Keras: input with size x*x generates unwanted output y*x
I have the following neural network in Keras:
inp = layers.Input((3,))
#Middle layers omitted
out_prop = layers.Dense(units=3, activation='softmax')(inp)
out_value = layers.Dense(units=1, activation = 'linear')(inp)
Then I prepared a pseudo-input to test my network:
inpu = np.array([[1,2,3],[4,5,6],[7,8,9]])
When I try to predict, this happens:
In [45]:nn.network.predict(inpu)
Out[45]:
[array([[0.257513 , 0.41672954, 0.32575747],
[0.20175152, 0.4763418 , 0.32190666],
[0.15986516, 0.53449154, 0.30564335]], dtype=float32),
array([[-0.24281949],
[-0.10461146],
[ 0.11201331]], dtype=float32)]
So, as you can see above, I wanted two output: one should have been an array with size 3, the other should have been a normal value. Instead, I get a 3x3 matrix, and an array with 3 elements. What am I doing wrong?
python machine-learning keras neural-network keras-layer
edited Nov 21 '18 at 12:22
today
11.3k22239
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
add a comment |
I have the following neural network in Keras:
inp = layers.Input((3,))
#Middle layers omitted
out_prop = layers.Dense(units=3, activation='softmax')(inp)
out_value = layers.Dense(units=1, activation = 'linear')(inp)
Then I prepared a pseudo-input to test my network:
inpu = np.array([[1,2,3],[4,5,6],[7,8,9]])
When I try to predict, this happens:
In [45]:nn.network.predict(inpu)
Out[45]:
[array([[0.257513 , 0.41672954, 0.32575747],
[0.20175152, 0.4763418 , 0.32190666],
[0.15986516, 0.53449154, 0.30564335]], dtype=float32),
array([[-0.24281949],
[-0.10461146],
[ 0.11201331]], dtype=float32)]
So, as you can see above, I wanted two output: one should have been an array with size 3, the other should have been a normal value. Instead, I get a 3x3 matrix, and an array with 3 elements. What am I doing wrong?
python machine-learning keras neural-network keras-layer
edited Nov 21 '18 at 12:22
today
11.3k22239
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
add a comment |
I have the following neural network in Keras:
inp = layers.Input((3,))
#Middle layers omitted
out_prop = layers.Dense(units=3, activation='softmax')(inp)
out_value = layers.Dense(units=1, activation = 'linear')(inp)
Then I prepared a pseudo-input to test my network:
inpu = np.array([[1,2,3],[4,5,6],[7,8,9]])
When I try to predict, this happens:
In [45]:nn.network.predict(inpu)
Out[45]:
[array([[0.257513 , 0.41672954, 0.32575747],
[0.20175152, 0.4763418 , 0.32190666],
[0.15986516, 0.53449154, 0.30564335]], dtype=float32),
array([[-0.24281949],
[-0.10461146],
[ 0.11201331]], dtype=float32)]
So, as you can see above, I wanted two output: one should have been an array with size 3, the other should have been a normal value. Instead, I get a 3x3 matrix, and an array with 3 elements. What am I doing wrong?
python machine-learning keras neural-network keras-layer
edited Nov 21 '18 at 12:22
today
11.3k22239
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
I have the following neural network in Keras:
inp = layers.Input((3,))
#Middle layers omitted
out_prop = layers.Dense(units=3, activation='softmax')(inp)
out_value = layers.Dense(units=1, activation = 'linear')(inp)
Then I prepared a pseudo-input to test my network:
inpu = np.array([[1,2,3],[4,5,6],[7,8,9]])
When I try to predict, this happens:
In [45]:nn.network.predict(inpu)
Out[45]:
[array([[0.257513 , 0.41672954, 0.32575747],
[0.20175152, 0.4763418 , 0.32190666],
[0.15986516, 0.53449154, 0.30564335]], dtype=float32),
array([[-0.24281949],
[-0.10461146],
[ 0.11201331]], dtype=float32)]
So, as you can see above, I wanted two output: one should have been an array with size 3, the other should have been a normal value. Instead, I get a 3x3 matrix, and an array with 3 elements. What am I doing wrong?
python machine-learning keras neural-network keras-layer
python machine-learning keras neural-network keras-layer
edited Nov 21 '18 at 12:22
today
11.3k22239
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
edited Nov 21 '18 at 12:22
today
11.3k22239
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
edited Nov 21 '18 at 12:22
today
11.3k22239
edited Nov 21 '18 at 12:22
today
11.3k22239
edited Nov 21 '18 at 12:22
today
11.3k22239
11.3k22239
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
asked Nov 21 '18 at 11:58
Federico DoratoFederico Dorato
4211
4211
add a comment |
add a comment |
1 Answer
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You are passing three input samples to the network:
>>> inpu.shape
(3,3) # three samples of size 3
And you have two output layers: one of them outputs a vector of size 3 for each sample and the other outputs a vector of size one (i.e. scalar), again for each sample. As a result the output shapes would be (3, 3) and (3, 1).
Update: If you want your network to accept an input sample of shape (3,3) and outputs vectors of size 3 and 1, and you want to only use Dense layers in your network, then you must use a Flatten layer somewhere in the model. One possible option is to use it right after the input layer:
inp = layers.Input((3,3)) # don't forget to set the correct input shape
x = Flatten()(inp)
# pass x to other Dense layers
Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer.
Update 2: As @Mete correctly pointed out in the comments, make sure the input array have a shape of (num_samples, 3, 3) if each input sample has a shape of (3,3).
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
inp = layers.Input((3,3))means every sample is 3x3inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]])(note shape is 1x3x3) means you have 1 sample of 3x3 data
– Mete Han Kahraman
Nov 21 '18 at 12:29
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
|
show 1 more comment
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You are passing three input samples to the network:
>>> inpu.shape
(3,3) # three samples of size 3
And you have two output layers: one of them outputs a vector of size 3 for each sample and the other outputs a vector of size one (i.e. scalar), again for each sample. As a result the output shapes would be (3, 3) and (3, 1).
Update: If you want your network to accept an input sample of shape (3,3) and outputs vectors of size 3 and 1, and you want to only use Dense layers in your network, then you must use a Flatten layer somewhere in the model. One possible option is to use it right after the input layer:
inp = layers.Input((3,3)) # don't forget to set the correct input shape
x = Flatten()(inp)
# pass x to other Dense layers
Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer.
Update 2: As @Mete correctly pointed out in the comments, make sure the input array have a shape of (num_samples, 3, 3) if each input sample has a shape of (3,3).
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
inp = layers.Input((3,3))means every sample is 3x3inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]])(note shape is 1x3x3) means you have 1 sample of 3x3 data
– Mete Han Kahraman
Nov 21 '18 at 12:29
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
|
show 1 more comment
You are passing three input samples to the network:
>>> inpu.shape
(3,3) # three samples of size 3
And you have two output layers: one of them outputs a vector of size 3 for each sample and the other outputs a vector of size one (i.e. scalar), again for each sample. As a result the output shapes would be (3, 3) and (3, 1).
Update: If you want your network to accept an input sample of shape (3,3) and outputs vectors of size 3 and 1, and you want to only use Dense layers in your network, then you must use a Flatten layer somewhere in the model. One possible option is to use it right after the input layer:
inp = layers.Input((3,3)) # don't forget to set the correct input shape
x = Flatten()(inp)
# pass x to other Dense layers
Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer.
Update 2: As @Mete correctly pointed out in the comments, make sure the input array have a shape of (num_samples, 3, 3) if each input sample has a shape of (3,3).
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
inp = layers.Input((3,3))means every sample is 3x3inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]])(note shape is 1x3x3) means you have 1 sample of 3x3 data
– Mete Han Kahraman
Nov 21 '18 at 12:29
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
|
show 1 more comment
You are passing three input samples to the network:
>>> inpu.shape
(3,3) # three samples of size 3
And you have two output layers: one of them outputs a vector of size 3 for each sample and the other outputs a vector of size one (i.e. scalar), again for each sample. As a result the output shapes would be (3, 3) and (3, 1).
Update: If you want your network to accept an input sample of shape (3,3) and outputs vectors of size 3 and 1, and you want to only use Dense layers in your network, then you must use a Flatten layer somewhere in the model. One possible option is to use it right after the input layer:
inp = layers.Input((3,3)) # don't forget to set the correct input shape
x = Flatten()(inp)
# pass x to other Dense layers
Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer.
Update 2: As @Mete correctly pointed out in the comments, make sure the input array have a shape of (num_samples, 3, 3) if each input sample has a shape of (3,3).
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
You are passing three input samples to the network:
>>> inpu.shape
(3,3) # three samples of size 3
And you have two output layers: one of them outputs a vector of size 3 for each sample and the other outputs a vector of size one (i.e. scalar), again for each sample. As a result the output shapes would be (3, 3) and (3, 1).
Update: If you want your network to accept an input sample of shape (3,3) and outputs vectors of size 3 and 1, and you want to only use Dense layers in your network, then you must use a Flatten layer somewhere in the model. One possible option is to use it right after the input layer:
inp = layers.Input((3,3)) # don't forget to set the correct input shape
x = Flatten()(inp)
# pass x to other Dense layers
Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer.
Update 2: As @Mete correctly pointed out in the comments, make sure the input array have a shape of (num_samples, 3, 3) if each input sample has a shape of (3,3).
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
edited Nov 21 '18 at 12:33
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
answered Nov 21 '18 at 12:20
todaytoday
11.3k22239
11.3k22239
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
inp = layers.Input((3,3))means every sample is 3x3inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]])(note shape is 1x3x3) means you have 1 sample of 3x3 data
– Mete Han Kahraman
Nov 21 '18 at 12:29
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
|
show 1 more comment
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
inp = layers.Input((3,3))means every sample is 3x3inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]])(note shape is 1x3x3) means you have 1 sample of 3x3 data
– Mete Han Kahraman
Nov 21 '18 at 12:29
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
I don't want to have in input 3 samples of size 3. I want to have one sample of size 3x3. Could you tell me how to do that?
– Federico Dorato
Nov 21 '18 at 12:22
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
@FedericoDorato Please see my updated answer.
– today
Nov 21 '18 at 12:28
inp = layers.Input((3,3))means every sample is 3x3 inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]]) (note shape is 1x3x3) means you have 1 sample of 3x3 data– Mete Han Kahraman
Nov 21 '18 at 12:29
inp = layers.Input((3,3))means every sample is 3x3 inpu = np.array([[[1,2,3],[4,5,6],[7,8,9]]]) (note shape is 1x3x3) means you have 1 sample of 3x3 data– Mete Han Kahraman
Nov 21 '18 at 12:29
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@FedericoDorato Please see the second update.
– today
Nov 21 '18 at 12:35
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
@today i am forced to follow this: "Alternatively, you could flatten your data to have a shape of (num_samples, 9) and then pass it to your network without using a Flatten layer." Because otherwise there is this error: 'Tensor' object has no attribute 'lower'
– Federico Dorato
Nov 30 '18 at 15:05
|
show 1 more comment
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