def main(params):
data = polyvore_dataset()
transforms = data.get_data_transforms()
X_train, X_test, y_train, y_test, n_classes = data.create_dataset()
train_set = (X_train, y_train, transforms['train'])
test_set = (X_test, y_test, transforms['test'])
dataset_size = {'train': len(y_train), 'test': len(y_test)}
params = {
'batch_size': Config['batch_size'],
'n_classes': n_classes,
'shuffle': True
}
train_generator = DataGenerator(train_set, dataset_size, params)
test_generator = DataGenerator(test_set, dataset_size, params)
from tensorflow.keras.layers import MaxPooling2D, Dense, Dropout, Input, Conv2D, Flatten
from data import polyvore_dataset, DataGenerator, PredictDataGenerator
from tensorflow.keras.utils import plot_model
from tensorflow.keras.models import Model
import matplotlib.pyplot as plt
from utils import Config
import tensorflow as tf
import numpy as np
from tensorflow.keras import regularizers
from sklearn.preprocessing import LabelEncoder
if __name__ == '__main__':
# data generators
dataset = polyvore_dataset()
transforms = dataset.get_data_transforms()
X_train, X_test, y_train, y_test, n_classes, le_dictionary = dataset.create_dataset(
)
if Config['debug']:
train_set = (X_train[:100], y_train[:100], transforms['train'])
test_set = (X_test[:100], y_test[:100], transforms['test'])
dataset_size = {'train': 100, 'test': 100}
else:
train_set = (X_train, y_train, transforms['train'])
test_set = (X_test, y_test, transforms['test'])
dataset_size = {'train': len(y_train), 'test': len(y_test)}
params = {
'batch_size': Config['batch_size'],
'n_classes': n_classes,
'shuffle': True
