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414 lines
20 KiB
Python
414 lines
20 KiB
Python
7 years ago
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.layers.core import Dense
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import itertools
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import collections
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import helper
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def _print_success_message():
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print('Tests Passed')
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def test_text_to_ids(text_to_ids):
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test_source_text = 'new jersey is sometimes quiet during autumn , and it is snowy in april .\nthe united states is usually chilly during july , and it is usually freezing in november .\ncalifornia is usually quiet during march , and it is usually hot in june .\nthe united states is sometimes mild during june , and it is cold in september .'
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test_target_text = 'new jersey est parfois calme pendant l\' automne , et il est neigeux en avril .\nles états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\ncalifornia est généralement calme en mars , et il est généralement chaud en juin .\nles états-unis est parfois légère en juin , et il fait froid en septembre .'
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test_source_text = test_source_text.lower()
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test_target_text = test_target_text.lower()
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source_vocab_to_int, source_int_to_vocab = helper.create_lookup_tables(test_source_text)
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target_vocab_to_int, target_int_to_vocab = helper.create_lookup_tables(test_target_text)
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test_source_id_seq, test_target_id_seq = text_to_ids(test_source_text, test_target_text, source_vocab_to_int, target_vocab_to_int)
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assert len(test_source_id_seq) == len(test_source_text.split('\n')),\
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'source_id_text has wrong length, it should be {}.'.format(len(test_source_text.split('\n')))
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assert len(test_target_id_seq) == len(test_target_text.split('\n')), \
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'target_id_text has wrong length, it should be {}.'.format(len(test_target_text.split('\n')))
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target_not_iter = [type(x) for x in test_source_id_seq if not isinstance(x, collections.Iterable)]
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assert not target_not_iter,\
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'Element in source_id_text is not iteratable. Found type {}'.format(target_not_iter[0])
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target_not_iter = [type(x) for x in test_target_id_seq if not isinstance(x, collections.Iterable)]
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assert not target_not_iter, \
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'Element in target_id_text is not iteratable. Found type {}'.format(target_not_iter[0])
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source_changed_length = [(words, word_ids)
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for words, word_ids in zip(test_source_text.split('\n'), test_source_id_seq)
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if len(words.split()) != len(word_ids)]
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assert not source_changed_length,\
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'Source text changed in size from {} word(s) to {} id(s): {}'.format(
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len(source_changed_length[0][0].split()), len(source_changed_length[0][1]), source_changed_length[0][1])
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target_missing_end = [word_ids for word_ids in test_target_id_seq if word_ids[-1] != target_vocab_to_int['<EOS>']]
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assert not target_missing_end,\
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'Missing <EOS> id at the end of {}'.format(target_missing_end[0])
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target_bad_size = [(words.split(), word_ids)
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for words, word_ids in zip(test_target_text.split('\n'), test_target_id_seq)
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if len(word_ids) != len(words.split()) + 1]
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assert not target_bad_size,\
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'Target text incorrect size. {} should be length {}'.format(
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target_bad_size[0][1], len(target_bad_size[0][0]) + 1)
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source_bad_id = [(word, word_id)
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for word, word_id in zip(
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[word for sentence in test_source_text.split('\n') for word in sentence.split()],
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itertools.chain.from_iterable(test_source_id_seq))
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if source_vocab_to_int[word] != word_id]
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assert not source_bad_id,\
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'Source word incorrectly converted from {} to id {}.'.format(source_bad_id[0][0], source_bad_id[0][1])
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target_bad_id = [(word, word_id)
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for word, word_id in zip(
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[word for sentence in test_target_text.split('\n') for word in sentence.split()],
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[word_id for word_ids in test_target_id_seq for word_id in word_ids[:-1]])
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if target_vocab_to_int[word] != word_id]
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assert not target_bad_id,\
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'Target word incorrectly converted from {} to id {}.'.format(target_bad_id[0][0], target_bad_id[0][1])
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_print_success_message()
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def test_model_inputs(model_inputs):
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with tf.Graph().as_default():
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input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()
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# Check type
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assert input_data.op.type == 'Placeholder',\
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'Input is not a Placeholder.'
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assert targets.op.type == 'Placeholder',\
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'Targets is not a Placeholder.'
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assert lr.op.type == 'Placeholder',\
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'Learning Rate is not a Placeholder.'
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assert keep_prob.op.type == 'Placeholder', \
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'Keep Probability is not a Placeholder.'
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assert target_sequence_length.op.type == 'Placeholder', \
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'Target Sequence Length is not a Placeholder.'
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assert max_target_sequence_length.op.type == 'Max', \
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'Max Target Sequence Length is not a Placeholder.'
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assert source_sequence_length.op.type == 'Placeholder', \
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'Source Sequence Length is not a Placeholder.'
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# Check name
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assert input_data.name == 'input:0',\
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'Input has bad name. Found name {}'.format(input_data.name)
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assert target_sequence_length.name == 'target_sequence_length:0',\
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'Target Sequence Length has bad name. Found name {}'.format(target_sequence_length.name)
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assert source_sequence_length.name == 'source_sequence_length:0',\
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'Source Sequence Length has bad name. Found name {}'.format(source_sequence_length.name)
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assert keep_prob.name == 'keep_prob:0', \
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'Keep Probability has bad name. Found name {}'.format(keep_prob.name)
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assert tf.assert_rank(input_data, 2, message='Input data has wrong rank')
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assert tf.assert_rank(targets, 2, message='Targets has wrong rank')
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assert tf.assert_rank(lr, 0, message='Learning Rate has wrong rank')
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assert tf.assert_rank(keep_prob, 0, message='Keep Probability has wrong rank')
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assert tf.assert_rank(target_sequence_length, 1, message='Target Sequence Length has wrong rank')
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assert tf.assert_rank(max_target_sequence_length, 0, message='Max Target Sequence Length has wrong rank')
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assert tf.assert_rank(source_sequence_length, 1, message='Source Sequence Lengthhas wrong rank')
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_print_success_message()
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def test_encoding_layer(encoding_layer):
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rnn_size = 512
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batch_size = 64
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num_layers = 3
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source_sequence_len = 22
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source_vocab_size = 20
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encoding_embedding_size = 30
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with tf.Graph().as_default():
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rnn_inputs = tf.placeholder(tf.int32, [batch_size,
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source_sequence_len])
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source_sequence_length = tf.placeholder(tf.int32,
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(None,),
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name='source_sequence_length')
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keep_prob = tf.placeholder(tf.float32)
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enc_output, states = encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob,
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source_sequence_length, source_vocab_size,
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encoding_embedding_size)
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assert len(states) == num_layers,\
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'Found {} state(s). It should be {} states.'.format(len(states), num_layers)
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bad_types = [type(state) for state in states if not isinstance(state, tf.contrib.rnn.LSTMStateTuple)]
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assert not bad_types,\
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'Found wrong type: {}'.format(bad_types[0])
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bad_shapes = [state_tensor.get_shape()
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for state in states
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for state_tensor in state
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if state_tensor.get_shape().as_list() not in [[None, rnn_size], [batch_size, rnn_size]]]
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assert not bad_shapes,\
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'Found wrong shape: {}'.format(bad_shapes[0])
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_print_success_message()
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def test_decoding_layer(decoding_layer):
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batch_size = 64
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vocab_size = 1000
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embedding_size = 200
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sequence_length = 22
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rnn_size = 512
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num_layers = 3
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target_vocab_to_int = {'<EOS>': 1, '<GO>': 3}
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with tf.Graph().as_default():
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target_sequence_length_p = tf.placeholder(tf.int32, (None,), name='target_sequence_length')
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max_target_sequence_length = tf.reduce_max(target_sequence_length_p, name='max_target_len')
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dec_input = tf.placeholder(tf.int32, [batch_size, sequence_length])
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dec_embed_input = tf.placeholder(tf.float32, [batch_size, sequence_length, embedding_size])
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dec_embeddings = tf.placeholder(tf.float32, [vocab_size, embedding_size])
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keep_prob = tf.placeholder(tf.float32)
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state = tf.contrib.rnn.LSTMStateTuple(
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tf.placeholder(tf.float32, [None, rnn_size]),
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tf.placeholder(tf.float32, [None, rnn_size]))
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encoder_state = (state, state, state)
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train_decoder_output, infer_logits_output = decoding_layer( dec_input,
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encoder_state,
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target_sequence_length_p,
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max_target_sequence_length,
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rnn_size,
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num_layers,
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target_vocab_to_int,
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vocab_size,
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batch_size,
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keep_prob,
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embedding_size)
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assert isinstance(train_decoder_output, tf.contrib.seq2seq.BasicDecoderOutput),\
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'Found wrong type: {}'.format(type(train_decoder_output))
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assert isinstance(infer_logits_output, tf.contrib.seq2seq.BasicDecoderOutput),\
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'Found wrong type: {}'.format(type(infer_logits_output))
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assert train_decoder_output.rnn_output.get_shape().as_list() == [batch_size, None, vocab_size], \
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'Wrong shape returned. Found {}'.format(train_decoder_output.rnn_output.get_shape())
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assert infer_logits_output.sample_id.get_shape().as_list() == [batch_size, None], \
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'Wrong shape returned. Found {}'.format(infer_logits_output.sample_id.get_shape())
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_print_success_message()
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def test_seq2seq_model(seq2seq_model):
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batch_size = 64
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vocab_size = 300
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embedding_size = 100
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sequence_length = 22
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rnn_size = 512
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num_layers = 3
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target_vocab_to_int = {'<EOS>': 1, '<GO>': 3}
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with tf.Graph().as_default():
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dec_input = tf.placeholder(tf.int32, [batch_size, sequence_length])
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dec_embed_input = tf.placeholder(tf.float32, [batch_size, sequence_length, embedding_size])
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dec_embeddings = tf.placeholder(tf.float32, [vocab_size, embedding_size])
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keep_prob = tf.placeholder(tf.float32)
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enc_state = tf.contrib.rnn.LSTMStateTuple(
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tf.placeholder(tf.float32, [None, rnn_size]),
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tf.placeholder(tf.float32, [None, rnn_size]))
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input_data = tf.placeholder(tf.int32, [batch_size, sequence_length])
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target_data = tf.placeholder(tf.int32, [batch_size, sequence_length])
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keep_prob = tf.placeholder(tf.float32)
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source_sequence_length = tf.placeholder(tf.int32, (None,), name='source_sequence_length')
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target_sequence_length_p = tf.placeholder(tf.int32, (None,), name='target_sequence_length')
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max_target_sequence_length = tf.reduce_max(target_sequence_length_p, name='max_target_len')
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train_decoder_output, infer_logits_output = seq2seq_model( input_data,
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target_data,
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keep_prob,
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batch_size,
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source_sequence_length,
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target_sequence_length_p,
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max_target_sequence_length,
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vocab_size,
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vocab_size,
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embedding_size,
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embedding_size,
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rnn_size,
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num_layers,
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target_vocab_to_int)
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# input_data, target_data, keep_prob, batch_size, sequence_length,
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# 200, target_vocab_size, 64, 80, rnn_size, num_layers, target_vocab_to_int)
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assert isinstance(train_decoder_output, tf.contrib.seq2seq.BasicDecoderOutput),\
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'Found wrong type: {}'.format(type(train_decoder_output))
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assert isinstance(infer_logits_output, tf.contrib.seq2seq.BasicDecoderOutput),\
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'Found wrong type: {}'.format(type(infer_logits_output))
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assert train_decoder_output.rnn_output.get_shape().as_list() == [batch_size, None, vocab_size], \
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'Wrong shape returned. Found {}'.format(train_decoder_output.rnn_output.get_shape())
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assert infer_logits_output.sample_id.get_shape().as_list() == [batch_size, None], \
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'Wrong shape returned. Found {}'.format(infer_logits_output.sample_id.get_shape())
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_print_success_message()
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def test_sentence_to_seq(sentence_to_seq):
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sentence = 'this is a test sentence'
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vocab_to_int = {'<PAD>': 0, '<EOS>': 1, '<UNK>': 2, 'this': 3, 'is': 6, 'a': 5, 'sentence': 4}
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output = sentence_to_seq(sentence, vocab_to_int)
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assert len(output) == 5,\
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'Wrong length. Found a length of {}'.format(len(output))
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assert output[3] == 2,\
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'Missing <UNK> id.'
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assert np.array_equal(output, [3, 6, 5, 2, 4]),\
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'Incorrect ouput. Found {}'.format(output)
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_print_success_message()
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def test_process_encoding_input(process_encoding_input):
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batch_size = 2
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seq_length = 3
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target_vocab_to_int = {'<GO>': 3}
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with tf.Graph().as_default():
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target_data = tf.placeholder(tf.int32, [batch_size, seq_length])
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dec_input = process_encoding_input(target_data, target_vocab_to_int, batch_size)
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assert dec_input.get_shape() == (batch_size, seq_length),\
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'Wrong shape returned. Found {}'.format(dec_input.get_shape())
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test_target_data = [[10, 20, 30], [40, 18, 23]]
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with tf.Session() as sess:
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test_dec_input = sess.run(dec_input, {target_data: test_target_data})
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assert test_dec_input[0][0] == target_vocab_to_int['<GO>'] and\
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test_dec_input[1][0] == target_vocab_to_int['<GO>'],\
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'Missing GO Id.'
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_print_success_message()
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def test_decoding_layer_train(decoding_layer_train):
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batch_size = 64
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vocab_size = 1000
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embedding_size = 200
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sequence_length = 22
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rnn_size = 512
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num_layers = 3
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with tf.Graph().as_default():
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with tf.variable_scope("decoding") as decoding_scope:
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# dec_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(rnn_size)] * num_layers)
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|
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dec_embed_input = tf.placeholder(tf.float32, [batch_size, sequence_length, embedding_size])
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keep_prob = tf.placeholder(tf.float32)
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target_sequence_length_p = tf.placeholder(tf.int32, (None,), name='target_sequence_length')
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max_target_sequence_length = tf.reduce_max(target_sequence_length_p, name='max_target_len')
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|
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for layer in range(num_layers):
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with tf.variable_scope('decoder_{}'.format(layer)):
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lstm = tf.contrib.rnn.LSTMCell(rnn_size,
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initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
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dec_cell = tf.contrib.rnn.DropoutWrapper(lstm,
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input_keep_prob=keep_prob)
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|
|
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output_layer = Dense(vocab_size,
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|
kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
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name='output_layer')
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# output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)
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|
|
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||
|
|
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||
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|
encoder_state = tf.contrib.rnn.LSTMStateTuple(
|
||
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|
tf.placeholder(tf.float32, [None, rnn_size]),
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|
tf.placeholder(tf.float32, [None, rnn_size]))
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|
|
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||
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train_decoder_output = decoding_layer_train(encoder_state, dec_cell,
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||
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|
dec_embed_input,
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||
|
|
target_sequence_length_p,
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||
|
|
max_target_sequence_length,
|
||
|
|
output_layer,
|
||
|
|
keep_prob)
|
||
|
|
|
||
|
|
# encoder_state, dec_cell, dec_embed_input, sequence_length,
|
||
|
|
# decoding_scope, output_fn, keep_prob)
|
||
|
|
|
||
|
|
|
||
|
|
assert isinstance(train_decoder_output, tf.contrib.seq2seq.BasicDecoderOutput),\
|
||
|
|
'Found wrong type: {}'.format(type(train_decoder_output))
|
||
|
|
|
||
|
|
assert train_decoder_output.rnn_output.get_shape().as_list() == [batch_size, None, vocab_size], \
|
||
|
|
'Wrong shape returned. Found {}'.format(train_decoder_output.rnn_output.get_shape())
|
||
|
|
|
||
|
|
_print_success_message()
|
||
|
|
|
||
|
|
|
||
|
|
def test_decoding_layer_infer(decoding_layer_infer):
|
||
|
|
batch_size = 64
|
||
|
|
vocab_size = 1000
|
||
|
|
sequence_length = 22
|
||
|
|
embedding_size = 200
|
||
|
|
rnn_size = 512
|
||
|
|
num_layers = 3
|
||
|
|
|
||
|
|
with tf.Graph().as_default():
|
||
|
|
with tf.variable_scope("decoding") as decoding_scope:
|
||
|
|
|
||
|
|
dec_embeddings = tf.Variable(tf.random_uniform([vocab_size, embedding_size]))
|
||
|
|
|
||
|
|
dec_embed_input = tf.placeholder(tf.float32, [batch_size, sequence_length, embedding_size])
|
||
|
|
keep_prob = tf.placeholder(tf.float32)
|
||
|
|
target_sequence_length_p = tf.placeholder(tf.int32, (None,), name='target_sequence_length')
|
||
|
|
max_target_sequence_length = tf.reduce_max(target_sequence_length_p, name='max_target_len')
|
||
|
|
|
||
|
|
for layer in range(num_layers):
|
||
|
|
with tf.variable_scope('decoder_{}'.format(layer)):
|
||
|
|
lstm = tf.contrib.rnn.LSTMCell(rnn_size,
|
||
|
|
initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
|
||
|
|
dec_cell = tf.contrib.rnn.DropoutWrapper(lstm,
|
||
|
|
input_keep_prob=keep_prob)
|
||
|
|
|
||
|
|
output_layer = Dense(vocab_size,
|
||
|
|
kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
|
||
|
|
name='output_layer')
|
||
|
|
# output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)
|
||
|
|
|
||
|
|
|
||
|
|
encoder_state = tf.contrib.rnn.LSTMStateTuple(
|
||
|
|
tf.placeholder(tf.float32, [None, rnn_size]),
|
||
|
|
tf.placeholder(tf.float32, [None, rnn_size]))
|
||
|
|
|
||
|
|
infer_logits_output = decoding_layer_infer( encoder_state,
|
||
|
|
dec_cell,
|
||
|
|
dec_embeddings,
|
||
|
|
1,
|
||
|
|
2,
|
||
|
|
max_target_sequence_length,
|
||
|
|
vocab_size,
|
||
|
|
output_layer,
|
||
|
|
batch_size,
|
||
|
|
keep_prob)
|
||
|
|
|
||
|
|
# encoder_state, dec_cell, dec_embeddings, 10, 20,
|
||
|
|
# sequence_length, vocab_size, decoding_scope, output_fn, keep_prob)
|
||
|
|
|
||
|
|
|
||
|
|
assert isinstance(infer_logits_output, tf.contrib.seq2seq.BasicDecoderOutput),\
|
||
|
|
'Found wrong type: {}'.format(type(infer_logits_output))
|
||
|
|
|
||
|
|
assert infer_logits_output.sample_id.get_shape().as_list() == [batch_size, None], \
|
||
|
|
'Wrong shape returned. Found {}'.format(infer_logits_output.sample_id.get_shape())
|
||
|
|
|
||
|
|
_print_success_message()
|