def train_ensemble(experiment_name, model_params, base_hyperparams):
data_folder = 'data'
dataset = 'lung_ct_dataset'
SEED = 0
result_save_path = experiment_name + '_' + 'submission.csv'
tf.random.set_seed(SEED)
gpu_setup()
mixed_precision_setup()
train_folds = tfds.load(
name=dataset,
split=[f'train[:{k}%]+train[{k+10}%:]' for k in range(0, 100, 20)],
download=False,
shuffle_files=False,
as_supervised=True,
data_dir=data_folder
)
valid_folds = tfds.load(
name=dataset,
split=[f'train[{k}%:{k+10}%]' for k in range(0, 100, 20)],
download=False,
shuffle_files=False,
as_supervised=True,
data_dir=data_folder
)
test_ds_raw, test_info_raw = tfds.load(
name=dataset,
split='test',
download=False,
shuffle_files=False,
as_supervised=False,
with_info=True,
data_dir=data_folder
)
img_ids = tfds.as_dataframe(test_ds_raw, test_info_raw)
train_ds = preprocess(
train_folds[0], model_params, batch_size=base_hyperparams['train_batch_size'], ds_type='train')
valid_ds = preprocess(
valid_folds[0], model_params, batch_size=base_hyperparams['valid_batch_size'], ds_type='valid')
test_ds = preprocess(test_ds_raw, model_params,
batch_size=base_hyperparams['test_batch_size'], ds_type='test')
train_ds = train_ds.concatenate(valid_ds) # train on entire dataset
train_ds = preprocess_ensemble(train_ds, model_params)
valid_ds = preprocess_ensemble(valid_ds, model_params)
test_ds = preprocess_ensemble(test_ds, model_params)
ensemble_model = ensemble_learn(
experiment_name, train_ds, valid_ds, model_params, base_hyperparams)
predicted_labels = evaluate(ensemble_model, test_ds)
save_results(img_ids, predicted_labels, result_save_path)
def __init__(self):
peng = tfds.load('penguins/simple', download=True, try_gcs=True)
dataset_df = tfds.as_dataframe(peng['train'])
# Filter out invalid rows
dataset_df = dataset_df.loc[dataset_df['sex'] != 2]
records = dataset_df.to_dict(orient='records')
for rec in records:
ex = {
'body_mass_g': rec['body_mass_g'],
'culmen_depth_mm': rec['culmen_depth_mm'],
'culmen_length_mm': rec['culmen_length_mm'],
'flipper_length_mm': rec['flipper_length_mm'],
'island': VOCABS['island'][rec['island']],
'sex': VOCABS['sex'][rec['sex']],
'species': VOCABS['species'][rec['species']]
}
self._examples.append(ex)
Dense(256, activation="relu"),
Dropout(0.2),
Dense(256, activation="relu"),
Dropout(0.2),
Dense(2, activation=None)
] # No activation, pure Q-values
learning_rate = 0.00025 # Learning rate
gamma = 0.0 # Discount factor
min_epsilon = 0.5 # Minimal and final chance of choosing random action
decay_episodes = episodes // 10 # Number of episodes to decay from 1.0 to `min_epsilon``
min_class = [1] # Minority classes
maj_class = [0] # Majority classes
df = tfds.as_dataframe(*tfds.load("titanic", split='train', with_info=True))
y = df.survived.values
df = df.drop(columns=[
"survived", "features/boat", "features/cabin", "features/home.dest",
"features/name", "features/ticket"
])
df = df.astype(np.float64)
df = (df - df.min()) / (
df.max() - df.min()
) # Normalization should happen after splitting train and test sets
X_train, X_test, y_train, y_test = train_test_split(df.to_numpy(),
y,
stratify=y,
test_size=0.2)
X_train, y_train, X_test, y_test, X_val, y_val = get_train_test_val(
def ds_to_texts_and_labels(ds):
# necessary for tokenization
df = tfds.as_dataframe(ds)
sentences = df['text'].to_list()
return [s.decode('utf-8') for s in sentences], np.array(df['label'])
def build_dsprites_dataframe(target_path):
"""Recreates the dsprites dataframe from base tfds version.
Each image is converted to png and written to the 'images' subfolder of the
specified target_path.
The dataframe contains the latent values and labels of each example, a one-hot
encoding of its shape, and the path to the corresponding image.
Args:
target_path: Str, path to where the dataframe and images should be saved.
Returns:
Location where dataframe was saved.
"""
tfds_dataset, tfds_info = tfds.load('dsprites',
split='train',
with_info=True,
shuffle_files=False)
num_examples = tfds_info.splits['train'].num_examples
# list the features we care about
feature_keys = list(tfds_info.features.keys())
feature_keys.remove('image')
feature_keys.remove('value_shape')
feature_keys.remove('label_shape')
shapes = ['square', 'ellipse', 'heart']
# helper function to modify how the data is stored in the tf dataset before
# we convert it to a pandas dataframe
def pandas_setup(x):
# encoding the image as a png byte string turns out to be a convenient way
# of temporarily storing the images until we can write them to disk.
img = tf.io.encode_png(x['image'])
latents = {k: x[k] for k in feature_keys}
latents.update(
{k: int(x['label_shape'] == i)
for i, k in enumerate(shapes)})
latents['png'] = img
return latents
temp_ds = tfds_dataset.map(pandas_setup)
dsprites_df = tfds.as_dataframe(temp_ds)
dsprites_df = dsprites_df[shapes + feature_keys +
['png']] # reorder columns
# setup for saving the pngs to disk
if os.path.basename(target_path).endswith('.csv'):
dataset_dir = os.path.dirname(target_path)
dataframe_location = target_path
else:
dataset_dir = target_path
dataframe_location = os.path.join(target_path, 'dsprites_df.csv')
images_path = os.path.join(dataset_dir, 'images')
tf.io.gfile.makedirs(images_path) # creates any missing parent directories
padding = len(str(num_examples))
temp_index = pd.Series(range(num_examples))
def create_image_paths(x):
path_to_file = os.path.join(images_path,
str(x).zfill(padding) + '.png')
return path_to_file
# create a col in the dataframe for the image file path
dsprites_df['img_path'] = temp_index.apply(create_image_paths)
# iterate through the dataframe and save each image to specified folder
for i, x in dsprites_df.iterrows():
img = tf.io.decode_image(x['png'])
with tf.io.gfile.GFile(x['img_path'], 'wb') as f:
tf.keras.preprocessing.image.save_img(f,
img.numpy(),
file_format='PNG')
if i % 100 == 0:
logging.info('%s of %s images processed', i + 1, num_examples)
dsprites_df.drop(columns=['png'], inplace=True)
logging.info('finished processing images')
logging.info('conversion complete, saving...')
with tf.io.gfile.GFile(dataframe_location, 'wb') as f:
dsprites_df.to_csv(f, index=False)
# also make a copy so if you screw up the original df you don't have to run
# the entire generation process again
_ = data_utils.make_backup(dataframe_location)
return dataframe_location
import tensorflow as tf
import tensorflow_datasets as tfds
import pandas as pd
ds, ds_info = tfds.load('cnn_dailymail', split='train', shuffle_files=True, with_info=True)
assert isinstance(ds, tf.data.Dataset)
df = tfds.as_dataframe(ds, ds_info)
df.head()
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_datasets as tfds
train = tfds.load("cnn_dailymail", split="train[0:20000]")
validation = tfds.load("cnn_dailymail", split="validation[0:3000]")
test = tfds.load("cnn_dailymail", split="test[0:5000]")
train_df = tfds.as_dataframe(train)
validation_df = tfds.as_dataframe(validation)
test_df = tfds.as_dataframe(validation)
# Decoding bytes to string in pandas in both columns
def bytes_to_str(dataframe):
'''
Takes a dataframe column and converts it into a proper string
'''
dataframe['article'] = dataframe['article'].str.decode("utf-8")
dataframe['highlights'] = dataframe['highlights'].str.decode("utf-8")
return dataframe
if __name__ == "__main__":
# Create a list of dataframes and convert byte data to string
df_list = [train_df, test_df, validation_df]
for dframe in df_list:
class_names = [
'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt',
'Sneaker', 'Bag', 'Ankle boot'
]
def get_name(id):
return class_names[id]
num_train_examples = metadata.splits['train'].num_examples
num_test_examples = metadata.splits['test'].num_examples
print("Number of training examples: {}".format(num_train_examples))
print("Number of test examples: {}".format(num_test_examples))
tfds.as_dataframe(train_dataset.take(5), metadata)
# ### Normalize
# # Preprocess the data
# The value of each pixel in the image data is an integer in the range `[0,255]`.
# For the model to work properly, these values need to be normalized to the range `[0,1]`.
# So here we create a normalization function, and then apply it to each image in the test and train datasets.
def normalize(images, labels):
images = tf.cast(images, tf.float32)
images /= 255
return images, labels
#looking at just one sample of our data
pt = data_train.take(1)
# type(pt)
#can convert this TakeDataset object to a numpy array (can do this for the whole dataset too)
print("Features and label for first entry")
for features, label in tfds.as_numpy(pt):
print(features,label)
#we want to load dataset as a a dictionary of tf.Tensors (can't transform tuples to dataframe)
data_train_white = tfds.load('wine_quality/white',split='train')
data_train_red = tfds.load('wine_quality/red',split='train')
#transform dictionary to dataframe - combining red and white wine
df_white = tfds.as_dataframe(data_train_white)
df_red = tfds.as_dataframe(data_train_red)
df = pd.concat([df_white,df_red])
print('number of samples',len(df['quality']))
#what are our output possibilities?
print('possible wine quality ratings',df['quality'].unique())
#do we have any missing data (empty or NaN entries in features or labels)?
dataNans = df.isnull().values.any()
if not dataNans:
print("all good!")
#it's helpful to separate our input features from our target features (quality)
print(tf.config.list_logical_devices())
from datetime import datetime
print(tf.__version__)
from typing import Dict, Text
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
# Ratings data.
ratings = tfds.load('movielens/100k-ratings', split="train")
# Features of all the available movies.
movies = tfds.load('movielens/100k-movies', split="train")
x = tfds.as_dataframe(ratings)
y = tfds.as_dataframe(movies)
print(x)
print(y)
# Select the basic features.
ratings = ratings.map(lambda x: {
"movie_id": tf.strings.to_number(x["movie_id"]),
"user_id": tf.strings.to_number(x["user_id"])
})
movies = movies.map(lambda x: tf.strings.to_number(x["movie_id"]))
x = tfds.as_dataframe(ratings)
y = tfds.as_dataframe(movies)
print(x)
print(y)
data_dir = 'D:\\Sandbox\\Github\\DATA_TFDS'
dataset, info = tfds.load(
name="cifar10",
data_dir=data_dir,
with_info=True,
as_supervised=True, # mutually exclusive with split
shuffle_files=True,
download=False)
print(info.features["label"].names)
print(info.features["label"].int2str(7))
train_dataset, test_dataset = dataset['train'], dataset['test']
tfds.as_dataframe(train_dataset.take(5), info)
def normalize(images, labels):
images = tf.cast(images, tf.float32)
images /= 255
return images, labels
# ### Prepare data for Model
BATCH_SIZE = 64
TRAIN_SIZE = len(train_dataset) # memory max = 1000
# if repeat() then model needs epoch/step
# train_dataset = #train_dataset.cache().repeat().shuffle(TRAIN_SIZE).batch(BATCH_SIZE).prefetch(TRAIN_SIZE)
