将张量输入张量流图

Question

I am following tutorial on simple audio recognition and currently editing the label_wav.py . In original case we input wave file and the graph predicts the label (in between it calculates spectrum, mfcc's inside the graph). Now i am looking to input mfcc's directly rather than inputting the wave file. Run the graph by inputting the mfcc tensor.

# mfccs:  Tensor("strided_slice:0", shape=(1, 98, 40), dtype=float32)
mfcc_input_layer_name = 'Reshape:0'
with tf.Session() as sess:
    predictions, = sess.run(softmax_tensor, {mfcc_input_layer_name: mfcc})

After a bit of googling, i found some discussion in git and created a session_handle .

# mfccs:  Tensor("strided_slice:0", shape=(1, 98, 40), dtype=float32)
mfcc_input_layer_name = 'Reshape:0'
with tf.Session() as sess:
      h = tf.get_session_handle(mfccs)
      h = sess.run(h)
      predictions, = sess.run(softmax_tensor, {mfcc_input_layer_name: h})

The code is working as expected but I am wondering if there could be a better way of dealing with the tensor rather than creating the handle and then passing it?

Answer 1

I suppose you want to replace an intermediate Tensor with a value by feed_dict. If you have a Tensor object, you can replace it by feed_dict as the following

a = tf.constant(3, name="a")
b = tf.constant(4, name="b")
c = tf.add(a, b, name="c")
d = c * 3

with tf.Session() as sess:
    print(sess.run(d))    
    print(sess.run(d, feed_dict={c: 2}))

Even though you don't have the Tensor object, you can get it by get_tensor_by_name

a = tf.constant(3, name="a")
b = tf.constant(4, name="b")
c = tf.add(a, b, name="c")
d = c * 3

with tf.Session() as sess:
    c_tensor = tf.get_default_graph().get_tensor_by_name("c:0")
    print(sess.run(d, feed_dict={c_tensor: 2}))