1024
embedding_dim = 50
initial_embedding = np.random.random((embedding_rows, embedding_dim))
inputs = HierarchicalAttention.build_inputs(input_shape, input_feature_dims)
hierarchical_attention = HierarchicalAttention(input_feature_dims[0], attention_output_dims, attention_weight_vector_dims, embedding_rows, embedding_dim, initial_embedding, use_sequence_to_vector_encoder=False)
output = hierarchical_attention(inputs)
self.assertEqual(shape(output), (None, 7, 20 + 45 * 2), "output")
def test_mlp_softmax_classifier(self):
x = Input(shape=(5, 10))
output_dim = 100
hidden_unit_numbers = (5, 20) # 5--> first hidden layer, 20 --> second hidden layer
hidden_unit_activation_functions = ("relu", "relu")
mlp_softmax_classifier = MLPClassifierLayer(output_dim, hidden_unit_numbers, hidden_unit_activation_functions)
y = mlp_softmax_classifier(x)
self.assertEqual(shape(y), (None, 5, 100), "y")
def test_hierarchical_attention_model(self):
# time_steps* documents * sections* sentences * words
input_shape = (7, 8, 5, 6, 9)
# record, document,section,sentence,word
input_feature_dims = (20, 10, 50, 60, 30)
# document, section, sentence, word
output_dims = (45, 35, 25, 65)
# document, section, sentence, word
attention_weight_vector_dims = (82, 72, 62, 52)
# embedding
vocabulary_size = 1024
word_embedding_dim = 50
# classifier
output_dim = 100
hidden_unit_numbers = (5, 20) # 5--> first hidden layer, 20 --> second hidden layer
hidden_unit_activation_functions = ("relu", "relu")
use_sequence_to_vector_encoder = False
initial_embedding = np.random.random((vocabulary_size, word_embedding_dim))
classifier = ClassifierWithHierarchicalAttention(input_feature_dims[0], output_dims, attention_weight_vector_dims, vocabulary_size, word_embedding_dim, initial_embedding, use_sequence_to_vector_encoder, output_dim, hidden_unit_numbers, hidden_unit_activation_functions)
# check inputs of model
inputs = HierarchicalAttention.build_inputs(input_shape, input_feature_dims)
self.assertEqual(len(inputs) , len(input_feature_dims) + 1, "inputs")
self.assertEqual(shape(inputs[0]), (None, 7, 8, 5, 6, 9), "inputs") # original input
self.assertEqual(shape(inputs[1]), (None, 7, 8, 5, 6, 9, 30), "inputs") # word features
self.assertEqual(shape(inputs[2]), (None, 7, 8, 5, 6, 60), "inputs") # sentence features
self.assertEqual(shape(inputs[3]), (None, 7, 8, 5, 50), "inputs") # section features
self.assertEqual(shape(inputs[4]), (None, 7, 8, 10), "inputs") # document features
self.assertEqual(shape(inputs[5]), (None, 7, 20), "inputs") # snapshot features
# check output
output = classifier(inputs)
self.assertEqual(shape(output), (None, 7, 100), "y")
def test_attention_layer_by_run(self):
input_shape = (7, 8, 5, 6, 9)
# record, document,section,sentence,word
input_feature_dims = (20, 10, 50, 60, 30)
# document, section, sentence, word
attention_output_dims = (45, 35, 25, 65)
# document, section, sentence, word
attention_weight_vector_dims = (82, 72, 62, 52)
# embedding
embedding_rows = 200
embedding_dim = 50
# classifier
initial_embedding = np.random.random((embedding_rows, embedding_dim))
inputs = HierarchicalAttention.build_inputs(input_shape, input_feature_dims)
hierarchical_attention = HierarchicalAttention(input_feature_dims[0], attention_output_dims, attention_weight_vector_dims, embedding_rows, embedding_dim, initial_embedding, use_sequence_to_vector_encoder=False)
output = hierarchical_attention(inputs)
total = 2
output_dim = 10
timesteps = input_shape[0]
x_train, _ = faked_dataset(inputs, total, timesteps, embedding_rows, output_dim)
# build feed_dic
feed_dict = {}
for i in range(len(inputs)):
feed_dict[inputs[i]] = x_train[i]
feed_dict[K.learning_phase()] = 1
# tf.initialize_all_variables()
# y_out is fine 2,7, 110
y_out = K.get_session().run(output, feed_dict=feed_dict)
self.assertEquals(y_out.shape , (2, 7, 110), "y_out")
def test_softmax_layer_by_run(self):
input_sequence = Input(shape=(7, 110))
output_dim = 5
hidden_unit_numbers = (5, 20) # 5--> first hidden layer, 20 --> second hidden layer
hidden_unit_activation_functions = ("relu", "relu")
mlp_softmax_classifier = MLPClassifierLayer(output_dim, hidden_unit_numbers, hidden_unit_activation_functions)
output = mlp_softmax_classifier(input_sequence)
# build feed_dic
total = 4
feed_dict = {}
feed_dict[input_sequence] = np.random.random((total,) + shape(input_sequence)[1:])
feed_dict[K.learning_phase()] = 1
# tf.initialize_all_variables()
# y_out is fine 2,7, 110
y_out = K.get_session().run(output, feed_dict=feed_dict)
self.assertEqual(y_out.shape , (total, shape(input_sequence)[1], output_dim) , "y_out")
def test_attention_with_classifier_layer_by_run(self):
input_shape = (7, 8, 5, 6, 9)
# record, document,section,sentence,word
input_feature_dims = (20, 10, 50, 60, 30)
# document, section, sentence, word
attention_output_dims = (45, 35, 25, 65)
# document, section, sentence, word
attention_weight_vector_dims = (82, 72, 62, 52)
# embedding
embedding_rows = 200
embedding_dim = 50
output_dim = 5
# hidden_unit_numbers=(5, 20) # 5--> first hidden layer, 20 --> second hidden layer
# drop_out_rates = ( 0.5, 0.6)
hidden_unit_numbers = () # 5--> first hidden layer, 20 --> second hidden layer
hidden_unit_activation_functions = ()
# classifier
use_sequence_to_vector_encoder = False
initial_embedding = np.random.random((embedding_rows, embedding_dim))
inputs = HierarchicalAttention.build_inputs(input_shape, input_feature_dims)
classifier = ClassifierWithHierarchicalAttention(input_feature_dims[0], attention_output_dims, attention_weight_vector_dims,
embedding_rows, embedding_dim, initial_embedding, use_sequence_to_vector_encoder,
output_dim, hidden_unit_numbers, hidden_unit_activation_functions)
output = classifier(inputs)
total = 2
timesteps = input_shape[0]
x_train, _ = faked_dataset(inputs, total, timesteps, embedding_rows, output_dim)
# build feed_dic
feed_dict = {}
for i in range(len(inputs)):
feed_dict[inputs[i]] = x_train[i]
feed_dict[K.learning_phase()] = 1
y_out = K.get_session().run(output, feed_dict=feed_dict)
self.assertEqual(y_out.shape , (total , timesteps, output_dim), "y_out")
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
unittest.main()