def __init__(self, model_weight='mn_model_weight.h5', scale_ratio=1):
self.scale_ratio = scale_ratio
self.characters = sorted([
*set("".join(
sum(ArtsInfo.ArtNames, []) + ArtsInfo.TypeNames +
list(ArtsInfo.MainAttrNames.values()) +
list(ArtsInfo.SubAttrNames.values()) + list(".,+%0123456789")))
])
# Mapping characters to integers
self.char_to_num = StringLookup(vocabulary=list(self.characters),
num_oov_indices=0,
mask_token="")
# Mapping integers back to original characters
self.num_to_char = StringLookup(
vocabulary=self.char_to_num.get_vocabulary(),
oov_token="",
mask_token="",
invert=True)
self.width = 240
self.height = 16
self.max_length = 15
self.build_model(input_shape=(self.width, self.height))
self.model.load_weights(model_weight)
def encode_string_categorical_feature(feature, name, dataset):
# Create a StringLookup layer which will turn strings into integer indices
index = StringLookup()
# Prepare a Dataset that only yields our feature
feature_ds = dataset.map(lambda x, y: x[name])
feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))
# Learn the set of possible string values and assign them a fixed integer index
index.adapt(feature_ds)
# Turn the string input into integer indices
encoded_feature = index(feature)
# Create a CategoryEncoding for our integer indices
encoder = CategoryEncoding(output_mode="binary")
# Prepare a dataset of indices
feature_ds = feature_ds.map(index)
# Learn the space of possible indices
encoder.adapt(feature_ds)
# Apply one-hot encoding to our indices
encoded_feature = encoder(encoded_feature)
return encoded_feature
def build(self, input_shape=None):
self.squeeze = False
if 2 == len(input_shape):
if 1 != input_shape[-1]:
raise ValueError(
'Input 0 of layer {} is incompatible with the layer: if ndim=2 expected axis[-1]=1, found '
'axis[-1]={}. Full shape received: {}'.format(self.name, input_shape[-1], input_shape))
self.squeeze = True
input_shape = input_shape[:1]
self.lookup = StringLookup(vocabulary=self._vocabulary, mask_token=None, oov_token=self.UNK_MARK)
self.lookup.build(input_shape)
if 'adapt' == self.embed_type:
self.embed = AdaptiveEmbedding(
self.adapt_cutoff, self.lookup.vocabulary_size(), self.output_dim, factor=self.adapt_factor,
embeddings_initializer=self.embeddings_initializer)
else:
self.embed = layers.Embedding(
self.lookup.vocabulary_size(), self.output_dim, embeddings_initializer=self.embeddings_initializer)
if 'dense_auto' == self.embed_type:
self.embed.build(input_shape)
else: # 'dense_cpu' == self.embed_type
with tf.device('cpu:0'):
self.embed.build(input_shape)
super().build(input_shape)
def _category_lookup(self, params: dict):
key, input_layer = self._get_input_layer(params)
num_oov_buckets = params.get('num_oov_buckets', 0)
if input_layer.dtype == 'string':
if 'vocabulary_file' in params.keys():
return StringLookup(max_tokens=params['vocabulary_size'],
num_oov_indices=num_oov_buckets,
mask_token=None,
vocabulary=params['vocabulary_file'])(input_layer)
elif 'vocabulary_list' in params.keys():
return StringLookup(max_tokens=len(params['vocabulary_list']) + num_oov_buckets,
num_oov_indices=num_oov_buckets,
mask_token=None,
vocabulary=params['vocabulary_list'])(input_layer)
else:
if 'vocabulary_file' in params.keys():
return IntegerLookup(max_values=params['vocabulary_size'] + num_oov_buckets,
num_oov_indices=num_oov_buckets,
mask_value=None,
vocabulary=['vocabulary_file'])(input_layer)
elif 'vocabulary_list' in params.keys():
return IntegerLookup(max_values=len(params['vocabulary_list']) + num_oov_buckets,
num_oov_indices=num_oov_buckets,
mask_value=None,
vocabulary=params['vocabulary_list'])(input_layer)
def __init__(self, log_dir):
self.log_dir = log_dir
self.START_TOKEN = '[SOS]'
self.END_TOKEN = '[EOS]'
self.vocab = list(sorted(set(string.printable))) + [self.START_TOKEN, self.END_TOKEN]
self.chars_to_ids = StringLookup(vocabulary=self.vocab)
self.vocab_size = self.chars_to_ids.vocab_size()
def encode_inputs(inputs):
encoded_features = []
for feature_name in inputs:
if feature_name in CATEGORICAL_FEATURE_NAMES:
vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
# Create a lookup to convert a string values to an integer indices.
# Since we are not using a mask token, nor expecting any out of vocabulary
# (oov) token, we set mask_token to None and num_oov_indices to 0.
lookup = StringLookup(vocabulary=vocabulary,
mask_token=None,
num_oov_indices=0)
# Convert the string input values into integer indices.
value_index = lookup(inputs[feature_name])
embedding_dims = int(math.sqrt(lookup.vocabulary_size()))
# Create an embedding layer with the specified dimensions.
embedding = layers.Embedding(input_dim=lookup.vocabulary_size(),
output_dim=embedding_dims)
# Convert the index values to embedding representations.
encoded_feature = embedding(value_index)
else:
# Use the numerical features as-is.
encoded_feature = inputs[feature_name]
if inputs[feature_name].shape[-1] is None:
encoded_feature = tf.expand_dims(encoded_feature, -1)
encoded_features.append(encoded_feature)
encoded_features = layers.concatenate(encoded_features)
return encoded_features
def load_data(self):
data = GFile(self.file_path, 'rb').read().decode(encoding='UTF-8')
# Get a list of the unique characters in the text
vocab = list(sorted(set(data)))
vocab_size = len(vocab)
chars_to_ids = StringLookup(vocabulary=vocab)
self.ids_to_chars_layer = StringLookup(
vocabulary=chars_to_ids.get_vocabulary(), invert=True)
# Split the entire text by character
chars = unicode_split(data, 'UTF-8')
ids_of_chars = chars_to_ids(chars)
# Group characters to form sequences (+1 since the targets are shifted by one)
sequences_ds = Dataset.from_tensor_slices(ids_of_chars)
sequences_ds = sequences_ds.batch(C.SEQUENCE_LENGTH + 1)
# Batch the sequences
ds = sequences_ds.padded_batch(C.BATCH_SIZE)
ds = ds.map(self._to_inputs_and_targets,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.shuffle(C.BUFFER_SIZE)
ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
return ds
def load_data():
data = pd.read_csv(
"https://storage.googleapis.com/tf-datasets/titanic/train.csv")
from sklearn.model_selection import train_test_split
labels = data.pop('survived')
label_names = ["Not survived", "Survived"]
features = {}
# Converting CSV file into Tensorflow object
for name, column in data.items():
dtype = column.dtype
if dtype == object:
dtype = string
else:
dtype = float32
features[name] = Input(shape=(1, ), name=name, dtype=dtype)
# Extracting and normalizing numeric features
numeric_features = {
name: feature
for name, feature in features.items() if feature.dtype == float32
}
x = Concatenate()(list(numeric_features.values()))
norm = Normalization()
norm.adapt(np.array(data[numeric_features.keys()]))
numeric_features = norm(x)
processed_features = [numeric_features]
# Extracting and normalizing non-numeric features
for name, feature in features.items():
if feature.dtype == float32:
continue
word = StringLookup(vocabulary=np.unique(data[name]))
one_hot = CategoryEncoding(max_tokens=word.vocab_size())
x = word(feature)
x = one_hot(x)
processed_features.append(x)
processed_features = Concatenate()(processed_features)
processed_features = Model(features, processed_features)
utils.plot_model(model=processed_features,
rankdir='LR',
dpi=72,
show_shapes=True)
feature_dict = {name: np.array(value) for name, value in data.items()}
train_features, test_features, train_labels, test_labels = train_test_split(
processed_features(feature_dict).numpy(), labels, test_size=0.2)
return train_features, train_labels, test_features, test_labels
def build(self, input_shape):
if self.options & WordShape.SHAPE_CHAR_CAT_FIRST or self.options & WordShape.SHAPE_CHAR_CAT_LAST:
category_vocab = [
'Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Mn', 'Me', 'Mc', 'Nd', 'Nl',
'No', 'Zs', 'Zl', 'Zp', 'Cc', 'Cf', 'Co', 'Cs', 'Pd', 'Ps',
'Pe', 'Pc', 'Po', 'Sm', 'Sc', 'Sk', 'So', 'Pi', 'Pf'
]
self.cat_lookup = StringLookup(num_oov_indices=0,
oov_token='Cn',
vocabulary=category_vocab)
if self.cat_lookup.vocab_size() != 30:
raise ValueError('Wrong vocabulary size')
super(WordShape, self).build(input_shape)
def encode_string_categorical_feature(feature, name, dataset):
index = StringLookup()
feature_ds = dataset.map(lambda x, y: x[name])
feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))
index.adapt(feature_ds)
encoded_feature = index(feature)
encoder = CategoryEncoding(output_mode="binary")
feature_ds = feature_ds.map(index)
encoder.adapt(feature_ds)
encoded_feature = encoder(encoded_feature)
return encoded_feature
def encode_inputs(inputs, encoding_size):
encoded_features = []
for feature_name in inputs:
if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
# Create a lookup to convert a string values to an integer indices.
# Since we are not using a mask token nor expecting any out of vocabulary
# (oov) token, we set mask_token to None and num_oov_indices to 0.
index = StringLookup(vocabulary=vocabulary,
mask_token=None,
num_oov_indices=0)
# Convert the string input values into integer indices.
value_index = index(inputs[feature_name])
# Create an embedding layer with the specified dimensions
embedding_ecoder = layers.Embedding(input_dim=len(vocabulary),
output_dim=encoding_size)
# Convert the index values to embedding representations.
encoded_feature = embedding_ecoder(value_index)
else:
# Project the numeric feature to encoding_size using linear transformation.
encoded_feature = tf.expand_dims(inputs[feature_name], -1)
encoded_feature = layers.Dense(
units=encoding_size)(encoded_feature)
encoded_features.append(encoded_feature)
return encoded_features
def encode_inputs(inputs, use_embedding=False):
encoded_features = []
for feature_name in inputs:
if feature_name in CATEGORICAL_FEATURE_NAMES:
vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
# Create a lookup to convert string values to an integer indices.
# Since we are not using a mask token nor expecting any out of vocabulary
# (oov) token, we set mask_token to None and num_oov_indices to 0.
index = StringLookup(vocabulary=vocabulary,
mask_token=None,
num_oov_indices=0)
# Convert the string input values into integer indices.
value_index = index(inputs[feature_name])
if use_embedding:
embedding_dims = int(math.sqrt(len(vocabulary)))
# Create an embedding layer with the specified dimensions.
embedding_ecoder = layers.Embedding(input_dim=len(vocabulary),
output_dim=embedding_dims)
# Convert the index values to embedding representations.
encoded_feature = embedding_ecoder(value_index)
else:
# Create a one-hot encoder.
onehot_encoder = CategoryEncoding(output_mode="binary")
onehot_encoder.adapt(index(vocabulary))
# Convert the index values to a one-hot representation.
encoded_feature = onehot_encoder(value_index)
else:
# Use the numerical features as-is.
encoded_feature = tf.expand_dims(inputs[feature_name], -1)
encoded_features.append(encoded_feature)
all_features = layers.concatenate(encoded_features)
return all_features
class DataManager:
def __init__(self, log_dir):
self.log_dir = log_dir
self.START_TOKEN = '[SOS]'
self.END_TOKEN = '[EOS]'
self.vocab = list(sorted(set(string.printable))) + [self.START_TOKEN, self.END_TOKEN]
self.chars_to_ids = StringLookup(vocabulary=self.vocab)
self.vocab_size = self.chars_to_ids.vocab_size()
def load_dataset(self):
ds = TextLineDataset(str(pathlib.Path(self.log_dir, 'file_names.txt')))
ds = ds.take(5)
ds = ds.map(self.parse_svg_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.padded_batch(2, drop_remainder=True)
return ds
def parse_svg_img(self, file_name):
svg_path = tf.strings.join([self.log_dir, '/svgs/', file_name, '.svg'])
img_path = tf.strings.join([self.log_dir, '/imgs/', file_name, '.png'])
svg = tf.io.read_file(svg_path)
svg = tf.concat([[self.START_TOKEN], unicode_split(svg, 'UTF-8'), [self.END_TOKEN]], axis=0)
svg = self.chars_to_ids(svg)
img = tf.io.read_file(img_path)
img = tf.io.decode_png(img, channels=3)
img = tf.cast(img, tf.float32)
img = img / 255.0
return (svg, img), svg
def __init__(self, vocabulary, embedding_dim, num_buckets, name=None):
super(QREmbedding, self).__init__(name=name)
self.num_buckets = num_buckets
self.index_lookup = StringLookup(
vocabulary=vocabulary, mask_token=None, num_oov_indices=0
)
self.q_embeddings = layers.Embedding(num_buckets, embedding_dim,)
self.r_embeddings = layers.Embedding(num_buckets, embedding_dim,)
def _encode_categorical_feature(
feature: KerasTensor,
name: str,
dataset: Optional[BatchDataset],
) -> KerasTensor:
"""One-hot encode categorical features.
Args:
- feature: The input layer of the feature.
- name: The feature's name (its column name in the original dataframe).
- dataset: The training data, if not specified, return a no-op layer.
Returns:
The one-hot encoded tensor of the input feature.
"""
# Return generic layer for the tuner initialization
if not dataset:
return KerasTensor(type_spec=TensorSpec(
shape=(None, 1), dtype=tf.float32, name=None))
# Create a StringLookup layer which will turn strings into integer indices
index = StringLookup()
# Prepare a Dataset that only yields our feature
feature_ds = dataset.map(lambda x, y: x[name])
feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))
# Learn the set of possible string values and assign them a fixed integer index
index.adapt(feature_ds)
# Turn the string input into integer indices
encoded_feature = index(feature)
# Create a CategoryEncoding for our integer indices
encoder = CategoryEncoding(output_mode="binary")
# Learn the space of possible indices
encoder.adapt(np.arange(index.vocab_size()))
# Apply one-hot encoding to our indices{split + 1} / {n_splits}
encoded_feature = encoder(encoded_feature)
return encoded_feature
def embedding_encoder(vocabulary, embedding_dim, num_oov_indices=0, name=None):
return keras.Sequential(
[
StringLookup(
vocabulary=vocabulary, mask_token=None, num_oov_indices=num_oov_indices
),
layers.Embedding(
input_dim=len(vocabulary) + num_oov_indices, output_dim=embedding_dim
),
],
name=f"{name}_embedding" if name else None,
)
def get_svg_ds(self):
data = GFile('datasets/svgs/simpleline.svg',
'rb').read().decode(encoding='UTF-8')
# Get the list of the unique characters in the text
vocab = ['e', 'g', 'n', 'r', '\n']
vocab_size = len(vocab)
# Build the id to char lookup table
chars_to_ids = StringLookup(vocabulary=vocab)
self.ids_to_chars_layer = StringLookup(
vocabulary=chars_to_ids.get_vocabulary(), invert=True)
# Split the entire text by character
chars = unicode_split(data, 'UTF-8')
ids_of_chars = chars_to_ids(chars)
# Group characters to form sequences
svg_ds = Dataset.from_tensor_slices(ids_of_chars)
svg_ds = svg_ds.batch(C.SEQUENCE_LENGTH)
svg_ds = svg_ds.batch(C.BATCH_SIZE)
return svg_ds
def __init__(self, emb_name, vocab):
super(CustomEmbed, self).__init__()
self.vocab = vocab
self.vocab_size = len(vocab)
self.output_dim = int(math.sqrt(self.vocab_size))
self.custom_embed = layers.Embedding(input_dim=self.vocab_size,
output_dim=self.output_dim,
name=f"{emb_name}_embedding")
self.stringLookUp = StringLookup(vocabulary=self.vocab,
mask_token=None,
num_oov_indices=0)
print(emb_name, self.output_dim)
def character_decoder(encoder):
"""Character decoder
Parameters:
encoder: keras.preprocessing.StringLookup, character encoder.
Returns:
Character decoder(keras.preprocessing.StringLookup).
"""
num_to_char = StringLookup(mask_token=None,
num_oov_indices=1,
vocabulary=encoder.get_vocabulary(),
invert=True)
return num_to_char
def character_encoder(vocab):
"""Character encoder
Parameters:
vocab: list, characters to be encoded.
Returns:
Character encoder(keras.preprocessing.StringLookup).
"""
char_to_num = StringLookup(mask_token=None,
num_oov_indices=0,
vocabulary=list(vocab),
invert=False)
return char_to_num
"""
### Building the character vocabulary
Keras provides different preprocessing layers to deal with different modalities of data.
[This guide](https://keras.io/guides/preprocessing_layers/) 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, g, o, t} (without any special tokens). We use the
[`StringLookup`](https://keras.io/api/layers/preprocessing_layers/categorical/string_lookup/)
layer for this purpose.
"""
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
Instead of square images, many OCR models work with rectangular images. This will become
clearer in a moment when we will visualize a few samples from the dataset. While
aspect-unaware resizing square images does not introduce a significant amount of
distortion this is not the case for rectangular images. But resizing images to a uniform
size is a requirement for mini-batching. So we need to perform our resizing such that
the following criteria are met:
class WordEmbedding(layers.Layer):
UNK_MARK = '[UNK]'
REP_CHAR = '\uFFFD'
def __init__(self, vocabulary, output_dim, normalize_unicode='NFKC', lower_case=False, zero_digits=False,
max_len=None, reserved_words=None, embed_type='dense_auto', adapt_cutoff=None, adapt_factor=4,
embeddings_initializer='uniform', **kwargs):
super().__init__(**kwargs)
self.input_spec = layers.InputSpec(min_ndim=1, max_ndim=2, dtype='string')
if not isinstance(vocabulary, list) or not all(map(lambda x: isinstance(x, str), vocabulary)):
raise ValueError('Expected "vocabulary" to be a list of strings')
if len(vocabulary) != len(set(vocabulary)):
raise ValueError('Expected "vocabulary" to contain unique values')
self.vocabulary = vocabulary
self.output_dim = output_dim
self.normalize_unicode = normalize_unicode
self.lower_case = lower_case
self.zero_digits = zero_digits
if max_len is not None and max_len < 3:
raise ValueError('Expected "max_len" to be None or greater then 2')
self.max_len = max_len
if reserved_words and len(reserved_words) != len(set(reserved_words)):
raise ValueError('Expected "reserved_words" to contain unique values')
self.reserved_words = reserved_words
if embed_type not in {'dense_auto', 'dense_cpu', 'adapt'}:
raise ValueError('Expected "embed_type" to be one of "dense_auto", "dense_cpu" or "adapt"')
self.embed_type = embed_type
self.adapt_cutoff = adapt_cutoff
self.adapt_factor = adapt_factor
self.embeddings_initializer = initializers.get(embeddings_initializer)
all_reserved_words = [] if reserved_words is None else [r for r in reserved_words if self.UNK_MARK != r]
self._reserved_words = [self.UNK_MARK] + all_reserved_words
miss_reserved_words = [m for m in self._reserved_words if m not in vocabulary]
if miss_reserved_words:
tf.get_logger().warning('Vocabulary missed some reserved_words values: {}. '
'This may indicate an error in vocabulary estimation'.format(miss_reserved_words))
clean_vocab = [w for w in vocabulary if w not in self._reserved_words]
self._vocabulary = self._reserved_words + clean_vocab
def vocab(self, word_counts, **kwargs):
if not word_counts:
raise ValueError('Can\'t estimate vocabulary with empty word counter')
if not all(map(lambda k: isinstance(k, str), word_counts.keys())):
raise ValueError('Expected all words to be strings')
word_counts = Vocabulary(word_counts)
word_tokens = word_counts.tokens()
adapt_words = self.adapt(word_tokens)
if 1 == adapt_words.shape.rank:
adapt_words = adapt_words[..., None]
adapt_counts = Vocabulary()
for adapts, word in zip(adapt_words, word_tokens):
adapts = np.char.decode(adapts.numpy().reshape([-1]).astype('S'), 'utf-8')
for adapt in adapts:
adapt_counts[adapt] += word_counts[word]
return adapt_counts
@tf_utils.shape_type_conversion
def build(self, input_shape=None):
self.squeeze = False
if 2 == len(input_shape):
if 1 != input_shape[-1]:
raise ValueError(
'Input 0 of layer {} is incompatible with the layer: if ndim=2 expected axis[-1]=1, found '
'axis[-1]={}. Full shape received: {}'.format(self.name, input_shape[-1], input_shape))
self.squeeze = True
input_shape = input_shape[:1]
self.lookup = StringLookup(vocabulary=self._vocabulary, mask_token=None, oov_token=self.UNK_MARK)
self.lookup.build(input_shape)
if 'adapt' == self.embed_type:
self.embed = AdaptiveEmbedding(
self.adapt_cutoff, self.lookup.vocabulary_size(), self.output_dim, factor=self.adapt_factor,
embeddings_initializer=self.embeddings_initializer)
else:
self.embed = layers.Embedding(
self.lookup.vocabulary_size(), self.output_dim, embeddings_initializer=self.embeddings_initializer)
if 'dense_auto' == self.embed_type:
self.embed.build(input_shape)
else: # 'dense_cpu' == self.embed_type
with tf.device('cpu:0'):
self.embed.build(input_shape)
super().build(input_shape)
def adapt(self, inputs):
inputs = tf.convert_to_tensor(inputs, dtype='string')
if self.normalize_unicode:
inputs = miss_text.normalize_unicode(inputs, form=self.normalize_unicode, skip=self._reserved_words)
if self.lower_case:
inputs = miss_text.lower_case(inputs, skip=self._reserved_words)
if self.zero_digits:
inputs = miss_text.zero_digits(inputs, skip=self._reserved_words)
if self.max_len is not None:
inputs_ = tf.stack([
miss_text.sub_string(inputs, 0, self.max_len // 2, skip=self._reserved_words),
tf.fill(tf.shape(inputs), self.REP_CHAR),
miss_text.sub_string(inputs, -self.max_len // 2 + 1, -1, skip=self._reserved_words)],
axis=-1)
inputs_ = tf.strings.reduce_join(inputs_, axis=-1)
sizes = tf.strings.length(inputs, unit='UTF8_CHAR')
inputs = tf.where(sizes > self.max_len, inputs_, inputs)
return inputs
def call(self, inputs, **kwargs):
if self.squeeze:
# Workaround for Sequential model test
inputs = tf.squeeze(inputs, axis=-1)
adapts = self.adapt(inputs)
indices = self.lookup(adapts)
outputs = self.embed(indices)
return outputs
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
return input_shape + (self.output_dim,)
def get_config(self):
config = super().get_config()
config.update({
'vocabulary': self.vocabulary,
'output_dim': self.output_dim,
'normalize_unicode': self.normalize_unicode,
'lower_case': self.lower_case,
'zero_digits': self.zero_digits,
'max_len': self.max_len,
'reserved_words': self.reserved_words,
'embed_type': self.embed_type,
'adapt_cutoff': self.adapt_cutoff,
'adapt_factor': self.adapt_factor,
'embeddings_initializer': initializers.serialize(self.embeddings_initializer)
})
return config
if __name__ == "__main__":
if len(sys.argv) < 4:
print("usage: python predict.py [input_path] [model_path] [output_path]")
sys.exit()
input_path = sys.argv[1]
model_path = sys.argv[2]
output_path = sys.argv[2]
data_loader = DataLoader(input_path, training_ratio=0.7)
raw_train_ds, raw_val_ds = data_loader.load()
# Why N? for one encoding purpose, last character = [0, 0, 0, 0]
VOCAB = ["A", "G", "T", "N"]
string_lookup = StringLookup(vocabulary=VOCAB)
AUTOTUNE = tf.data.experimental.AUTOTUNE
BATCH_SIZE = 256
SHUFFLE_SIZE = 1000
encoded_train_ds = raw_train_ds.cache().shuffle(SHUFFLE_SIZE)
encoded_train_ds = encoded_train_ds.prefetch(buffer_size=AUTOTUNE)
encoded_train_ds = encoded_train_ds.map(preprocess)
encoded_val_ds = raw_val_ds.cache().map(preprocess)
train_ds = encoded_train_ds.cache().batch(BATCH_SIZE)
train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)
val_ds = encoded_val_ds.cache().batch(BATCH_SIZE)
model = TwoTowerModel(RNA_length=33, gRNA_length=23)
TARGET_FEATURE_NAME = "income_bracket"
# A list of the labels of the target features.
TARGET_LABELS = [" <=50K", " >50K"]
"""
## Create `tf.data.Dataset` objects for training and validation
We create an input function to read and parse the file, and convert features and labels
into a [`tf.data.Dataset`](https://www.tensorflow.org/guide/datasets)
for training and validation. We also preprocess the input by mapping the target label
to an index.
"""
from tensorflow.keras.layers.experimental.preprocessing import StringLookup
target_label_lookup = StringLookup(vocabulary=TARGET_LABELS,
mask_token=None,
num_oov_indices=0)
def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128):
dataset = tf.data.experimental.make_csv_dataset(
csv_file_path,
batch_size=batch_size,
column_names=CSV_HEADER,
column_defaults=COLUMN_DEFAULTS,
label_name=TARGET_FEATURE_NAME,
num_epochs=1,
header=False,
na_value="?",
shuffle=shuffle,
).map(lambda features, target: (features, target_label_lookup(target)))
class WordShape(tf.keras.layers.Layer):
SHAPE_HAS_CASE = 1
SHAPE_LOWER_CASE = 2
SHAPE_UPPER_CASE = 4
SHAPE_TITLE_CASE = 8
SHAPE_MIXED_CASE = 16
SHAPE_ALL_CASES = SHAPE_HAS_CASE | SHAPE_LOWER_CASE | SHAPE_UPPER_CASE | SHAPE_TITLE_CASE | SHAPE_MIXED_CASE
# Mean and std length from Universal Dependencies and large russian POS corporas
# Tokens (split_words): 3.057 and 3.118
# Words: 4.756 and 3.453
SHAPE_LENGTH_NORM = 32
SHAPE_LEFT_SAME = 64
SHAPE_RIGHT_SAME = 128
SHAPE_LEFT2_SAME = 256
SHAPE_RIGHT2_SAME = 512
SHAPE_ALL_SAME = SHAPE_LEFT_SAME | SHAPE_RIGHT_SAME | SHAPE_LEFT2_SAME | SHAPE_RIGHT2_SAME
SHAPE_CHAR_CAT_FIRST = 1024
SHAPE_CHAR_CAT_LAST = 2048
SHAPE_CHAR_CAT_BOTH = SHAPE_CHAR_CAT_FIRST | SHAPE_CHAR_CAT_LAST
SHAPE_ALL = SHAPE_ALL_CASES | SHAPE_LENGTH_NORM | SHAPE_ALL_SAME | SHAPE_CHAR_CAT_BOTH
def __init__(self,
options,
mean_len=3.906,
std_len=3.285,
char_embed=5,
*args,
**kwargs):
super(WordShape, self).__init__(*args, **kwargs)
self.input_spec = tf.keras.layers.InputSpec(dtype='string')
self._supports_ragged_inputs = True
if 0 == options:
raise ValueError('At least one shape option should be selected')
self.options = options
self.mean_len = mean_len
self.std_len = std_len
@tf_utils.shape_type_conversion
def build(self, input_shape):
if self.options & WordShape.SHAPE_CHAR_CAT_FIRST or self.options & WordShape.SHAPE_CHAR_CAT_LAST:
category_vocab = [
'Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Mn', 'Me', 'Mc', 'Nd', 'Nl',
'No', 'Zs', 'Zl', 'Zp', 'Cc', 'Cf', 'Co', 'Cs', 'Pd', 'Ps',
'Pe', 'Pc', 'Po', 'Sm', 'Sc', 'Sk', 'So', 'Pi', 'Pf'
]
self.cat_lookup = StringLookup(num_oov_indices=0,
oov_token='Cn',
vocabulary=category_vocab)
if self.cat_lookup.vocab_size() != 30:
raise ValueError('Wrong vocabulary size')
super(WordShape, self).build(input_shape)
def call(self, inputs, **kwargs):
outputs_one, outputs_many = [], []
# Case
any_case = self.SHAPE_HAS_CASE | self.SHAPE_LOWER_CASE | self.SHAPE_UPPER_CASE | self.SHAPE_TITLE_CASE | \
self.SHAPE_MIXED_CASE
if self.options & any_case:
inputs_lower = lower_case(inputs)
inputs_upper = upper_case(inputs)
has_case = tf.not_equal(inputs_lower, inputs_upper)
if self.options & self.SHAPE_HAS_CASE:
outputs_one.append(has_case)
if self.options & self.SHAPE_LOWER_CASE or self.options & self.SHAPE_MIXED_CASE:
is_lower = tf.logical_and(has_case, tf.equal(inputs, inputs_lower))
if self.options & self.SHAPE_LOWER_CASE:
outputs_one.append(is_lower)
if self.options & self.SHAPE_UPPER_CASE or self.options & self.SHAPE_MIXED_CASE:
is_upper = tf.logical_and(has_case, tf.equal(inputs, inputs_upper))
if self.options & self.SHAPE_UPPER_CASE:
outputs_one.append(is_upper)
if self.options & self.SHAPE_TITLE_CASE or self.options & self.SHAPE_MIXED_CASE:
inputs_title = title_case(inputs)
is_title = tf.logical_and(has_case, tf.equal(inputs, inputs_title))
if self.options & self.SHAPE_TITLE_CASE:
outputs_one.append(is_title)
if self.options & self.SHAPE_MIXED_CASE:
no_case = tf.logical_not(has_case)
is_mixed = tf.logical_not(
tf.logical_or(tf.logical_or(no_case, is_lower),
tf.logical_or(is_upper, is_title)))
outputs_one.append(is_mixed)
# Length
if self.options & self.SHAPE_LENGTH_NORM:
length_norm = tf.strings.length(inputs, unit='UTF8_CHAR')
length_norm = (tf.cast(length_norm, self.compute_dtype) -
self.mean_len) / self.std_len
outputs_one.append(length_norm)
# Same
any_same = self.SHAPE_LEFT_SAME | self.SHAPE_RIGHT_SAME | self.SHAPE_LEFT2_SAME | self.SHAPE_RIGHT2_SAME
if self.options & any_same:
empty_pad = tf.zeros_like(inputs[..., :1])
inputs_padded = tf.concat(
[empty_pad, empty_pad, inputs, empty_pad, empty_pad], axis=-1)
if self.options & (self.SHAPE_LEFT_SAME | self.SHAPE_RIGHT_SAME):
same_one = tf.equal(inputs_padded[..., 1:],
inputs_padded[..., :-1])
if self.options & self.SHAPE_LEFT_SAME:
same_left = same_one[..., 1:-2]
outputs_one.append(same_left)
if self.options & self.SHAPE_RIGHT_SAME:
same_right = same_one[..., 2:-1]
outputs_one.append(same_right)
if self.options & (self.SHAPE_LEFT2_SAME | self.SHAPE_RIGHT2_SAME):
same_two = tf.equal(inputs_padded[..., 2:],
inputs_padded[..., :-2])
if self.options & self.SHAPE_LEFT2_SAME:
same_left2 = same_two[..., :-2]
outputs_one.append(same_left2)
if self.options & self.SHAPE_RIGHT2_SAME:
same_right2 = same_two[..., 2:]
outputs_one.append(same_right2)
# Char category
if self.options & WordShape.SHAPE_CHAR_CAT_FIRST:
first_cats = char_category(inputs)
first_ids = self.cat_lookup(first_cats)
first_feats = tf.one_hot(first_ids, depth=30)
outputs_many.append(first_feats)
if self.options & WordShape.SHAPE_CHAR_CAT_LAST:
last_cats = char_category(inputs, first=False)
last_ids = self.cat_lookup(last_cats)
last_feats = tf.one_hot(last_ids, depth=30)
outputs_many.append(last_feats)
outputs_one = [tf.cast(o, self.compute_dtype) for o in outputs_one]
outputs_many = [tf.cast(o, self.compute_dtype) for o in outputs_many]
if not outputs_one:
return tf.concat(outputs_many, axis=-1)
outputs_one = tf.stack(outputs_one, axis=-1)
if not outputs_many:
return outputs_one
return tf.concat([outputs_one, *outputs_many], axis=-1)
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
units = 0
options = [
self.SHAPE_HAS_CASE, self.SHAPE_LOWER_CASE, self.SHAPE_UPPER_CASE,
self.SHAPE_TITLE_CASE, self.SHAPE_MIXED_CASE,
self.SHAPE_LENGTH_NORM, self.SHAPE_LEFT_SAME,
self.SHAPE_RIGHT_SAME, self.SHAPE_LEFT2_SAME,
self.SHAPE_RIGHT2_SAME
]
for opt in options:
if self.options & opt:
units += 1
if self.options & WordShape.SHAPE_CHAR_CAT_FIRST:
units += 30
if self.options & WordShape.SHAPE_CHAR_CAT_LAST:
units += 30
return input_shape + (units, )
def get_config(self):
config = super().get_config()
config.update({
'options': self.options,
'mean_len': self.mean_len,
'std_len': self.std_len
})
return config
def encode_input_features(inputs,
sequence_length,
USER_FEATURES,
CATEGORICAL_FEATURES_WITH_VOCABULARY,
movies,
genres,
include_user_id=True,
include_user_features=True,
include_movie_features=True):
encoded_transformer_features = []
encoded_other_features = []
other_feature_names = []
if include_user_id:
other_feature_names.append("user_id")
if include_movie_features:
other_feature_names.extend(USER_FEATURES)
# Encode user features.
for feature_name in other_feature_names:
# Conver the string input values into integer indices.
vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
idx = StringLookup(vocabulary=vocabulary,
mask_token=None,
num_oov_indices=0)(inputs[feature_name])
# Compute embedding dimensions.
embedding_dims = int(math.sqrt(len(vocabulary)))
# Create an embedding layer with the specified dimensions.
embedding_encoder = layers.Embedding(
input_dim=len(vocabulary),
output_dim=embedding_dims,
name=f"{feature_name}_embedding",
)
# Convert the index values to embedding representations.
encoded_other_features.append(embedding_encoder(idx))
# Create a single embedding vector for the user features.
if len(encoded_other_features) > 1:
encoded_other_features = layers.concatenate(encoded_other_features)
elif len(encoded_other_features) == 1:
encoded_other_features = encoded_other_features[0]
else:
encoded_other_features = None
# Create a movie embedding encoder.
movie_vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY["movie_id"]
movie_embedding_dims = int(math.sqrt(len(movie_vocabulary)))
# Create a lookup to convert string values to integer indices.
movie_index_lookup = StringLookup(
input_dim=len(movie_vocabulary),
mask_token=None,
num_oov_indices=0,
name="movie_index_lookup",
)
# Create an embedding layer with the specified dimensions.
movie_embedding_encoder = layers.Embedding(
input_dim=len(movie_vocabulary),
output_dim=movie_embedding_dims,
name=f"movie_embedding",
)
# Create a vector lookup for movie genres.
genre_vectors = movies[genres].to_numpy()
movie_genres_lookup = layers.Embedding(
input_dim=genre_vectors.shape[0],
output_dim=genre_vectors.shape[1],
embeddings_initializer=tf.keras.initializers.Constant(genre_vectors),
trainable=False,
name="genres_vector")
# Create a processing layer for genres.
movie_embedding_processor = layers.Dense(
units=movie_embedding_dims,
activation="relu",
name="process_movie_embedding_with_genres",
)
# Define a function to encode a given movie id.
def encode_movie(movie_id):
# Convert the string input values into integer indices.
movie_idx = movie_index_lookup(movie_id)
movie_embedding = movie_embedding_encoder(movie_idx)
encoded_movie = movie_embedding
if include_movie_features:
movie_genres_vector = movie_genres_lookup(movie_idx)
encoded_movie = movie_embedding_processor(
layers.concatenate([movie_embedding, movie_genres_vector]))
return encoded_movie
# Encoded target_movie_id.
target_movie_id = inputs["target_movie_id"]
encoded_target_movie = encode_movie(target_movie_id)
# Encoding sequence movie_ids.
sequence_movie_ids = inputs["sequence_movie_ids"]
encoded_sequence_movies = encode_movie(sequence_movie_ids)
# Create positional embedding.
positional_embedding_encoder = layers.Embedding(
input_dim=sequence_length,
output_dim=movie_embedding_dims,
name="positional_embedding",
)
positions = tf.range(start=0, limit=sequence_length - 1, delta=1)
encoded_positions = positional_embedding_encoder(positions)
# Retrieve sequence ratings to incorporate them into the encoding
# of the movie.
sequence_ratings = tf.expand_dims(inputs["sequence_ratings"], -1)
# Add the positional encoding to the movie encodings and multiply
# them by rating.
encoded_sequence_movies_with_position_and_rating = layers.Multiply()([
(encoded_sequence_movies + encoded_positions), sequence_ratings
])
# Construct the transformer inputs.
for encoded_movie in tf.unstack(
encoded_sequence_movies_with_position_and_rating, axis=1):
encoded_transformer_features.append(tf.expand_dims(encoded_movie, 1))
encoded_transformer_features.append(encoded_target_movie)
encoded_transformer_features = layers.concatenate(
encoded_transformer_features, axis=1)
return encoded_transformer_features, encoded_other_features
class OCR:
def __init__(self, model_weight='mn_model_weight.h5', scale_ratio=1):
self.scale_ratio = scale_ratio
self.characters = sorted([
*set("".join(
sum(ArtsInfo.ArtNames, []) + ArtsInfo.TypeNames +
list(ArtsInfo.MainAttrNames.values()) +
list(ArtsInfo.SubAttrNames.values()) + list(".,+%0123456789")))
])
# Mapping characters to integers
self.char_to_num = StringLookup(vocabulary=list(self.characters),
num_oov_indices=0,
mask_token="")
# Mapping integers back to original characters
self.num_to_char = StringLookup(
vocabulary=self.char_to_num.get_vocabulary(),
oov_token="",
mask_token="",
invert=True)
self.width = 240
self.height = 16
self.max_length = 15
self.build_model(input_shape=(self.width, self.height))
self.model.load_weights(model_weight)
def detect_info(self, art_img):
info = self.extract_art_info(art_img)
x = np.concatenate([
self.preprocess(info[key]).T[None, :, :, None]
for key in sorted(info.keys())
],
axis=0)
y = self.model.predict(x)
y = self.decode(y)
return {
**{key: v
for key, v in zip(sorted(info.keys()), y)},
**{
'star': self.detect_star(art_img)
}
}
def extract_art_info(self, art_img):
name = art_img.crop([i * self.scale_ratio for i in Config.name_coords])
type = art_img.crop([i * self.scale_ratio for i in Config.type_coords])
main_attr_name = art_img.crop(
[i * self.scale_ratio for i in Config.main_attr_name_coords])
main_attr_value = art_img.crop(
[i * self.scale_ratio for i in Config.main_attr_value_coords])
level = art_img.crop(
[i * self.scale_ratio for i in Config.level_coords])
subattr_1 = art_img.crop([
i * self.scale_ratio for i in Config.subattr_1_coords
]) # [73, 83, 102]
subattr_2 = art_img.crop(
[i * self.scale_ratio for i in Config.subattr_2_coords])
subattr_3 = art_img.crop(
[i * self.scale_ratio for i in Config.subattr_3_coords])
subattr_4 = art_img.crop(
[i * self.scale_ratio for i in Config.subattr_4_coords])
if np.all(
np.abs(np.array(subattr_1, np.float) -
[[[73, 83, 102]]]).max(axis=-1) > 25):
del subattr_1
del subattr_2
del subattr_3
del subattr_4
elif np.all(
np.abs(np.array(subattr_2, np.float) -
[[[73, 83, 102]]]).max(axis=-1) > 25):
del subattr_2
del subattr_3
del subattr_4
elif np.all(
np.abs(np.array(subattr_3, np.float) -
[[[73, 83, 102]]]).max(axis=-1) > 25):
del subattr_3
del subattr_4
elif np.all(
np.abs(np.array(subattr_4, np.float) -
[[[73, 83, 102]]]).max(axis=-1) > 25):
del subattr_4
return {
key: value
for key, value in locals().items()
if key not in ['art_img', 'self']
}
def detect_star(self, art_img):
star = art_img.crop([i * self.scale_ratio for i in Config.star_coords])
cropped_star = self.crop(self.normalize(self.to_gray(star)))
coef = cropped_star.shape[1] / cropped_star.shape[0]
coef = coef / 1.30882352 + 0.21568627
return int(round(coef))
def to_gray(self, text_img):
text_img = np.array(text_img)
if len(text_img.shape) > 2:
text_img = (
text_img[..., :3] @ [[[0.299], [0.587], [0.114]]])[:, :, 0]
return np.array(text_img, np.float32)
def normalize(self, img, auto_inverse=True):
img -= img.min()
img /= img.max()
if auto_inverse and img[-1, -1] > 0.5:
img = 1 - img
return img
def crop(self, img, tol=0.7):
# img is 2D image data
# tol is tolerance
mask = img > tol
m, n = img.shape
mask0, mask1 = mask.any(0), mask.any(1)
col_start, col_end = mask0.argmax(), n - mask0[::-1].argmax()
row_start, row_end = mask1.argmax(), m - mask1[::-1].argmax()
# print(row_end-row_start, col_end-col_start)
return img[row_start:row_end, col_start:col_end]
def resize_to_height(self, img):
height = self.height
return (np.array(
Image.fromarray(np.uint8(img * 255)).resize(
(int(img.shape[1] * height / img.shape[0]), height),
Image.BILINEAR,
)) / 255)
def pad_to_width(self, img):
width = self.width
if img.shape[1] >= width:
return img[:, :width]
return np.pad(img, [[0, 0], [0, width - img.shape[1]]],
mode="constant",
constant_values=0)
def preprocess(self, text_img):
result = self.to_gray(text_img)
result = self.normalize(result, True)
result = self.crop(result)
result = self.normalize(result, False)
result = self.resize_to_height(result)
result = self.pad_to_width(result)
return result
def decode(self, pred):
input_len = np.ones(pred.shape[0]) * pred.shape[1]
# Use greedy search. For complex tasks, you can use beam search
results = ctc_decode(pred, input_length=input_len,
greedy=True)[0][0][:, :self.max_length]
# Iterate over the results and get back the text
output_text = []
for res in results:
res = self.num_to_char(res)
res = reduce_join(res)
res = res.numpy().decode("utf-8")
output_text.append(res)
return output_text
def build_model(self, input_shape):
input_img = Input(shape=(input_shape[0], input_shape[1], 1),
name="image",
dtype="float32")
mobilenet = MobileNetV3_Small((input_shape[0], input_shape[1], 1),
0,
alpha=1.0,
include_top=False).build()
x = mobilenet(input_img)
new_shape = ((input_shape[0] // 8), (input_shape[1] // 8) * 576)
x = Reshape(target_shape=new_shape, name="reshape")(x)
x = Dense(64, activation="relu", name="dense1")(x)
x = Dropout(0.2)(x)
# RNNs
x = Bidirectional(LSTM(128, return_sequences=True, dropout=0.25))(x)
x = Bidirectional(LSTM(64, return_sequences=True, dropout=0.25))(x)
# Output layer
output = Dense(len(self.characters) + 2,
activation="softmax",
name="dense2")(x)
# Define the model
self.model = Model(inputs=[input_img],
outputs=output,
name="ocr_model_v1")
def main():
# Prepare the data.
CSV_HEADER = [
"age",
"workclass",
"fnlwgt",
"education",
"education_num",
"marital_status",
"occupation",
"relationship",
"race",
"gender",
"capital_gain",
"capital_loss",
"hours_per_week",
"native_country",
"income_bracket",
]
train_data_url = (
"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
)
train_data = pd.read_csv(train_data_url, header=None, names=CSV_HEADER)
test_data_url = (
"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
)
test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER)
print(f"Train dataset shape: {train_data.shape}")
print(f"Test dataset shape: {test_data.shape}")
# Remove the first record (because it is not a valid example) and a
# trailing "dot" in the class labels.
test_data = test_data[1:]
test_data.income_bracket = test_data.income_bracket.apply(
lambda value: value.replace(".", ""))
# Store the training and test data splits locally as CSV files.
train_data_file = "train_data.csv"
test_data_file = "test_data.csv"
train_data.to_csv(train_data_file, index=False, header=False)
test_data.to_csv(test_data_file, index=False, header=False)
# Define dataset metadata.
# Here, define the metadata of the dataset that will be useful for
# reading and parsing and encoding input features.
# A list of numerical feature names.
NUMERICAL_FEATURE_NAMES = [
"age",
"education_num",
"capital_gain",
"capital_loss",
"hours_per_week",
]
# A dictionary of the categorical features and their vocabulary.
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
"workclass": sorted(list(train_data["workclass"].unique())),
"education": sorted(list(train_data["education"].unique())),
"marital_status": sorted(list(train_data["marital_status"].unique())),
"occupation": sorted(list(train_data["occupation"].unique())),
"relationship": sorted(list(train_data["relationship"].unique())),
"race": sorted(list(train_data["race"].unique())),
"gender": sorted(list(train_data["gender"].unique())),
"native_country": sorted(list(train_data["native_country"].unique())),
}
# A list of the columns to ignore from the dataset.
IGNORE_COLUMN_NAMES = ["fnlwgt"]
# A list of the categorical feature names.
CATEGORICAL_FEATURE_NAMES = list(
CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())
# A list of all the input features.
FEATURE_NAMES = NUMERICAL_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES
# A list of column default values for each feature.
COLUMN_DEFAULTS = [[0.0] if feature_name in NUMERICAL_FEATURE_NAMES +
IGNORE_COLUMN_NAMES else ["NA"]
for feature_name in CSV_HEADER]
# The name of the target feature.
TARGET_FEATURE_NAME = "income_bracket"
# A list of the labels of the target features.
TARGET_LABELS = [" <=50K", " >50K"]
# Create tf.data.Dataset objects for training and validation.
target_label_lookup = StringLookup(vocabulary=TARGET_LABELS,
mask_token=None,
num_oov_indices=0)
# Set up the code that will train and evaluate the model.
learning_rate = 0.01
batch_size = 265
num_epochs = 10
hidden_units = [64, 64]
# Experiment 1: Train a decision tree model.
# In this experiment, train a single neural decision tree model
# that uses all input features.
num_trees = 10
depth = 10
used_features_rate = 1.0
num_classes = len(TARGET_LABELS)
tree_model = create_tree_model(FEATURE_NAMES, NUMERICAL_FEATURE_NAMES,
CATEGORICAL_FEATURE_NAMES,
CATEGORICAL_FEATURES_WITH_VOCABULARY, depth,
used_features_rate, num_classes)
run_experiment(tree_model, learning_rate, train_data_file, test_data_file,
CSV_HEADER, COLUMN_DEFAULTS, TARGET_FEATURE_NAME,
target_label_lookup, batch_size, num_epochs)
# Experiment 2: Train a forest model.
# In this experiment, train a neural decision forest with num_trees
# where each tree uses randomly selected 50% of the input features.
# Can control the number of features to be used in each tree by
# setting the used_features_rate variable. In addition, set the
# depth to 5 instead of 10 compared to the previous experiment.
num_trees = 25
depth = 5
used_features_rate = 0.5
forest_model = create_forest_model(FEATURE_NAMES, NUMERICAL_FEATURE_NAMES,
CATEGORICAL_FEATURE_NAMES,
CATEGORICAL_FEATURES_WITH_VOCABULARY,
num_trees, depth, used_features_rate,
num_classes)
run_experiment(forest_model, learning_rate, train_data_file,
test_data_file, CSV_HEADER, COLUMN_DEFAULTS,
TARGET_FEATURE_NAME, target_label_lookup, batch_size,
num_epochs)
# Exit the program.
exit(0)
def __init__(self):
super().__init__(StringLookup())
