恢复张量流模型

Question

I want to restore a tensorflow model after it's trained. I know that I can use tf.train.Saver but the problem is with the restoring because I get confused with the names for get_tensor_by_name . Can anybody help me? This is my graph:

x_hat = tf.placeholder(tf.float32, shape=[None, dim_img], name='input_img')
x = tf.placeholder(tf.float32, shape=[None, dim_img], name='target_img')

# dropout
keep_prob = tf.placeholder(tf.float32, name='keep_prob')

# input for PMLR
z_in = tf.placeholder(tf.float32, shape=[None, dim_z], name='latent_variable')

# network architecture
y, z, loss, neg_marginal_likelihood, KL_divergence = vae.autoencoder(x_hat, x, dim_img, dim_z, n_hidden,
                                                                                keep_prob)

Answer 1

When you save a model you save 2 things: 1) the meta graph, that is a representation of the graph (all the TF symbols you've defined; and 2) the checkpoint which contains the actual Variable values (which are saved and restored by name).

When you restore you can restore one or both of those components. What you are describing is restoring both the meta graph AND the checkpoint data. In this case you need to look up the various operations and tensors you're interested by name, which can be confusing (especially if you didn't name your variables well, which you should always do).

# In this method you import the meta graph then restore
saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
saver.restore(sess, 'my-save-dir/my-model-10000')

The other option to restoring (which I prefer mysefl) is to not load the meta graph at all. Instead just re-run the same code you originally used to create the graph (if you've done things well this will all be organized in one place). Then you only restore the checkpoint. This approach has the benefit that you can easily keep a reference to all the operations you'll need (such as cost, train_op, placeholders, etc).

# This method only performs the restor operation 
# assuming the graph is already constructure
saver.restore(sess, 'my-save-dir/my-model-10000')