Module Expr Str ImportFrom alias alias alias Import alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias Assign TupleStore TupleStore NameStore NameStore TupleStore NameStore NameStore Call AttributeLoad NameLoad attr Assign NameStore Call NameLoad NameLoad Num Assign NameStore Call NameLoad NameLoad Num Import alias Assign NameStore Call AttributeLoad NameLoad attr Str Str Expr Call AttributeLoad NameLoad attr Str keyword NameLoad Expr Call AttributeLoad NameLoad attr Str keyword NameLoad Expr Call AttributeLoad NameLoad attr Str keyword NameLoad Expr Call AttributeLoad NameLoad attr Str keyword NameLoad Expr Call AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr Str Str Assign NameStore SubscriptLoad NameLoad Index Str Assign NameStore SubscriptLoad NameLoad Index Str Assign NameStore SubscriptLoad NameLoad Index Str Assign NameStore SubscriptLoad NameLoad Index Str Assign NameStore Call NameLoad keyword ListLoad NameLoad Num Num Num keyword AttributeLoad NameLoad attr Assign NameStore Call NameLoad NameLoad Num Num keyword Str Assign NameStore Call NameLoad NameLoad Num Assign NameStore Call NameLoad NameLoad Num Num keyword Str Assign NameStore Call NameLoad NameLoad Num Num keyword Str Assign NameStore Call NameLoad NameLoad Num Assign NameStore Call NameLoad NameLoad Num keyword Str Assign NameStore Call NameLoad NameLoad Num Assign NameStore Call NameLoad NameLoad Num keyword Str Assign NameStore Call NameLoad NameLoad keyword Str keyword Str keyword Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword Num Expr Call AttributeLoad NameLoad attr NameLoad NameLoad keyword Num keyword NameLoad keyword TupleLoad NameLoad NameLoad keyword NameLoad keyword Num keyword Str Expr Call AttributeLoad NameLoad attr
N/A N/A 
Example on how to use HDF5 dataset with TFLearn. HDF5 is a data model,
library, and file format for storing and managing data. It can handle large
dataset that could not fit totally in ram memory. Note that this example
just give a quick compatibility demonstration. In practice, there is no so
real need to use HDF5 for small dataset such as CIFAR-10.
 __future__ division print_function absolute_import N/A tflearn tflearn.layers.core * tflearn.layers.conv * tflearn.data_utils * tflearn.layers.normalization * tflearn.layers.estimator regression tflearn.datasets cifar10 N/A N/A N/A X Y N/A X_test Y_test N/A N/A cifar10 load_data N/A Y N/A to_categorical Y 10 N/A Y_test N/A to_categorical Y_test 10 N/A h5py N/A h5f N/A N/A h5py File data.h5 w N/A N/A N/A h5f create_dataset cifar10_X data X N/A N/A N/A h5f create_dataset cifar10_Y data Y N/A N/A N/A h5f create_dataset cifar10_X_test data X_test N/A N/A N/A h5f create_dataset cifar10_Y_test data Y_test N/A N/A N/A h5f close N/A h5f N/A N/A h5py File data.h5 r N/A X N/A h5f N/A cifar10_X N/A Y N/A h5f N/A cifar10_Y N/A X_test N/A h5f N/A cifar10_X_test N/A Y_test N/A h5f N/A cifar10_Y_test N/A network N/A input_data shape N/A None 32 32 3 dtype N/A tf float32 N/A network N/A conv_2d network 32 3 activation relu N/A network N/A max_pool_2d network 2 N/A network N/A conv_2d network 64 3 activation relu N/A network N/A conv_2d network 64 3 activation relu N/A network N/A max_pool_2d network 2 N/A network N/A fully_connected network 512 activation relu N/A network N/A dropout network 0.5 N/A network N/A fully_connected network 10 activation softmax N/A network N/A regression network optimizer adam loss categorical_crossentropy learning_rate 0.001 N/A model N/A N/A tflearn DNN network tensorboard_verbose 0 N/A N/A N/A model fit X Y n_epoch 50 shuffle True validation_set N/A X_test Y_test show_metric True batch_size 96 run_id cifar10_cnn N/A N/A N/A h5f close