How to use a tflearn trained model in an application? - python-3.x

Question

I am currently trying use a trained model in an application.
I've been using this code to generate US city names with an LSTM model. The code works fine and I do manage to get city names.
Right now, I am trying to save the model so I can load it in a different application without training the model again.
Here is the code of my basic application :
from __future__ import absolute_import, division, print_function
import os
from six import moves
import ssl
import tflearn
from tflearn.data_utils import *
path = "US_cities.txt"
maxlen = 20
X, Y, char_idx = textfile_to_semi_redundant_sequences(
path, seq_maxlen=maxlen, redun_step=3)
# --- Create LSTM model
g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
g = tflearn.lstm(g, 512, return_seq=True, name="lstm1")
g = tflearn.dropout(g, 0.5, name='dropout1')
g = tflearn.lstm(g, 512, name='lstm2')
g = tflearn.dropout(g, 0.5, name='dropout')
g = tflearn.fully_connected(g, len(char_idx), activation='softmax', name='fc')
g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
learning_rate=0.001)
# --- Initializing model and loading
model = tflearn.models.generator.SequenceGenerator(g, char_idx)
model.load('myModel.tfl')
print("Model is now loaded !")
#
# Main Application
#
while(True):
user_choice = input("Do you want to generate a U.S. city names ? [y/n]")
if user_choice == 'y':
seed = random_sequence_from_textfile(path, 20)
print("-- Test with temperature of 1.5 --")
model.generate(20, temperature=1.5, seq_seed=seed, display=True)
else:
exit()
And here is what I get as an output :
Do you want to generate a U.S. city names ? [y/n]y
-- Test with temperature of 1.5 --
rk
Orange Park AcresTraceback (most recent call last):
File "App.py", line 46, in <module>
model.generate(20, temperature=1.5, seq_seed=seed, display=True)
File "/usr/local/lib/python3.5/dist-packages/tflearn/models/generator.py", line 216, in generate
preds = self._predict(x)[0]
File "/usr/local/lib/python3.5/dist-packages/tflearn/models/generator.py", line 180, in _predict
return self.predictor.predict(feed_dict)
File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/evaluator.py", line 69, in predict
o_pred = self.session.run(output, feed_dict=feed_dict).tolist()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 717, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 894, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (1, 25, 61) for Tensor 'InputData/X:0', which has shape '(?, 20, 61)'
Unfortunately, I can't see why the shape has changed when using generate() in my app. Could anyone help me solve this problem?
Thank you in advance
William

Answer 1

SOLVED?
One solution would be to simply add "modes" to the python script thanks to the argument parser :
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("mode", help="Train or/and test", nargs='+', choices=["train","test"])
args = parser.parse_args()
And then
if args.mode == "train":
# define your model
# train the model
model.save('my_model.tflearn')
if args.mode == "test":
model.load('my_model.tflearn')
# do whatever you want with your model
I dont really understand why this works and why when you're trying to load a model from a different script it doesn't.
But I guess this should be fine for the moment...