I'm starting using Dataset API to replace the feed_dict system.
However, after creating your Dataset pipeline, how can you feed the Dataset's data to the model without using feed_dict ?
First, I created a one shot iterator. But in this case, you need to use feed_dict to provide the data coming from your iterator to the model.
Secondly, I tried to create my dataset directly from a tf.placeholder and then use a initializable_iterator. But here again, I don't understand how to get rid of feed_dict. In addition, I don't understand what's the purpose of this kind of dataset based on plaeholders.
My very basic model:
x = tf.placeholder(tf.float32, [None, 2])dense = tf.layers.dense(x, 1)init_dense = tf.global_variables_initializer()My data:
np_data = np.random.sample((100,2))Method 1:
dataset = tf.data.Dataset.from_tensor_slices(np_data)iterator = dataset.make_one_shot_iterator()next_value = iterator.get_next()with tf.Session() as sess: sess.run(init_glob) for i in range(100): value = sess.run(next_value) # Cannot get rid of feed_dict result = sess.run(dense, feed_dict({x: value})Method 2:
dataset = tf.data.Dataset.from_tensor_slices(x)iterator = dataset.make_initializable_iterator()next_value = iterator.get_next()with tf.Session() as sess: sess.run(init_glob) sess.run(iterator.initializer, feed_dict={x: np_data}) for i in range(100): value = sess.run(next_value) # Cannot get rid of feed_dict result = sess.run(dense, feed_dict({x: value})https://www.tensorflow.org/guide/performance/overview#input_pipeline
So, how can I "Avoid using feed_dict for all but trivial examples" ?I think I didn't understand the concept of Dataset API
