CODEWITHSUNDEEP
pythontensorflow
Federico Matera
Federico Matera

Reputation: 245

How to restore a Tensorflow model

I'm learning to use Tensorflow and I wrote this Python script that learns from mnist db, save the model and make a prediction on a image:

X = tf.placeholder(tf.float32, [None, 28, 28, 1])
W = tf.Variable(tf.zeros([784, 10], name="W"))
b = tf.Variable(tf.zeros([10]), name="b")
Y = tf.nn.softmax(tf.matmul(tf.reshape(X, [-1, 784]), W) + b)
# ...
init = tf.global_variables_initializer()

saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(init)

    # ... learning loop

    saver.save(sess, "/tmp/my-model")

    # Make a prediction with an image
    im = numpy.asarray(Image.open("digit.png")) / 255
    im = im[numpy.newaxis, :, :, numpy.newaxis]
    dict = {X: im}
    print("Prediction: ", numpy.array(sess.run(Y, dict)).argmax())

The prediction is correct, but I can't restore the saved model for reusing. I wrote this other script that tries to restore the model and make the same prediction:

X = tf.placeholder(tf.float32, [None, 28, 28, 1])
W = tf.Variable(tf.zeros([784, 10]), name="W")
b = tf.Variable(tf.ones([10]) / 10, name="b")
Y = tf.nn.softmax(tf.matmul(tf.reshape(X, [-1, 784]), W) + b)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    saver = tf.train.import_meta_graph('/tmp/my-model.meta')
    saver.restore(sess, tf.train.latest_checkpoint('/tmp/'))

    # Make a prediction with an image
    im = numpy.asarray(Image.open("digit.png")) / 255
    im = im[numpy.newaxis, :, :, numpy.newaxis]
    dict = {X: im}
    print("Prediction: ", numpy.array(sess.run(Y, dict)).argmax())

but the prediction is wrong. How can I restore my variables and make a prediction? Thanks

Upvotes: 0

Views: 280

Answers (1)

LI Xuhong
LI Xuhong

Reputation: 2356

When test, comment this line 

# saver = tf.train.import_meta_graph('/tmp/my-model.meta')

will solve your problem.

import_meta_graph will create a new Graph/model saved in the '.meta' file and the new model will co-exist with the model you created manually. The saver is assigned to the new model, so saver.restore restores the trained weights to the new model, but the sess runs using the model you created manually.

Upvotes: 1

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