def convert_checkpoint(estimator_type, source_checkpoint, source_graph,
target_checkpoint):
"""Converts checkpoint from TF 1.x to TF 2.0 for CannedEstimator.
Args:
estimator_type: The type of estimator to be converted. So far, the allowed
args include 'dnn', 'linear', and 'combined'.
source_checkpoint: Path to the source checkpoint file to be read in.
source_graph: Path to the source graph file to be read in.
target_checkpoint: Path to the target checkpoint to be written out.
"""
with ops.Graph().as_default():
# Get v1 optimizer names and it's corresponding variable name
reader = training.NewCheckpointReader(source_checkpoint)
variable_names = sorted(reader.get_variable_to_shape_map())
opt_names_v1 = {}
for var_name in variable_names:
for opt_name in OPT_NAME_V1_TO_V2:
if opt_name in var_name:
opt_names_v1[opt_name] = var_name
# SGD doesn't appear in optimizer variables, so we need to add it manually
# if no optimizer is found in checkpoint for DNN or Linear model.
if not opt_names_v1:
if estimator_type == 'dnn' or estimator_type == 'linear':
opt_names_v1['SGD'] = ''
# As the case is not handled in the converter if dnn_optimizer and
# linear_optimizer in DNNLinearCombined model are the same, an error is
# is raised if two SGD optimizers are used in DNNLinearCombined model.
elif estimator_type == 'combined':
raise ValueError(
'Two `SGD` optimizers are used in DNNLinearCombined '
'model, and this is not handled by the checkpoint '
'converter.')
# A dict mapping from v2 variable name to the v2 variable.
var_map = {}
# A dict mapping from v2 variable name to v1 variable name.
var_names_map = {}
# Determine the names of dnn_optimizer and linear_optimizer in
# DNNLinearCombined model.
if estimator_type == 'combined':
linear_opt_v1 = None
if len(opt_names_v1) == 1: # When one of the optimizer is 'SGD'.
key = list(opt_names_v1.keys())[0]
# Case 1: linear_optimizer is non-SGD, and dnn_optimizer is SGD.
if opt_names_v1[key].startswith('linear/linear_model/'):
linear_opt_v1 = key
# Case 2: linear_optimizer is SGD, and dnn_optimizer is non-SGD.
if not linear_opt_v1:
linear_opt_v1 = 'SGD'
opt_names_v1['SGD'] = ''
else: # two non-SGD optimizers
for key in opt_names_v1:
if opt_names_v1[key].startswith('linear/linear_model/'):
linear_opt_v1 = key
# Add the 'iter' hyper parameter to the new checkpoint for
# linear_optimizer. Note dnn_optimizer uses global_step.
tensor = reader.get_tensor('global_step')
var_name_v2 = 'training/' + OPT_NAME_V1_TO_V2[
linear_opt_v1] + '/iter'
var_name_v1 = 'global_step'
_add_new_variable(tensor, var_name_v2, var_name_v1, var_map,
var_names_map)
for opt_name_v1 in opt_names_v1:
# Convert all existing variables from checkpoint.
_convert_variables_in_ckpt(opt_name_v1, reader, variable_names,
var_map, var_names_map, estimator_type)
# Convert hyper parameters for optimizer v2 from the graph.
_convert_hyper_params_in_graph(source_graph, opt_name_v1, var_map,
var_names_map)
# Log the variable mapping from opt v1 to v2.
logging.info('<----- Variable names converted (v1 --> v2): ----->')
for name_v2 in var_names_map:
logging.info('%s --> %s' % (var_names_map[name_v2], name_v2))
# Save to checkpoint v2.
saver = saver_lib.Saver(var_list=var_map)
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
logging.info('Writing checkpoint_to_path %s' % target_checkpoint)
saver.save(sess, target_checkpoint)
def list_variables(filename):
reader = training.NewCheckpointReader(filename)
variable_map = reader.get_variable_to_shape_map()
names = sorted(variable_map.keys())
return names
# input()
datas_test = genfromtxt(DATAS_TEST_FILE, delimiter=";")
datas_test = datas_test[:num_features]
X_test = genfromtxt(X_TEST_FILE, delimiter=";")
Y_test = genfromtxt(Y_TEST_FILE, delimiter=";")
Y_test = np.transpose([Y_test])
######################################################
####### LOAD LEARNING VARIABLES
######################################################
# Create graph
sess = tf.Session()
# Load Learning Variables
variables = train.NewCheckpointReader(
os.path.join(os.getcwd(), 'spam_detection_datas.ckpt'))
print("Model restored.")
# Get the Weight and Bias Vectors
W = variables.get_tensor("W")
b = variables.get_tensor("b")
print("X_test" + str(X_test) + "\n Y_test" + str(Y_test))
# Do some work with the model
######################################################
####### TEST
######################################################
# Placeholder definition
x = tf.placeholder("float", shape=[None, num_features], name="x")
