问题
I put two Dataset pipeline for train/test = 9:1 set in my Graph and the control the flow by a tf.cond. I encountered a problem that during the training the both pipelines are activated at each step. The testset ran out before the trainset as it has less during training.
OutOfRangeError (see above for traceback): End of sequence
First, nest the input pipeline in a function:
def input_pipeline(*args):
...
# construct iterator
it = batch.make_initializable_iterator()
iter_init_op = it.initializer
# get next img and label
X_it, y_it = it.get_next()
inputs = {'img': X_it, 'label': y_it, 'iterator_init_op': iter_init_op}
return inputs
Then initiate:
train_input = input_pipeline(args)
test_input = input_pipeline(args)
In the model, we put a placeholder to fill condition with feed_dict which won't penalize the performance:
...
def f1(): return train_input
def f2(): return test_input
cond_pl = tf.placeholder(tf.string, name='cond_pl')
input = tf.cond(tf.equal(cond_pl, 'train'), lambda: f1(), lambda: f2())
...
Within the session:
for ep in range(nb_ep):
...
for step in range(ep_len):
print('step:{}\r'.format(step))
try:
sess.run([train_op], feed_dict={cond_pl: 'train'})
if step % step_len == (step_len - 1):
sess.run([test_op], feed_dict={cond_pl: 'test'})
except tf.errors.OutOfRangeError:
raise('drop the remainder')
...
How can I let the input pipeline get_next() get called only if the condition fits?
Snippet to play around updated based on @sharky answer:
def write_h5(args):
x, is_training = args
with h5py.File('./{}_{}.h5'.format('train' if is_training else 'test', x), 'w') as f:
h = w = np.arange(-1, 1, 0.02)
hh, _ = np.meshgrid(h, w)
a = hh ** 2
b = np.add(a + 1, np.random.randn(100, 100)) #do something and add gaussian noise
f.create_dataset('X', shape=(100, 100), dtype='float32', data=a)
f.create_dataset('y', shape=(100, 100), dtype='float32', data=b)
def input_pipeline(window_size, batch_size, is_train=True, ncores=mp.cpu_count()):
flist = []
for dirpath, _, fnames in os.walk('./'):
for fname in fnames:
if fname.startswith('train' if is_train else 'test') and fname.endswith('.h5'):
print(fname)
flist.append((os.path.abspath(os.path.join(dirpath, fname)), str(window_size)))
f_len = len(flist)
print(f_len)
# init list of files
batch = tf.data.Dataset.from_tensor_slices((tf.constant(flist)))
batch = batch.map(_pyfn_wrapper, num_parallel_calls=ncores)
batch = batch.shuffle(batch_size).batch(batch_size, drop_remainder=True).prefetch(ncores).repeat()
# construct iterator
it = batch.make_initializable_iterator()
iter_init_op = it.initializer
# get next img and label
X_it, y_it = it.get_next()
inputs = {'img': X_it, 'label': y_it, 'iterator_init_op': iter_init_op}
return inputs, f_len
def _pyfn_wrapper(args):
return tf.py_func(parse_h5, #wrapped pythonic function
[args],
[tf.float32, tf.float32] #[input, output] dtype
)
def parse_h5(args):
name, window_size = args
window_size = int(window_size.decode('utf-8'))
with h5py.File(name, 'r') as f:
X = f['X'][:].reshape(window_size, window_size, 1)
y = f['y'][:].reshape(window_size, window_size, 1)
return X, y
# init data
p = mp.Pool(mp.cpu_count())
p.map(write_h5, zip(range(9000), repeat(True)))
p.map(write_h5, zip(range(1000), repeat(False)))
# hparam
ep_len = 90
step_len = 9 # run test_op after 9 steps
# create tf.data.Dataset
train_input, train_len = input_pipeline(100, 5, is_train=True)
test_input, test_len = input_pipeline(100, 5, is_train=False)
# draw graph
def f1(): return train_input
def f2(): return test_input
cond_pl = tf.placeholder(tf.string, shape=None, name='cond_pl')
input = tf.cond(tf.equal(cond_pl, 'train'), lambda: f1(), lambda: f2()) # I thou
with tf.name_scope("Conv1"):
W = tf.get_variable("W", shape=[3, 3, 1, 1],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable("b", shape=[1], initializer=tf.contrib.layers.xavier_initializer())
layer1 = tf.nn.conv2d(input['img'], W, strides=[1, 1, 1, 1], padding='SAME') + b
logits = tf.nn.relu(layer1)
loss = tf.reduce_mean(tf.losses.mean_squared_error(labels=input['label'], predictions=logits))
train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)
test_op = print(loss)
#
# session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for ep in range(5):
print('ep:{}'.format(ep))
sess.run(input['iterator_init_op'], feed_dict={cond_pl: 'train'})
sess.run(input['iterator_init_op'], feed_dict={cond_pl: 'test'})
for step in range(ep_len):
print('step:{}\r'.format(step))
try:
sess.run([train_op], feed_dict={cond_pl: 'train'})
if step % step_len == (step_len - 1):
sess.run([test_op], feed_dict={cond_pl: 'test'})
except tf.errors.OutOfRangeError:
raise('drop the remainder')
回答1:
Consider example:
train = np.arange(90)
test = np.arange(10)
train_ds = tf.data.Dataset.from_tensor_slices(train).shuffle(10).batch(10).repeat()
test_ds = tf.data.Dataset.from_tensor_slices(test).shuffle(10).batch(10).repeat()
train_iterator = train_ds.make_initializable_iterator()
test_iterator = test_ds.make_initializable_iterator()
with tf.Session() as sess:
sess.run(train_iterator.initializer)
sess.run(test_iterator.initializer)
for i in range(len(train) + 1):
print(sess.run(train_iterator.get_next()))
if i % 9 == 8:
print(sess.run(test_iterator.get_next()))
Two datasets, two iterators, both initialized at startup. When i exceeds length of datasets, it starts repeating both of them because of repeat(). If it'll be called with num_epochs or not called at all, you'll get end of sequence. If for some reason you need/want to use cond, maybe this answer will help
How to use Tensorflow's tf.cond() with two different Dataset iterators without iterating both?
来源：https://stackoverflow.com/questions/55516484/could-two-tf-data-dataset-coexist-and-controled-by-tf-cond