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- import tensorflow as tf
- # define two variables w1 and w2 as weight matrices, use seed to guarantee we get constant result.
- w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
- w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
- # define input eigenvector as a constant vector
- # x = tf.constant([[0.7, 0.9]])
- # use placeholder to store data in a constant place rather than create a large number of variables
- x = tf.placeholder(tf.float32, shape=[3, 2], name="input")
- # forward propagation to receive the output
- a = tf.matmul(x, w1)
- y = tf.matmul(a, w2)
- with tf.Session() as sess:
- # sess.run(w1.initializer)
- # sess.run(w2.initializer)
- sess.run(tf.global_variables_initializer())
- print (sess.run(y, feed_dict={x: [[0.7, 0.9], [0.1, 0.4], [0.5, 0.8]]}))
- saver = tf.train.Saver()
- saver.export_meta_graph("model.ckpt.meda.json", as_text=True)
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