# Path: studio/fs_tracker.py
# STUDIOML_EXPERIMENT = 'STUDIOML_EXPERIMENT'
# STUDIOML_HOME = 'STUDIOML_HOME'
# STUDIOML_ARTIFACT_MAPPING = 'STUDIOML_ARTIFACT_MAPPING'
# def get_experiment_key():
# def get_studio_home():
# def setup_experiment(env, experiment, clean=True):
# def get_artifact(tag):
# def get_artifacts():
# def get_model_directory(experiment_name=None):
# def get_artifact_cache(tag, experiment_name=None):
# def get_blob_cache(blobkey):
# def get_model_directory(experiment_name=None):
# def _get_artifact_mapping_path(experiment_name=None):
# def _get_experiment_key(experiment):
# def _setup_model_directory(experiment_name, clean=False):
# def get_queue_directory():
# def get_tensorboard_dir(experiment_name=None):


import os
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
from studio import fs_tracker



model = Sequential()
model.add(Dense(2, input_shape=(2,)))

weights = model.get_weights()
new_weights = [np.array([[2, 0], [0, 2]])]
# print weights
# new_weights = []
# for weight in weights:
#   new_weights.append(weight + 1)


# Path: studio/fs_tracker.py
# STUDIOML_EXPERIMENT = 'STUDIOML_EXPERIMENT'
# STUDIOML_HOME = 'STUDIOML_HOME'
# STUDIOML_ARTIFACT_MAPPING = 'STUDIOML_ARTIFACT_MAPPING'
# def get_experiment_key():
# def get_studio_home():
# def setup_experiment(env, experiment, clean=True):
# def get_artifact(tag):
# def get_artifacts():
# def get_model_directory(experiment_name=None):
# def get_artifact_cache(tag, experiment_name=None):
# def get_blob_cache(blobkey):
# def get_model_directory(experiment_name=None):
# def _get_artifact_mapping_path(experiment_name=None):
# def _get_experiment_key(experiment):
# def _setup_model_directory(experiment_name, clean=False):
# def get_queue_directory():
# def get_tensorboard_dir(experiment_name=None):


# Path: tests/save_model.py
import os
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
from studio import fs_tracker




model = Sequential()
model.add(Dense(2, input_shape=(2,)))

weights = model.get_weights()
new_weights = [np.array([[2, 0], [0, 2]])]
# print weights
# new_weights = []
# for weight in weights:
#   new_weights.append(weight + 1)


model.set_weights(new_weights)