Module Expr Str ImportFrom alias alias alias Import alias identifier Import alias Import alias identifier Assign TupleStore NameStore NameStore NameStore NameStore Call AttributeLoad NameLoad attr keyword NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad TupleLoad Num Num Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad TupleLoad Num Num Num Assign NameStore Call AttributeLoad NameLoad attr keyword ListLoad NameLoad Num Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad Num keyword NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad Num keyword Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword Str keyword Str keyword Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword Num Expr Call AttributeLoad NameLoad attr NameLoad NameLoad keyword Num keyword Num keyword NameLoad keyword Num
N/A N/A 
MNIST Classification using RNN over images pixels. A picture is
representated as a sequence of pixels, coresponding to an image's
width (timestep) and height (number of sequences).
 __future__ division print_function absolute_import N/A numpy np N/A tflearn N/A tflearn.datasets.mnist mnist N/A N/A X Y testX testY N/A N/A mnist load_data one_hot True N/A X N/A N/A np reshape X N/A -1 28 28 N/A testX N/A N/A np reshape testX N/A -1 28 28 N/A net N/A N/A tflearn input_data shape N/A None 28 28 N/A net N/A N/A tflearn lstm net 128 return_seq True N/A net N/A N/A tflearn lstm net 128 N/A net N/A N/A tflearn fully_connected net 10 activation softmax N/A net N/A N/A tflearn regression net optimizer adam loss categorical_crossentropy name output1 N/A model N/A N/A tflearn DNN net tensorboard_verbose 2 N/A N/A N/A model fit X Y n_epoch 1 validation_set 0.1 show_metric True snapshot_step 100