def showPicture(test_length, path):
if not os.path.exists(path):
os.makedirs(path)
data_gen.init_data(task, test_length, 1, n_input)
while len(data_gen.train_set[task][test_length])==0:
test_length += 1
data_gen.init_data(task, test_length, 1, n_input)
data_gen.resetCounters()
with tf.Graph().as_default(),tf.device('/cpu:0'):
tester = DNGPU(n_hidden, [test_length], n_input, [1], n_output, dropout_keep_prob)
tester.createTestGraph(test_length)
saver = tf.train.Saver(tf.trainable_variables())
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver.restore(sess, model_file)
if not os.path.exists(path): os.makedirs(path)
batch_xs, batch_ys = genTestData(test_length, 1)
print batch_xs, batch_ys
mem = tester.getAllMem(sess, batch_xs, batch_ys)
mem = np.squeeze(mem, 1)
width = mem.shape[1]
height = mem.shape[0]
for unit in range(n_hidden):
img=np.zeros((height,width),dtype=np.float32)
for x in range(width):
for y in range(height):
img[y,x]=mem[y, x,unit]
mpimg.imsave(path+"/frame"+str(unit)+".png",img, cmap='gray')
# create execution trace images
showPicture(200, image_path)
# test generalization to longer examples
test_length = 7
test_examples = 1024
while test_length < max_test_length:
if test_length > 4001: test_length = 4001
if len(data_gen.test_set[task][test_length]) == 0:
data_gen.init_data(task, test_length, test_data_size, n_input)
while len(data_gen.test_set[task][test_length]) == 0:
test_length += 1
data_gen.init_data(task, test_length, test_data_size, n_input)
data_gen.resetCounters()
batchSize = 1
if test_length < 2000: batchSize = 16
if test_length < 800: batchSize = 128
with tf.Graph().as_default(): #, tf.device('/cpu:0'):
tester = DNGPU(n_hidden, [test_length], n_input, [batchSize], n_output,
dropout_keep_prob)
tester.createTestGraph(test_length)
saver = tf.train.Saver(tf.trainable_variables())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, model_file)
errors, seq_errors, total = 0, 0, 0
for iter in range(test_examples // batchSize):