displayname - 6 months ago 30

Python Question

I am trying to implement a simple feed forward network. However, I can't figure out how to feed a

`Placeholder`

`import tensorflow as tf`

num_input = 2

num_hidden = 3

num_output = 2

x = tf.placeholder("float", [num_input, 1])

W_hidden = tf.Variable(tf.zeros([num_hidden, num_input]))

W_out = tf.Variable(tf.zeros([num_output, num_hidden]))

b_hidden = tf.Variable(tf.zeros([num_hidden]))

b_out = tf.Variable(tf.zeros([num_output]))

h = tf.nn.softmax(tf.matmul(W_hidden,x) + b_hidden)

sess = tf.Session()

with sess.as_default():

print h.eval()

Gives me the following error:

`...`

results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 419, in _do_run

e.code)

tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape dim { size: 2 } dim { size: 1 }

[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[2,1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op u'Placeholder', defined at:

File "/home/sfalk/workspace/SemEval2016/java/semeval2016-python/slot1_tf.py", line 8, in <module>

x = tf.placeholder("float", [num_input, 1])

...

I have tried

`tf.assign([tf.Variable(1.0), tf.Variable(1.0)], x)`

tf.assign([1.0, 1.0], x)

but that does not work apparently.

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Answer Source

To feed a placeholder, you use the `feed_dict`

argument to `Session.run()`

(or `Tensor.eval()`

). Let's say you have the following graph, with a placeholder:

```
x = tf.placeholder(tf.float32, shape=[2, 2])
y = tf.constant([[1.0, 1.0], [0.0, 1.0]])
z = tf.matmul(x, y)
```

If you want to evaluate `z`

, you must feed a value for `x`

. You can do this as follows:

```
sess = tf.Session()
print sess.run(z, feed_dict={x: [[3.0, 4.0], [5.0, 6.0]]})
```

For more information, see the documentation on feeding.

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