For TensorFlow 1.x and Python 3, there is my simple solution:
X_init = tf.placeholder(tf.float32, shape=(m_input, n_input))
X = tf.Variable(X_init)
sess.run(tf.global_variables_initializer(), feed_dict={X_init: data_for_X})
In practice, you will mostly specify Graph and Session for continuous computation, this following code will help you:
my_graph = tf.Graph()
sess = tf.Session(graph=my_graph)
with my_graph.as_default():
X_init = tf.placeholder(tf.float32, shape=(m_input, n_input))
X = tf.Variable(X_init)
sess.run(tf.global_variables_initializer(), feed_dict={X_init: data_for_X})
.... # build your graph with X here
.... # Do some other things here
with my_graph.as_default():
output_y = sess.run(your_graph_output, feed_dict={other_placeholder: other_data})