Tflearn: Support execution in Jupyter notebook

[bnaul]

Trying to run the contents of examples/basics/linear_regression.py (or anything, really) within a Jupyter notebook gives the following error:

---------------------------------
Run id: M51N6E
Log directory: /tmp/tflearn_logs/
---------------------------------------------------------------------------
UnsupportedOperation                      Traceback (most recent call last)
<ipython-input-1-99453409cb2b> in <module>()
     15                                 metric='R2', learning_rate=0.01)
     16 m = tflearn.DNN(regression)
---> 17 m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
     18 
     19 print("\nRegression result:")

/Users/brettnaul/miniconda3/envs/deep/lib/python3.5/site-packages/tflearn/models/dnn.py in fit(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, run_id)
    159                          snapshot_epoch=snapshot_epoch,
    160                          shuffle_all=shuffle,
--> 161                          run_id=run_id)
    162 
    163     def predict(self, X):

/Users/brettnaul/miniconda3/envs/deep/lib/python3.5/site-packages/tflearn/helpers/trainer.py in fit(self, feed_dicts, n_epoch, val_feed_dicts, show_metric, snapshot_step, snapshot_epoch, shuffle_all, run_id)
    188             validation_split(val_feed_dicts, feed_dicts)
    189 
--> 190             termlogger = callbacks.TermLogger(self.training_step)
    191             modelsaver = callbacks.ModelSaver(self.save,
    192                                               self.training_step,

/Users/brettnaul/miniconda3/envs/deep/lib/python3.5/site-packages/tflearn/callbacks.py in __init__(self, training_step)
     51         self.global_data_size = 0
     52         self.global_val_data_size = 0
---> 53         curses.setupterm()
     54         sys.stdout.write(curses.tigetstr('civis').decode())
     55 

/Users/brettnaul/miniconda3/envs/deep/lib/python3.5/site-packages/ipykernel/iostream.py in fileno(self)
    304 
    305     def fileno(self):
--> 306         raise UnsupportedOperation("IOStream has no fileno.")
    307 
    308     def write(self, string):

UnsupportedOperation: IOStream has no fileno.

I think this could be as simple as adding a flag to skip the TermLogger step, but maybe there's a solution that would look nice in the notebook and fall back to the curses version in the terminal?

• Plain text progress bar that works in both: https://github.com/noamraph/tqdm
• Fancy progress bar but not clear how hard it is to use outside of dask/distributed: http://matthewrocklin.com/blog/work/2016/02/26/dask-distributed-part-3#normalize-data

[aymericdamien]

Notebooks are now working, but there is another issue, because jupyter notebooks doesn't seem to be able to handle VT100 characters (such as \033[A), display isn't good and need to be fix.

As you proposed, maybe it is better to directly write a new callback that will used a different style, only for notebooks. I can investigate that later, unless someone want to help with this.

[georgeaf99]

Thanks for the fix @aymericdamien! I am also getting the following error when I try to run the linear regression example in Jupyter. It works as expected on the command line.

import tflearn

X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]
Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]

input_layer = tflearn.input_data(shape=[None])
linear_layer = tflearn.single_unit(input_layer)
regression_layer = tflearn.regression(linear_layer, optimizer="sgd", loss="mean_square",
                                      metric="R2", learning_rate=.01)

m = tflearn.DNN(regression_layer)
m.fit(X, Y, n_epoch=1000, show_metric=False, snapshot_epoch=False)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-16-0ed73d93e10c> in <module>()
     13 # Train the network
     14 m = tflearn.DNN(regression_layer)
---> 15 m.fit(X, Y, n_epoch=1000, show_metric=False, snapshot_epoch=False)
     16 
     17 # Results

/usr/local/lib/python2.7/site-packages/tflearn/models/dnn.pyc in fit(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, run_id)
    146         # TODO: check memory impact for large data and multiple optimizers
    147         feed_dict = feed_dict_builder(X_inputs, Y_targets, self.inputs,
--> 148                                       self.targets)
    149         feed_dicts = [feed_dict for i in self.train_ops]
    150         val_feed_dicts = None

/usr/local/lib/python2.7/site-packages/tflearn/utils.pyc in feed_dict_builder(X, Y, net_inputs, net_targets)
    240             elif len(net_inputs) > 1:
    241                 if np.ndim(X) < 2:
--> 242                     raise ValueError("Multiple inputs but only one data "
    243                                      "feeded. Please verify number of inputs "
    244                                      "and data provided match.")

ValueError: Multiple inputs but only one data feeded. Please verify number of inputs and data provided match.

[aymericdamien]

You got this error if you are running more than one time this code, because every time, data are added to the default graph, so it has a lot of input data layers, but you only feed a single X, thus the error.

To correct it, if you want to run this code multiple times, you need to specify a graph:

import tflearn
import tensorflow as tf
​
X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]
Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]
​
graph_to_use = tf.Graph()
with graph_to_use.as_default():
  input_layer = tflearn.input_data(shape=[None])
  linear_layer = tflearn.single_unit(input_layer)
  regression_layer = tflearn.regression(linear_layer, optimizer="sgd", loss="mean_square",
                                                              metric="R2", learning_rate=.01)
  m = tflearn.DNN(regression_layer)
  m.fit(X, Y, n_epoch=1000, show_metric=False, snapshot_epoch=False)