how to get tensor in default graph - tensorflow

Question

I just write a model class, in its init function, I build a graph and then I want to training with the tensor in the default graph, but I just dont know how to get those tensor in that graph. The ae is Autoencoder class and it has some class function like partial_fit(). For example, I want to get ae and x in train_test().
class Model:
def __init__(self, param):
# deal param
self.param = param
# create & build graph
self.graph = tf.Graph()
self.init_graph = self.build_graph()
# create session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
gpu_num = os.getpid() % 1 # cuda_gpu_count()
config.gpu_options.visible_device_list = str(gpu_num)
self.sess = tf.Session(config=config, graph=self.graph)
def build_graph(self):
with self.graph.as_default():
# define the autoencoder
x = tf.placeholder(tf.float32, [None, n_hidden2])
ae = Autoencoder(n_layers=[n_inputs, n_hidden],
transfer_function=tf.nn.relu,
optimizer=tf.train.AdamOptimizer(learning_rate=lr))
return tf.global_variables_initializer()
def __del__(self):
# explicitly collect resources by closing and deleting session and graph
self.sess.close()
del self.sess
del self.graph
del self.param
# train models and return the test accuracy
def train_test(self, train_data, train_label, test_data, test_label):
with self.graph.as_default():
# Initialization
sess = self.sess
sess.run(self.init_graph)
temp1 = ae.partial_fit()
temp2 = x
I think because I have use with self.graph.as_default(): in train_test function so I can get those tensor directely, but it shows that those name is not defined.

Answer 1

Define it as a class attribute like this:
def build_graph(self):
with self.graph.as_default():
# define the autoencoder
self.x = tf.placeholder(tf.float32, [None, n_hidden2])
self.ae = Autoencoder(
n_layers=[n_inputs, n_hidden], transfer_function=tf.nn.relu,
optimizer=tf.train.AdamOptimizer(learning_rate=lr))
return tf.global_variables_initializer()
And then access these attributes using self:
def train_test(self, train_data, train_label, test_data, test_label):
with self.graph.as_default():
# Initialization
sess = self.sess
sess.run(self.init_graph)
temp1 = self.ae.partial_fit()
temp2 = self.x
Alternatively, you could retrieve the tensor by using graph.as_graph_element(), for example:
graph = tf.Graph()
with graph.as_default():
x = tf.placeholder(tf.float32, shape=(None, 2), name='input')
logits = tf.layers.dense(x, 2)
input_ = graph.as_graph_element('input',
allow_tensor=True,
allow_operation=True)
# `input_` is an operation that outputs placeholder `x`
input_ = input_.outputs[0]
print(x == input_) # True