--- a
+++ b/config.py
@@ -0,0 +1,117 @@
+import os
+
+# The path points to the original images and is
+# used if a preprocessing step needs to be executed
+ALL_IMGS = 'D:/stage1'
+
+LABELS_INPUT_DIR = './input'
+
+PATIENT_LABELS_CSV = LABELS_INPUT_DIR + '/stage1_labels.csv'
+TEST_PATIENTS_IDS = LABELS_INPUT_DIR + '/stage1_sample_submission.csv'
+
+VALIDATION_PATINETS_IDS = LABELS_INPUT_DIR + '/validation_data.csv'
+TRAINING_PATIENTS_IDS = LABELS_INPUT_DIR + '/training_data.csv'
+
+MODELS_STORE_DIR = './models'
+SOLUTION_FILE_PATH = './solution_last.csv'
+REAL_SOLUTION_CSV = './input/stage1_solution.csv'
+RESTORE_MODEL_CKPT = 'model_best_sensitivity.ckpt'
+SUMMARIES_DIR = './summaries/model_summary'
+RESTORE = False
+START_STEP = 1
+
+COLUMN_NAME = 'cancer'
+ID_COLUMN_NAME = 'id'
+
+CANCER_CLS = 1
+NO_CANCER_CLS = 0
+
+# Image properties
+OUT_SCAN = -2000
+MIN_BOUND = -1000.0
+MAX_BOUND = 400.0
+
+BACKGROUND = 0
+BATCH_SIZE = 1
+NUM_CHANNELS = 1
+N_CLASSES = 2
+
+# Preprocessing options used for the defined models
+BASELINE_PREPROCESS = 0
+MORPHOLOGICAL_OPERATIONS = 1
+WATERSHED = 2
+
+FETCHED_DATA_DIR = './fetch_data'
+
+# Configuration for the buckets with preprocessed images
+# to download the data from
+bucket_names = {
+    BASELINE_PREPROCESS: 'baseline-preprocess',
+    MORPHOLOGICAL_OPERATIONS: 'segmented-lungs',
+    WATERSHED: 'segmented-lungs-watershed'  
+}
+
+# construct the path using the object name in the buckets, since
+# by default images are stored in directories with the object name
+preprocessed_imgs = {
+    BASELINE_PREPROCESS: os.path.join(FETCHED_DATA_DIR, 'baseline_preprocessing'),
+    MORPHOLOGICAL_OPERATIONS: os.path.join(FETCHED_DATA_DIR, 'segmented_morph_op'),
+    WATERSHED: os.path.join(FETCHED_DATA_DIR, 'segmented_watershed')  
+}
+
+
+# Defined models
+BASELINE = 'baseline'
+BASELINE_WITH_SEGMENTATION = 'baseline_with_segmentation'
+NO_REGULARIZATION = 'no_regularization'
+NO_REGULARIZATION_WATERSHED = 'no_regularization_watershed'
+DROPOUT_L2NORM_REGULARIZARION = 'with_regularization'
+REGULARIZATION_MORE_SLICES = 'regularization_more_slices'
+WITH_DATA_AUGMENTATION = 'more_slices_augmentation'
+
+
+model_to_img_shape = { 
+    BASELINE: (100, 128, 128),
+    BASELINE_WITH_SEGMENTATION: (100, 128, 128),
+    NO_REGULARIZATION: (140, 256, 256),
+    NO_REGULARIZATION_WATERSHED: (140, 256, 256),
+    DROPOUT_L2NORM_REGULARIZARION: (140, 256, 256),
+    REGULARIZATION_MORE_SLICES: (180, 256, 256),
+    WITH_DATA_AUGMENTATION: (180, 256, 256)
+}
+
+model_to_preprocessing = {
+    BASELINE: BASELINE_PREPROCESS,
+    BASELINE_WITH_SEGMENTATION: MORPHOLOGICAL_OPERATIONS,
+    NO_REGULARIZATION: MORPHOLOGICAL_OPERATIONS,
+    NO_REGULARIZATION_WATERSHED: WATERSHED,
+    DROPOUT_L2NORM_REGULARIZARION: WATERSHED,
+    REGULARIZATION_MORE_SLICES: WATERSHED,
+    WITH_DATA_AUGMENTATION: WATERSHED
+}
+
+# This configuration must be changed in order to select other
+# predefined model for training
+SELECTED_MODEL = WITH_DATA_AUGMENTATION
+
+SEGMENTATION_ALGO = model_to_preprocessing.get(SELECTED_MODEL, 
+    model_to_preprocessing[WITH_DATA_AUGMENTATION]) 
+
+IMG_SHAPE = model_to_img_shape.get(SELECTED_MODEL, 
+                                   model_to_img_shape[WITH_DATA_AUGMENTATION])
+
+SLICES, IMAGE_PXL_SIZE_X, IMAGE_PXL_SIZE_Y = IMG_SHAPE
+
+SEGMENTED_LUNGS_DIR = preprocessed_imgs[SEGMENTATION_ALGO]
+
+BUCKET_IN_USE = bucket_names[SEGMENTATION_ALGO]
+
+REQUIRED_IMGS_COUNT = 1595
+
+# Google cloud API client related
+# Use for downloading preprocessed images from the
+# cloud buckets
+PROJECT_NAME = 'lung-cancer-tests'
+CREDENTIALS_DIR = './fetch_data'
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] =  os.path.join(CREDENTIALS_DIR,
+    'lung-cancer-tests-168b7b36ab99.json')
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