@soilad You can use tf.train.write_graph
to save the graph but that doesn't save the weights. Tensorflow has a freeze_graph tool to combine the weights with the graph def: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py
Alternatively you can assign the weights as const values to trainable variables before exporting the graph:
# Add a new "assign_trained_variables" operation which fills the
# weights and biases with constant values (trained values)
ops = []
for v in tflearn.variables.get_all_trainable_variable():
vc = tf.constant(v.eval(session=model.session))
ops.append(tf.assign(v, vc))
# When using the exported graph, the "assign_trained_variables" op must be run first
tf.group(*ops, name="assign_trained_variables")
Hey, I have a question about feed_dics in tflearn. In tensorflow the feed_dict will load the data at each step, so I can preprocess data in batches. Tflearn, however loads the data only once into the feed_dict. Is this correct? I have a function that prepossess the data for each batch. when I use it as input in a tflearn.trainer it only preprocesses the first batch.
trainer.fit(feed_dicts=self.feed_dict(), val_feed_dicts=self.val_feed_dict(),
n_epoch=40, show_metric=True)
where the feed_dict() function returns a feed dict for each batch. Any ideas how to solve this in tflearn?
Alexnet(init_model='path/to/weights.np', num_classes=1000, nul_layers_to_init=8)
Hi, I wanted to know here is one tutorial about Classification , But its working on numerical data , Now i f i have a csv with column 0 as categorial data and other columns are having keywords , I want to classify sentence basis on those keywords , I know i can do this with text classifier but is there any way i can do text classification with using same logic of this tutorial ?
https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md
pip install git+https://github.com/tflearn/tflearn.git
, what version of tf it expects? I see 1.1 on the website, some words are there about that I can use the latest tensorflow too, so I use 1.7. But if I try to run some code it shows errors that look like API incompatibility issues, like if tflearn wants some thing older then 1.7.