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HenryAI
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KerasCodeExamples.txt

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assert len(words_list) == len(train_samples) + len(validation_samples) + len(
test_samples
)
print(f\"Total training samples: {len(train_samples)}\")
print(f\"Total validation samples: {len(validation_samples)}\")
print(f\"Total test samples: {len(test_samples)}\")
Total training samples: 86810
Total validation samples: 4823
Total test samples: 4823
Data input pipeline
We start building our data input pipeline by first preparing the image paths.
base_image_path = os.path.join(base_path, \"words\")
def get_image_paths_and_labels(samples):
paths = []
corrected_samples = []
for (i, file_line) in enumerate(samples):
line_split = file_line.strip()
line_split = line_split.split(\" \")
# Each line split will have this format for the corresponding image:
# part1/part1-part2/part1-part2-part3.png
image_name = line_split[0]
partI = image_name.split(\"-\")[0]
partII = image_name.split(\"-\")[1]
img_path = os.path.join(
base_image_path, partI, partI + \"-\" + partII, image_name + \".png\"
)
if os.path.getsize(img_path):
paths.append(img_path)
corrected_samples.append(file_line.split(\"\n\")[0])
return paths, corrected_samples
train_img_paths, train_labels = get_image_paths_and_labels(train_samples)
validation_img_paths, validation_labels = get_image_paths_and_labels(validation_samples)
test_img_paths, test_labels = get_image_paths_and_labels(test_samples)
Then we prepare the ground-truth labels.
# Find maximum length and the size of the vocabulary in the training data.
train_labels_cleaned = []
characters = set()
max_len = 0
for label in train_labels:
label = label.split(\" \")[-1].strip()
for char in label:
characters.add(char)
max_len = max(max_len, len(label))
train_labels_cleaned.append(label)
print(\"Maximum length: \", max_len)
print(\"Vocab size: \", len(characters))
# Check some label samples.
train_labels_cleaned[:10]
Maximum length: 21
Vocab size: 78
['sure',
'he',
'during',
'of',
'booty',
'gastronomy',
'boy',
'The',
'and',
'in']
Now we clean the validation and the test labels as well.
def clean_labels(labels):
cleaned_labels = []
for label in labels:
label = label.split(\" \")[-1].strip()
cleaned_labels.append(label)
return cleaned_labels
validation_labels_cleaned = clean_labels(validation_labels)
test_labels_cleaned = clean_labels(test_labels)
Building the character vocabulary
Keras provides different preprocessing layers to deal with different modalities of data. This guide provids a comprehensive introduction. Our example involves preprocessing labels at the character level. This means that if there are two labels, e.g. \"cat\" and \"dog\", then our character vocabulary should be {a, c, d,...
AUTOTUNE = tf.data.AUTOTUNE
# Mapping characters to integers.
char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
# Mapping integers back to original characters.
num_to_char = StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)
Resizing images without distortion