How to print out the prediction probabilities in Tensorflow
1
I am new to tf. I have trained an encoder - decoder using tensorflow. The program takes as input a word and prints out its phonemes.
For example: Hello World -> ['h', 'E', 'l', '"', '@U', ' ', 'w', '"', '3`', 'r', '5', 'd']
I would like to have access to the prediction probability of each phoneme chosen.
In the prediction section, the code I am using is the following:
def predict(words, sess):
if len(words) > hp.batch_size:
after = predict(words[hp.batch_size:], sess)
words = words[:hp.batch_size]
else:
after =
x = np.zeros((len(words), hp.maxlen), np.int32) # 0: <PAD>
for i, w in enumerate(words):
for j, g in enumerate((w + "E")[:hp.maxlen]):
x[i][j] = g2idx.get(g, 2)
preds = np.zeros((len(x), hp.maxlen), np.int32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
preds[:, j] = xpreds[:, j]
Thank you in advance!
My main problem is where these probabilities are "hidden" and how to access them. For example, the letter "o" in the word "Hello" was mapped with the phoneme "@U". I would like to find out with what probability "@U" was chosen as the ideal phoneme.
python tensorflow
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
|
show 6 more comments
1
I am new to tf. I have trained an encoder - decoder using tensorflow. The program takes as input a word and prints out its phonemes.
For example: Hello World -> ['h', 'E', 'l', '"', '@U', ' ', 'w', '"', '3`', 'r', '5', 'd']
I would like to have access to the prediction probability of each phoneme chosen.
In the prediction section, the code I am using is the following:
def predict(words, sess):
if len(words) > hp.batch_size:
after = predict(words[hp.batch_size:], sess)
words = words[:hp.batch_size]
else:
after =
x = np.zeros((len(words), hp.maxlen), np.int32) # 0: <PAD>
for i, w in enumerate(words):
for j, g in enumerate((w + "E")[:hp.maxlen]):
x[i][j] = g2idx.get(g, 2)
preds = np.zeros((len(x), hp.maxlen), np.int32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
preds[:, j] = xpreds[:, j]
Thank you in advance!
My main problem is where these probabilities are "hidden" and how to access them. For example, the letter "o" in the word "Hello" was mapped with the phoneme "@U". I would like to find out with what probability "@U" was chosen as the ideal phoneme.
python tensorflow
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
Can you format the code correctly? Not sure if everything in the code block is supposed to be within thepredictfunction. What have you tried doing to print out the probabilities? Trying to determine if this is a duplicate question, or if there's more to it
– Ian Quah
Nov 21 '18 at 16:13
Thank you for your answer. Actually, I have tried some things found online. I have tried the np.argmax, tf.argmax, tf.nn.top_k and other commands. A part of the problem is that even if the above commands produce something, there is a problem of accessing and reading the data. Mostly because they are tensors
– Konstantinos Markopoulos
Nov 21 '18 at 16:27
Look attf.Printorevalstackoverflow questions with answers
– Ian Quah
Nov 21 '18 at 16:32
Possible duplicate of How to print the value of a Tensor object in TensorFlow?
– Ian Quah
Nov 21 '18 at 16:32
1
The code that i use is based on: github.com/Kyubyong/g2p The things that I have changed are mostly on preprocessing, in order to use my own data. But the heart remains the same. I used train.py in order to train my model and I am using g2p.py to make predictions. The point of interest is on line 85 of g2p.py. I have to find a way to utilize the information in " _preds = sess.run(graph.preds, {graph.x: x, graph.y: preds}) " to not only make predictions but print the probability of each prediction
– Konstantinos Markopoulos
Nov 21 '18 at 19:09
|
show 6 more comments
1
1
1
I am new to tf. I have trained an encoder - decoder using tensorflow. The program takes as input a word and prints out its phonemes.
For example: Hello World -> ['h', 'E', 'l', '"', '@U', ' ', 'w', '"', '3`', 'r', '5', 'd']
I would like to have access to the prediction probability of each phoneme chosen.
In the prediction section, the code I am using is the following:
def predict(words, sess):
if len(words) > hp.batch_size:
after = predict(words[hp.batch_size:], sess)
words = words[:hp.batch_size]
else:
after =
x = np.zeros((len(words), hp.maxlen), np.int32) # 0: <PAD>
for i, w in enumerate(words):
for j, g in enumerate((w + "E")[:hp.maxlen]):
x[i][j] = g2idx.get(g, 2)
preds = np.zeros((len(x), hp.maxlen), np.int32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
preds[:, j] = xpreds[:, j]
Thank you in advance!
My main problem is where these probabilities are "hidden" and how to access them. For example, the letter "o" in the word "Hello" was mapped with the phoneme "@U". I would like to find out with what probability "@U" was chosen as the ideal phoneme.
python tensorflow
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
I am new to tf. I have trained an encoder - decoder using tensorflow. The program takes as input a word and prints out its phonemes.
For example: Hello World -> ['h', 'E', 'l', '"', '@U', ' ', 'w', '"', '3`', 'r', '5', 'd']
I would like to have access to the prediction probability of each phoneme chosen.
In the prediction section, the code I am using is the following:
def predict(words, sess):
if len(words) > hp.batch_size:
after = predict(words[hp.batch_size:], sess)
words = words[:hp.batch_size]
else:
after =
x = np.zeros((len(words), hp.maxlen), np.int32) # 0: <PAD>
for i, w in enumerate(words):
for j, g in enumerate((w + "E")[:hp.maxlen]):
x[i][j] = g2idx.get(g, 2)
preds = np.zeros((len(x), hp.maxlen), np.int32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
preds[:, j] = xpreds[:, j]
Thank you in advance!
My main problem is where these probabilities are "hidden" and how to access them. For example, the letter "o" in the word "Hello" was mapped with the phoneme "@U". I would like to find out with what probability "@U" was chosen as the ideal phoneme.
python tensorflow
python tensorflow
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
edited Nov 21 '18 at 16:44
Konstantinos Markopoulos
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
asked Nov 21 '18 at 16:11
Konstantinos MarkopoulosKonstantinos Markopoulos
185
185
Can you format the code correctly? Not sure if everything in the code block is supposed to be within thepredictfunction. What have you tried doing to print out the probabilities? Trying to determine if this is a duplicate question, or if there's more to it
– Ian Quah
Nov 21 '18 at 16:13
Thank you for your answer. Actually, I have tried some things found online. I have tried the np.argmax, tf.argmax, tf.nn.top_k and other commands. A part of the problem is that even if the above commands produce something, there is a problem of accessing and reading the data. Mostly because they are tensors
– Konstantinos Markopoulos
Nov 21 '18 at 16:27
Look attf.Printorevalstackoverflow questions with answers
– Ian Quah
Nov 21 '18 at 16:32
Possible duplicate of How to print the value of a Tensor object in TensorFlow?
– Ian Quah
Nov 21 '18 at 16:32
1
The code that i use is based on: github.com/Kyubyong/g2p The things that I have changed are mostly on preprocessing, in order to use my own data. But the heart remains the same. I used train.py in order to train my model and I am using g2p.py to make predictions. The point of interest is on line 85 of g2p.py. I have to find a way to utilize the information in " _preds = sess.run(graph.preds, {graph.x: x, graph.y: preds}) " to not only make predictions but print the probability of each prediction
– Konstantinos Markopoulos
Nov 21 '18 at 19:09
|
show 6 more comments
Can you format the code correctly? Not sure if everything in the code block is supposed to be within thepredictfunction. What have you tried doing to print out the probabilities? Trying to determine if this is a duplicate question, or if there's more to it
– Ian Quah
Nov 21 '18 at 16:13
Thank you for your answer. Actually, I have tried some things found online. I have tried the np.argmax, tf.argmax, tf.nn.top_k and other commands. A part of the problem is that even if the above commands produce something, there is a problem of accessing and reading the data. Mostly because they are tensors
– Konstantinos Markopoulos
Nov 21 '18 at 16:27
Look attf.Printorevalstackoverflow questions with answers
– Ian Quah
Nov 21 '18 at 16:32
Possible duplicate of How to print the value of a Tensor object in TensorFlow?
– Ian Quah
Nov 21 '18 at 16:32
1
The code that i use is based on: github.com/Kyubyong/g2p The things that I have changed are mostly on preprocessing, in order to use my own data. But the heart remains the same. I used train.py in order to train my model and I am using g2p.py to make predictions. The point of interest is on line 85 of g2p.py. I have to find a way to utilize the information in " _preds = sess.run(graph.preds, {graph.x: x, graph.y: preds}) " to not only make predictions but print the probability of each prediction
– Konstantinos Markopoulos
Nov 21 '18 at 19:09
Can you format the code correctly? Not sure if everything in the code block is supposed to be within the
predict function. What have you tried doing to print out the probabilities? Trying to determine if this is a duplicate question, or if there's more to it– Ian Quah
Nov 21 '18 at 16:13
Can you format the code correctly? Not sure if everything in the code block is supposed to be within the
predict function. What have you tried doing to print out the probabilities? Trying to determine if this is a duplicate question, or if there's more to it– Ian Quah
Nov 21 '18 at 16:13
Thank you for your answer. Actually, I have tried some things found online. I have tried the np.argmax, tf.argmax, tf.nn.top_k and other commands. A part of the problem is that even if the above commands produce something, there is a problem of accessing and reading the data. Mostly because they are tensors
– Konstantinos Markopoulos
Nov 21 '18 at 16:27
Thank you for your answer. Actually, I have tried some things found online. I have tried the np.argmax, tf.argmax, tf.nn.top_k and other commands. A part of the problem is that even if the above commands produce something, there is a problem of accessing and reading the data. Mostly because they are tensors
– Konstantinos Markopoulos
Nov 21 '18 at 16:27
Look at
tf.Print or eval stackoverflow questions with answers– Ian Quah
Nov 21 '18 at 16:32
Look at
tf.Print or eval stackoverflow questions with answers– Ian Quah
Nov 21 '18 at 16:32
Possible duplicate of How to print the value of a Tensor object in TensorFlow?
– Ian Quah
Nov 21 '18 at 16:32
Possible duplicate of How to print the value of a Tensor object in TensorFlow?
– Ian Quah
Nov 21 '18 at 16:32
1
1
The code that i use is based on: github.com/Kyubyong/g2p The things that I have changed are mostly on preprocessing, in order to use my own data. But the heart remains the same. I used train.py in order to train my model and I am using g2p.py to make predictions. The point of interest is on line 85 of g2p.py. I have to find a way to utilize the information in " _preds = sess.run(graph.preds, {graph.x: x, graph.y: preds}) " to not only make predictions but print the probability of each prediction
– Konstantinos Markopoulos
Nov 21 '18 at 19:09
The code that i use is based on: github.com/Kyubyong/g2p The things that I have changed are mostly on preprocessing, in order to use my own data. But the heart remains the same. I used train.py in order to train my model and I am using g2p.py to make predictions. The point of interest is on line 85 of g2p.py. I have to find a way to utilize the information in " _preds = sess.run(graph.preds, {graph.x: x, graph.y: preds}) " to not only make predictions but print the probability of each prediction
– Konstantinos Markopoulos
Nov 21 '18 at 19:09
|
show 6 more comments
1 Answer
1
active
oldest
votes
0
Following the discussion, I think I can point you to where the code should be changed.
In train.py, line 104:
self.preds = tf.to_int32(tf.argmax(logits, -1))
They assign the preds variable to the index with highest probability.
In order to get the softmax predictions, you can change the code as follows:
self.preds = tf.nn.softmax(logits)
I think that should do it.
How to view the probabilities:
preds = np.zeros((len(x), hp.maxlen), np.float32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
# print shape of output -> batch_size, max_length,number_of_output_options
print(xpreds.shape)
# print all predictions of the first output
print(xpreds[0, 0])
# print the probabilty of the network prediction
print(xpreds[0, 0, np.argmax(xpreds[0][0])])
# preds[:, j] = _preds[:, j] Need to accumulate the results according to the correct output shape
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
|
show 10 more comments
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Following the discussion, I think I can point you to where the code should be changed.
In train.py, line 104:
self.preds = tf.to_int32(tf.argmax(logits, -1))
They assign the preds variable to the index with highest probability.
In order to get the softmax predictions, you can change the code as follows:
self.preds = tf.nn.softmax(logits)
I think that should do it.
How to view the probabilities:
preds = np.zeros((len(x), hp.maxlen), np.float32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
# print shape of output -> batch_size, max_length,number_of_output_options
print(xpreds.shape)
# print all predictions of the first output
print(xpreds[0, 0])
# print the probabilty of the network prediction
print(xpreds[0, 0, np.argmax(xpreds[0][0])])
# preds[:, j] = _preds[:, j] Need to accumulate the results according to the correct output shape
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
|
show 10 more comments
0
Following the discussion, I think I can point you to where the code should be changed.
In train.py, line 104:
self.preds = tf.to_int32(tf.argmax(logits, -1))
They assign the preds variable to the index with highest probability.
In order to get the softmax predictions, you can change the code as follows:
self.preds = tf.nn.softmax(logits)
I think that should do it.
How to view the probabilities:
preds = np.zeros((len(x), hp.maxlen), np.float32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
# print shape of output -> batch_size, max_length,number_of_output_options
print(xpreds.shape)
# print all predictions of the first output
print(xpreds[0, 0])
# print the probabilty of the network prediction
print(xpreds[0, 0, np.argmax(xpreds[0][0])])
# preds[:, j] = _preds[:, j] Need to accumulate the results according to the correct output shape
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
|
show 10 more comments
0
0
0
Following the discussion, I think I can point you to where the code should be changed.
In train.py, line 104:
self.preds = tf.to_int32(tf.argmax(logits, -1))
They assign the preds variable to the index with highest probability.
In order to get the softmax predictions, you can change the code as follows:
self.preds = tf.nn.softmax(logits)
I think that should do it.
How to view the probabilities:
preds = np.zeros((len(x), hp.maxlen), np.float32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
# print shape of output -> batch_size, max_length,number_of_output_options
print(xpreds.shape)
# print all predictions of the first output
print(xpreds[0, 0])
# print the probabilty of the network prediction
print(xpreds[0, 0, np.argmax(xpreds[0][0])])
# preds[:, j] = _preds[:, j] Need to accumulate the results according to the correct output shape
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
Following the discussion, I think I can point you to where the code should be changed.
In train.py, line 104:
self.preds = tf.to_int32(tf.argmax(logits, -1))
They assign the preds variable to the index with highest probability.
In order to get the softmax predictions, you can change the code as follows:
self.preds = tf.nn.softmax(logits)
I think that should do it.
How to view the probabilities:
preds = np.zeros((len(x), hp.maxlen), np.float32)
for j in range(hp.maxlen):
xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds})
# print shape of output -> batch_size, max_length,number_of_output_options
print(xpreds.shape)
# print all predictions of the first output
print(xpreds[0, 0])
# print the probabilty of the network prediction
print(xpreds[0, 0, np.argmax(xpreds[0][0])])
# preds[:, j] = _preds[:, j] Need to accumulate the results according to the correct output shape
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
edited Nov 22 '18 at 14:53
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
answered Nov 21 '18 at 21:15
Ohad MeirOhad Meir
335111
335111
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
|
show 10 more comments
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
thank you for your answer. I changed the code as you proposed and i am now retraining my model. any idea on how to access the probabilities when i use the g2p.py script, in order to find them on every data i use as input? thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 8:48
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
After you make this change, after you run xpreds = sess.run(graph.preds, {graph.x: x, graph.y: preds}), the xpreds will contain the probabilities. Print to the console and check.
– Ohad Meir
Nov 22 '18 at 13:11
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
By the way, you don't need to retrain. you can just use the latest checkpoint that you already have
– Ohad Meir
Nov 22 '18 at 13:30
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
Thank you very much. One last question (i hope the last). I replaced "tf.to_int32" with "tf.nn.softmax" as you proposed above in the train.py. After that i run g2p.py and a ValueError occured: ValueError: could not broadcast input array from shape (97) into shape (1). Should i use an other manipulation in order to make my script run correctly? Thank you in advance
– Konstantinos Markopoulos
Nov 22 '18 at 14:21
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
The error is here: preds[:, j] = xpreds[:, j] First you can do: print(xpreds.shape) right after session.run to see the shape that you now get. you can also print(xpreds[0]) to see that you now get probabilties. I'll edit the answer
– Ohad Meir
Nov 22 '18 at 14:51
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Can you format the code correctly? Not sure if everything in the code block is supposed to be within the
predictfunction. What have you tried doing to print out the probabilities? Trying to determine if this is a duplicate question, or if there's more to it– Ian Quah
Nov 21 '18 at 16:13
Thank you for your answer. Actually, I have tried some things found online. I have tried the np.argmax, tf.argmax, tf.nn.top_k and other commands. A part of the problem is that even if the above commands produce something, there is a problem of accessing and reading the data. Mostly because they are tensors
– Konstantinos Markopoulos
Nov 21 '18 at 16:27
Look at
tf.Printorevalstackoverflow questions with answers– Ian Quah
Nov 21 '18 at 16:32
Possible duplicate of How to print the value of a Tensor object in TensorFlow?
– Ian Quah
Nov 21 '18 at 16:32
1
The code that i use is based on: github.com/Kyubyong/g2p The things that I have changed are mostly on preprocessing, in order to use my own data. But the heart remains the same. I used train.py in order to train my model and I am using g2p.py to make predictions. The point of interest is on line 85 of g2p.py. I have to find a way to utilize the information in " _preds = sess.run(graph.preds, {graph.x: x, graph.y: preds}) " to not only make predictions but print the probability of each prediction
– Konstantinos Markopoulos
Nov 21 '18 at 19:09