make tf.Estimator use default graph - tensorflow

Question

I am trying to make use of tensorflow protobuffer feeding pipeline. The easiest way seemed to use tf.estimator.Estimator with tf.contrib.data.TFRecordDataset. However, I came across the issue that it creates a new Graph in spite of being launched within with g.as_default(). In following code I see that both model tensors and tensors returned by the TFRecordDataset are the same before I feed them to Estimator, but become different within the Estimator. Any ideas how to put them on the same graph?
# coding: utf-8
import sys
import tensorflow as tf
from keras.applications.inception_v3 import InceptionV3
import numpy as np
final_activation='linear'
g = tf.Graph()
with g.as_default():
model = InceptionV3(weights='imagenet',
include_top=True,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000)
def model_fn(mode, features, labels, params):
optimizer = params["optimizer"]
opt_params= params.get("opt_params", {})
predictions = model(features)
if (mode == tf.estimator.ModeKeys.TRAIN or
mode == tf.estimator.ModeKeys.EVAL):
loss = tf.contrib.keras.backend.categorical_crossentropy(predictions, labels)
#loss = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logyhat)
else:
loss = None
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = getattr(tf.train, optimizer)
train_op = optimizer(opt_params).minimize(loss)
else:
train_op = None
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op)
def parser(record):
keys_to_features = {
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
}
features = tf.parse_single_example(
record,
features=keys_to_features)
# Convert from a scalar string tensor to a uint8 tensor
image = tf.decode_raw(features['image_raw'], tf.float32)
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
image_shape = tf.stack([height, width, 3])
image = tf.reshape(image, image_shape)
label = tf.cast(features["label"], tf.int32)
return image, label
def get_dataset_inp_fn(filenames, epochs=20):
def dataset_input_fn():
dataset = tf.contrib.data.TFRecordDataset(filenames)
# Use `Dataset.map()` to build a pair of a feature dictionary and a label
# tensor for each example.
dataset = dataset.map(parser)
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(32)
dataset = dataset.repeat(epochs)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
return dataset_input_fn
inpfun = get_dataset_inp_fn(["mydataset.tfrecords"], epochs=20)
x,y = inpfun()
print("X", x.graph)
print("DEFAULT", g)
print("MODEL", model.input.graph)
# everything is on the same graph
if not x.graph is tf.get_default_graph():
raise ValueError()
with tf.Session(graph=g) as sess:
est = tf.estimator.Estimator(
model_fn,
model_dir=None,
config=None,
params={"optimizer": "AdamOptimizer",
"opt_params":{}}
)
est.train(inpfun)