Setting input shape for an NLP task in R(Rstudio) using keras 1D convolution layer, when it expects 3 dimensional input (a tensor) Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election Resultsfeature extraction for a pretrained model in kerasKeras — Transfer learning — changing Input tensor shapeKeras shape error in applications Inception Resnet v2How to set input for proper fit with lstm?Keras exception: ValueError: Error when checking input: expected conv2d_1_input to have shape (150, 150, 3) but got array with shape (256, 256, 3)Keras input shape errorKeras input shape returning an error3 dimensional array as input with Embedding Layer and LSTM in KerasIN CIFAR 10 DATASETHow to set kernel size (height and width) for 1D convolution layer in CNN Keras R API for doc2vec input?
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Setting input shape for an NLP task in R(Rstudio) using keras 1D convolution layer, when it expects 3 dimensional input (a tensor)
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election Resultsfeature extraction for a pretrained model in kerasKeras — Transfer learning — changing Input tensor shapeKeras shape error in applications Inception Resnet v2How to set input for proper fit with lstm?Keras exception: ValueError: Error when checking input: expected conv2d_1_input to have shape (150, 150, 3) but got array with shape (256, 256, 3)Keras input shape errorKeras input shape returning an error3 dimensional array as input with Embedding Layer and LSTM in KerasIN CIFAR 10 DATASETHow to set kernel size (height and width) for 1D convolution layer in CNN Keras R API for doc2vec input?
$begingroup$
I am using R programming language and using Keras API to build a functional 1D CNN.
I have a matrix of my dataset of the following shape rows*features (6000*1024).
The input layer is set using the following code:
input_layer = layer_input(shape = 1024, batch_shape = c(nrow(train_matrix),1024), dtype = 'float64')
and then I am building a 1d conv layer as follows:
conv1 = input_layer %>% layer_conv_1d(filters = 32, kernel_size = 50, strides = 10, input_shape = 1024, batch_input_shape = list(NULL, 1024) ,dtype = 'float64', activation = 'relu' )
But I get the following error:
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Input 0 is incompatible with layer conv1d: expected ndim=3, found ndim=2
I believe it is due to the fact that 1D cnn layer expects the input in the following form
Input shape: 3D tensor with shape: (batch_size, steps, input_dim)
I understand that I have to reshape my data as (NULL, nrow(train_matrix), 1; as this has been suggested in various answer for the same issue arising for keras when used in Python.
If I am right,
- what values should I provide to input layer
- how should i reshape my training data?
- does that mean I have to reshape the test data as well?
also if my understanding is wrong what should be done otherwise ?
deep-learning keras r convnet
asked Apr 2 at 13:24
user3516188user3516188
62
$endgroup$
add a comment |
$begingroup$
I am using R programming language and using Keras API to build a functional 1D CNN.
I have a matrix of my dataset of the following shape rows*features (6000*1024).
The input layer is set using the following code:
input_layer = layer_input(shape = 1024, batch_shape = c(nrow(train_matrix),1024), dtype = 'float64')
and then I am building a 1d conv layer as follows:
conv1 = input_layer %>% layer_conv_1d(filters = 32, kernel_size = 50, strides = 10, input_shape = 1024, batch_input_shape = list(NULL, 1024) ,dtype = 'float64', activation = 'relu' )
But I get the following error:
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Input 0 is incompatible with layer conv1d: expected ndim=3, found ndim=2
I believe it is due to the fact that 1D cnn layer expects the input in the following form
Input shape: 3D tensor with shape: (batch_size, steps, input_dim)
I understand that I have to reshape my data as (NULL, nrow(train_matrix), 1; as this has been suggested in various answer for the same issue arising for keras when used in Python.
If I am right,
- what values should I provide to input layer
- how should i reshape my training data?
- does that mean I have to reshape the test data as well?
also if my understanding is wrong what should be done otherwise ?
deep-learning keras r convnet
asked Apr 2 at 13:24
user3516188user3516188
62
$endgroup$
add a comment |
$begingroup$
I am using R programming language and using Keras API to build a functional 1D CNN.
I have a matrix of my dataset of the following shape rows*features (6000*1024).
The input layer is set using the following code:
input_layer = layer_input(shape = 1024, batch_shape = c(nrow(train_matrix),1024), dtype = 'float64')
and then I am building a 1d conv layer as follows:
conv1 = input_layer %>% layer_conv_1d(filters = 32, kernel_size = 50, strides = 10, input_shape = 1024, batch_input_shape = list(NULL, 1024) ,dtype = 'float64', activation = 'relu' )
But I get the following error:
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Input 0 is incompatible with layer conv1d: expected ndim=3, found ndim=2
I believe it is due to the fact that 1D cnn layer expects the input in the following form
Input shape: 3D tensor with shape: (batch_size, steps, input_dim)
I understand that I have to reshape my data as (NULL, nrow(train_matrix), 1; as this has been suggested in various answer for the same issue arising for keras when used in Python.
If I am right,
- what values should I provide to input layer
- how should i reshape my training data?
- does that mean I have to reshape the test data as well?
also if my understanding is wrong what should be done otherwise ?
deep-learning keras r convnet
asked Apr 2 at 13:24
user3516188user3516188
62
$endgroup$
I am using R programming language and using Keras API to build a functional 1D CNN.
I have a matrix of my dataset of the following shape rows*features (6000*1024).
The input layer is set using the following code:
input_layer = layer_input(shape = 1024, batch_shape = c(nrow(train_matrix),1024), dtype = 'float64')
and then I am building a 1d conv layer as follows:
conv1 = input_layer %>% layer_conv_1d(filters = 32, kernel_size = 50, strides = 10, input_shape = 1024, batch_input_shape = list(NULL, 1024) ,dtype = 'float64', activation = 'relu' )
But I get the following error:
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Input 0 is incompatible with layer conv1d: expected ndim=3, found ndim=2
I believe it is due to the fact that 1D cnn layer expects the input in the following form
Input shape: 3D tensor with shape: (batch_size, steps, input_dim)
I understand that I have to reshape my data as (NULL, nrow(train_matrix), 1; as this has been suggested in various answer for the same issue arising for keras when used in Python.
If I am right,
- what values should I provide to input layer
- how should i reshape my training data?
- does that mean I have to reshape the test data as well?
also if my understanding is wrong what should be done otherwise ?
deep-learning keras r convnet
deep-learning keras r convnet
asked Apr 2 at 13:24
user3516188user3516188
62
asked Apr 2 at 13:24
user3516188user3516188
62
edited Apr 2 at 13:45
user3516188
asked Apr 2 at 13:24
user3516188user3516188
62
asked Apr 2 at 13:24
user3516188user3516188
62
asked Apr 2 at 13:24
user3516188user3516188
62
62
add a comment |
add a comment |
1 Answer
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$begingroup$
The input layer was producing a 2d tensor, whereas 1d convolutional layer expects a 3d tensor as input. A good explanation of as to why keras expects it and how should you reshape your 2d input to 3d can be found in this answer.
I used Keras R API function k_reshape( 2d_tensor, (list_of_new_dims)) --> k_reshape(input_layer, list(nrow(train_matrix), num_of_feature_vectors, 1).
nrow(train_matrix) - the total number of rows in my matrix (no of samples)
num_of_feature_vectors - total number of columns in matrix (total number of features)
1 - i want a 3d tensor with only 1 element at z axis, therefore z axis is initialised to 1
answered Apr 3 at 10:26
user3516188user3516188
62
$endgroup$
add a comment |
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$begingroup$
The input layer was producing a 2d tensor, whereas 1d convolutional layer expects a 3d tensor as input. A good explanation of as to why keras expects it and how should you reshape your 2d input to 3d can be found in this answer.
I used Keras R API function k_reshape( 2d_tensor, (list_of_new_dims)) --> k_reshape(input_layer, list(nrow(train_matrix), num_of_feature_vectors, 1).
nrow(train_matrix) - the total number of rows in my matrix (no of samples)
num_of_feature_vectors - total number of columns in matrix (total number of features)
1 - i want a 3d tensor with only 1 element at z axis, therefore z axis is initialised to 1
answered Apr 3 at 10:26
user3516188user3516188
62
$endgroup$
add a comment |
$begingroup$
The input layer was producing a 2d tensor, whereas 1d convolutional layer expects a 3d tensor as input. A good explanation of as to why keras expects it and how should you reshape your 2d input to 3d can be found in this answer.
I used Keras R API function k_reshape( 2d_tensor, (list_of_new_dims)) --> k_reshape(input_layer, list(nrow(train_matrix), num_of_feature_vectors, 1).
nrow(train_matrix) - the total number of rows in my matrix (no of samples)
num_of_feature_vectors - total number of columns in matrix (total number of features)
1 - i want a 3d tensor with only 1 element at z axis, therefore z axis is initialised to 1
answered Apr 3 at 10:26
user3516188user3516188
62
$endgroup$
add a comment |
$begingroup$
The input layer was producing a 2d tensor, whereas 1d convolutional layer expects a 3d tensor as input. A good explanation of as to why keras expects it and how should you reshape your 2d input to 3d can be found in this answer.
I used Keras R API function k_reshape( 2d_tensor, (list_of_new_dims)) --> k_reshape(input_layer, list(nrow(train_matrix), num_of_feature_vectors, 1).
nrow(train_matrix) - the total number of rows in my matrix (no of samples)
num_of_feature_vectors - total number of columns in matrix (total number of features)
1 - i want a 3d tensor with only 1 element at z axis, therefore z axis is initialised to 1
answered Apr 3 at 10:26
user3516188user3516188
62
$endgroup$
The input layer was producing a 2d tensor, whereas 1d convolutional layer expects a 3d tensor as input. A good explanation of as to why keras expects it and how should you reshape your 2d input to 3d can be found in this answer.
I used Keras R API function k_reshape( 2d_tensor, (list_of_new_dims)) --> k_reshape(input_layer, list(nrow(train_matrix), num_of_feature_vectors, 1).
nrow(train_matrix) - the total number of rows in my matrix (no of samples)
num_of_feature_vectors - total number of columns in matrix (total number of features)
1 - i want a 3d tensor with only 1 element at z axis, therefore z axis is initialised to 1
answered Apr 3 at 10:26
user3516188user3516188
62
answered Apr 3 at 10:26
user3516188user3516188
62
answered Apr 3 at 10:26
user3516188user3516188
62
answered Apr 3 at 10:26
user3516188user3516188
62
62
add a comment |
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