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')
# n_output = 6
# n_hidden = 48 # number of maps
model_file = "/tmp/varWeights.ckpt"
image_path = "/tmp/images"
countList = [batchSize for x in data_gen.bins]
np.set_printoptions(linewidth=2000)
# seed = 125459
# tf.set_random_seed(seed)
# random.seed(seed)
# np.random.seed(seed)
#prepare training and test data
max_length = data_gen.bins[-1]
for l in range(1, max_length + 1):
data_gen.init_data(task, l, data_size, n_input)
data_gen.collectBins()
data_gen.init_data(task, data_gen.forward_max, test_data_size, n_input)
# generates training data
def genTrainingData(forTraining):
x = []
y = []
for i in range(len(data_gen.bins)):
seq_len = data_gen.bins[i]
seq_count = countList[i]
data, labels = data_gen.get_batch(seq_len, seq_count, forTraining,
task)
x += [data]
y += [labels]