Why do I need to feed a value for placeholder if my output doesn't depend on it
Say I have following simple neural network. I have narrowed it down.
def Network(in1, in2, is_train):
dense1 = tf.layers.dense(in1, 100, activation=tf.nn.relu)
feed_tensor = tf.cond(is_train > 0, lambda: dense1, lambda: in2)
output = tf.layers.dense(feed_tensor, 3, activation=tf.nn.relu)
return output
First I train the neural network by feeding the values for all 3 tensors.
opt = tf.train.AdamOptimizer(learn_rate).minimize(loss)
sess.run(opt, feed_dict=in1: input1, in2: input2, is_train: 1)
Then I feed feed values only for in2 and is_train and try to observe the output.
out = (output, feed_dict=in2: input2, is_train: 0)
Ideally when is_train = 0 it should assign the value of in2 tensor to the feed_tensor, and there is no requirement to feed a value to in1.
However if I run the above, I get 'You must feed a value for placeholder tensor' error.
Can you explain why this happens and a possible way to get rid of this.
Specifically what I want is to select a tensor to be fed to the next layer depending on whether it's training time or not.
EDIT: Will this problem be resolved by feeding a dummy tensor to in1?
python tensorflow
asked Nov 13 '18 at 18:19
avinavin
284
add a comment |
Say I have following simple neural network. I have narrowed it down.
def Network(in1, in2, is_train):
dense1 = tf.layers.dense(in1, 100, activation=tf.nn.relu)
feed_tensor = tf.cond(is_train > 0, lambda: dense1, lambda: in2)
output = tf.layers.dense(feed_tensor, 3, activation=tf.nn.relu)
return output
First I train the neural network by feeding the values for all 3 tensors.
opt = tf.train.AdamOptimizer(learn_rate).minimize(loss)
sess.run(opt, feed_dict=in1: input1, in2: input2, is_train: 1)
Then I feed feed values only for in2 and is_train and try to observe the output.
out = (output, feed_dict=in2: input2, is_train: 0)
Ideally when is_train = 0 it should assign the value of in2 tensor to the feed_tensor, and there is no requirement to feed a value to in1.
However if I run the above, I get 'You must feed a value for placeholder tensor' error.
Can you explain why this happens and a possible way to get rid of this.
Specifically what I want is to select a tensor to be fed to the next layer depending on whether it's training time or not.
EDIT: Will this problem be resolved by feeding a dummy tensor to in1?
python tensorflow
asked Nov 13 '18 at 18:19
avinavin
284
add a comment |
Say I have following simple neural network. I have narrowed it down.
def Network(in1, in2, is_train):
dense1 = tf.layers.dense(in1, 100, activation=tf.nn.relu)
feed_tensor = tf.cond(is_train > 0, lambda: dense1, lambda: in2)
output = tf.layers.dense(feed_tensor, 3, activation=tf.nn.relu)
return output
First I train the neural network by feeding the values for all 3 tensors.
opt = tf.train.AdamOptimizer(learn_rate).minimize(loss)
sess.run(opt, feed_dict=in1: input1, in2: input2, is_train: 1)
Then I feed feed values only for in2 and is_train and try to observe the output.
out = (output, feed_dict=in2: input2, is_train: 0)
Ideally when is_train = 0 it should assign the value of in2 tensor to the feed_tensor, and there is no requirement to feed a value to in1.
However if I run the above, I get 'You must feed a value for placeholder tensor' error.
Can you explain why this happens and a possible way to get rid of this.
Specifically what I want is to select a tensor to be fed to the next layer depending on whether it's training time or not.
EDIT: Will this problem be resolved by feeding a dummy tensor to in1?
python tensorflow
asked Nov 13 '18 at 18:19
avinavin
284
Say I have following simple neural network. I have narrowed it down.
def Network(in1, in2, is_train):
dense1 = tf.layers.dense(in1, 100, activation=tf.nn.relu)
feed_tensor = tf.cond(is_train > 0, lambda: dense1, lambda: in2)
output = tf.layers.dense(feed_tensor, 3, activation=tf.nn.relu)
return output
First I train the neural network by feeding the values for all 3 tensors.
opt = tf.train.AdamOptimizer(learn_rate).minimize(loss)
sess.run(opt, feed_dict=in1: input1, in2: input2, is_train: 1)
Then I feed feed values only for in2 and is_train and try to observe the output.
out = (output, feed_dict=in2: input2, is_train: 0)
Ideally when is_train = 0 it should assign the value of in2 tensor to the feed_tensor, and there is no requirement to feed a value to in1.
However if I run the above, I get 'You must feed a value for placeholder tensor' error.
Can you explain why this happens and a possible way to get rid of this.
Specifically what I want is to select a tensor to be fed to the next layer depending on whether it's training time or not.
EDIT: Will this problem be resolved by feeding a dummy tensor to in1?
python tensorflow
python tensorflow
asked Nov 13 '18 at 18:19
avinavin
284
asked Nov 13 '18 at 18:19
avinavin
284
edited Nov 14 '18 at 11:19
avin
asked Nov 13 '18 at 18:19
avinavin
284
asked Nov 13 '18 at 18:19
avinavin
284
asked Nov 13 '18 at 18:19
avinavin
284
284
add a comment |
add a comment |
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