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Generalized code + Saving options #8

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20 changes: 15 additions & 5 deletions Omniglot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,12 +10,18 @@
from MANN.Utils.Metrics import accuracy_instance
from MANN.Utils.tf_utils import update_tensor

def omniglot():
def omniglot(load_model=False):

sess = tf.InteractiveSession()
saver = tf.train.Saver()

input_ph = tf.placeholder(dtype=tf.float32, shape=(16,50,400)) #(batch_size, time, input_dim)
target_ph = tf.placeholder(dtype=tf.int32, shape=(16,50)) #(batch_size, time)(label_indices)
if(load_model):
ckpt = tf.train.get_checkpoint_state('./saved/')
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("No Checkpoint found, setting load to false")
load_model = False

##Global variables for Omniglot Problem
nb_reads = 4
Expand All @@ -26,6 +32,9 @@ def omniglot():
batch_size = 16
nb_samples_per_class = 10

input_ph = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_class * nb_samples_per_class, input_size)) #(batch_size, time, input_dim)
target_ph = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_class * nb_samples_per_class)) #(batch_size, time)(label_indices)

#Load Data
generator = OmniglotGenerator(data_folder='./data/omniglot', batch_size=batch_size, nb_samples=nb_class, nb_samples_per_class=nb_samples_per_class, max_rotation=0., max_shift=0., max_iter=None)
output_var, output_var_flatten, params = memory_augmented_neural_network(input_ph, target_ph, batch_size=batch_size, nb_class=nb_class, memory_shape=memory_shape, controller_size=controller_size, input_size=input_size, nb_reads=nb_reads)
Expand Down Expand Up @@ -74,8 +83,8 @@ def omniglot():
t0 = time.time()
all_scores, scores, accs = [],[],np.zeros(generator.nb_samples_per_class)


sess.run(tf.global_variables_initializer())
if(!load_model):
sess.run(tf.global_variables_initializer())

print 'Training the model'

Expand All @@ -102,6 +111,7 @@ def omniglot():
print(accs / 100.0)
print('Episode %05d: %.6f' % (i, np.mean(score)))
scores, accs = [], np.zeros(generator.nb_samples_per_class)
saver.save(sess, './saved/model.ckpt', global_step=i+1)


except KeyboardInterrupt:
Expand Down