deepak-karkala

from tensorflow_examples.models.pix2pix import pix2pix

def build_model():
    LABEL_NAMES = np.asarray([
        'background', 'couch', 'chair', 'bed', 'vase', 'bowl', 'cup',
        'wine-glass', 'potted-plant'
    ])

    # Each pixel classified into one of OUTPUT_CHANNELS classes
    OUTPUT_CHANNELS = len(LABEL_NAMES) #3

deepak-karkala

# Training dataset
train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_masks))
train_dataset = train_dataset.map(load_image_train)
train_dataset = train_dataset.cache()
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE)
train_dataset = train_dataset.repeat()
train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)

deepak-karkala

# Hyperparameter tuning using Tensorflow and Tensor board

# Testing performance with varioud optimizers
HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd', 'rmsprop']))
METRIC_ACCURACY = 'accuracy'
with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
  hp.hparams_config(
    hparams=[HP_OPTIMIZER],
    metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],
  )

deepak-karkala

# Tune hyperparameters using Grid search and Randomised search Cross Validation

from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV

# Grid Search Cross Validation
# Specify discrete values for hyperparameters
param_grid = [
    {'max_depth': [1, 20, 100], 'max_features': [1, 5, 15, 20],

deepak-karkala

from sklearn.model_selection import learning_curve

# sklearn's learning_curve function can be used for following purposes
# 1. Learning curve: To study how training and validation error varies with more training examples
#      train_scores, valid_scores vs train_sizes
# 2. Model scalability: To study the time required to fit model as training data size increases
#      fit_times vs train_sizes
# 3. Model performance: To study how training error changes with time required to fit
#      train_scores vs fit_times

deepak-karkala

# Preprocessing pipeline for Numerical and Categorical features

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OrdinalEncoder
from sklearn.compose import ColumnTransformer


############ Pipeline for numerical features #############
# Numerical features