Add file

project_tests.py

@@ -0,0 +1,103 @@
+import numpy as np
+from keras.losses import sparse_categorical_crossentropy
+from keras.models import Sequential
+from keras.preprocessing.text import Tokenizer
+from keras.utils import to_categorical
+
+
+def _test_model(model, input_shape, output_sequence_length, french_vocab_size):
+    if isinstance(model, Sequential):
+        model = model.model
+
+    assert model.input_shape == (None, *input_shape[1:]),\
+        'Wrong input shape. Found input shape {} using parameter input_shape={}'.format(model.input_shape, input_shape)
+
+    assert model.output_shape == (None, output_sequence_length, french_vocab_size),\
+        'Wrong output shape. Found output shape {} using parameters output_sequence_length={} and french_vocab_size={}'\
+            .format(model.output_shape, output_sequence_length, french_vocab_size)
+
+    assert len(model.loss_functions) > 0,\
+        'No loss function set.  Apply the `compile` function to the model.'
+
+    assert sparse_categorical_crossentropy in model.loss_functions,\
+        'Not using `sparse_categorical_crossentropy` function for loss.'
+
+
+def test_tokenize(tokenize):
+    sentences = [
+        'The quick brown fox jumps over the lazy dog .',
+        'By Jove , my quick study of lexicography won a prize .',
+        'This is a short sentence .']
+    tokenized_sentences, tokenizer = tokenize(sentences)
+    assert tokenized_sentences == tokenizer.texts_to_sequences(sentences),\
+        'Tokenizer returned and doesn\'t generate the same sentences as the tokenized sentences returned. '
+
+
+def test_pad(pad):
+    tokens = [
+        [i for i in range(4)],
+        [i for i in range(6)],
+        [i for i in range(3)]]
+    padded_tokens = pad(tokens)
+    padding_id = padded_tokens[0][-1]
+    true_padded_tokens = np.array([
+        [i for i in range(4)] + [padding_id]*2,
+        [i for i in range(6)],
+        [i for i in range(3)] + [padding_id]*3])
+    assert isinstance(padded_tokens, np.ndarray),\
+        'Pad returned the wrong type.  Found {} type, expected numpy array type.'
+    assert np.all(padded_tokens == true_padded_tokens), 'Pad returned the wrong results.'
+
+    padded_tokens_using_length = pad(tokens, 9)
+    assert np.all(padded_tokens_using_length == np.concatenate((true_padded_tokens, np.full((3, 3), padding_id)), axis=1)),\
+        'Using length argument return incorrect results'
+
+
+def test_simple_model(simple_model):
+    input_shape = (137861, 21, 1)
+    output_sequence_length = 21
+    english_vocab_size = 199
+    french_vocab_size = 344
+
+    model = simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size)
+    _test_model(model, input_shape, output_sequence_length, french_vocab_size)
+
+
+def test_embed_model(embed_model):
+    input_shape = (137861, 21)
+    output_sequence_length = 21
+    english_vocab_size = 199
+    french_vocab_size = 344
+
+    model = embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size)
+    _test_model(model, input_shape, output_sequence_length, french_vocab_size)
+
+
+def test_encdec_model(encdec_model):
+    input_shape = (137861, 15, 1)
+    output_sequence_length = 21
+    english_vocab_size = 199
+    french_vocab_size = 344
+
+    model = encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size)
+    _test_model(model, input_shape, output_sequence_length, french_vocab_size)
+
+
+def test_bd_model(bd_model):
+    input_shape = (137861, 21, 1)
+    output_sequence_length = 21
+    english_vocab_size = 199
+    french_vocab_size = 344
+
+    model = bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size)
+    _test_model(model, input_shape, output_sequence_length, french_vocab_size)
+
+
+def test_model_final(model_final):
+    input_shape = (137861, 15)
+    output_sequence_length = 21
+    english_vocab_size = 199
+    french_vocab_size = 344
+
+    model = model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size)
+    _test_model(model, input_shape, output_sequence_length, french_vocab_size)