Extract feature vector of a CNN2019 Community Moderator ElectionHow do I provide input and output for such a network structure in kerasspeech accent recognition data augmentation and trainingHow can I find out what class each of the columns in the probabilities output correspond to using Keras for a multi-class classification problem?Visualizing ConvNet filters using my own fine-tuned network resulting in a “NoneType” when running: K.gradients(loss, model.input)[0]Keras- LSTM answers different sizeCNN backpropagation between layersGenerating image embedding using CNNdegeneracy of a CNN having only 1 convolution kernel down to a fully connected NNKeras: Poor classification by copying model weights before fine tuningHow do I create a feature vector for the training of an SVM?
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Extract feature vector of a CNN
2019 Community Moderator ElectionHow do I provide input and output for such a network structure in kerasspeech accent recognition data augmentation and trainingHow can I find out what class each of the columns in the probabilities output correspond to using Keras for a multi-class classification problem?Visualizing ConvNet filters using my own fine-tuned network resulting in a “NoneType” when running: K.gradients(loss, model.input)[0]Keras- LSTM answers different sizeCNN backpropagation between layersGenerating image embedding using CNNdegeneracy of a CNN having only 1 convolution kernel down to a fully connected NNKeras: Poor classification by copying model weights before fine tuningHow do I create a feature vector for the training of an SVM?
$begingroup$
How can I get the feature vector of my dataset. I have a fine-tuned CNN model with my data. Now I want to feed the features of all my dataset extracted from the last layer of the CNN into a LSTM.
So far I have
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
cnn_output = cnn_model.get_layer('fc7').output
I know I will have to feed all my dataset into this model but I have no idea on how to "save" the features.
keras feature-extraction cnn
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
$endgroup$
add a comment |
$begingroup$
How can I get the feature vector of my dataset. I have a fine-tuned CNN model with my data. Now I want to feed the features of all my dataset extracted from the last layer of the CNN into a LSTM.
So far I have
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
cnn_output = cnn_model.get_layer('fc7').output
I know I will have to feed all my dataset into this model but I have no idea on how to "save" the features.
keras feature-extraction cnn
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
$endgroup$
add a comment |
$begingroup$
How can I get the feature vector of my dataset. I have a fine-tuned CNN model with my data. Now I want to feed the features of all my dataset extracted from the last layer of the CNN into a LSTM.
So far I have
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
cnn_output = cnn_model.get_layer('fc7').output
I know I will have to feed all my dataset into this model but I have no idea on how to "save" the features.
keras feature-extraction cnn
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
$endgroup$
How can I get the feature vector of my dataset. I have a fine-tuned CNN model with my data. Now I want to feed the features of all my dataset extracted from the last layer of the CNN into a LSTM.
So far I have
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
cnn_output = cnn_model.get_layer('fc7').output
I know I will have to feed all my dataset into this model but I have no idea on how to "save" the features.
keras feature-extraction cnn
keras feature-extraction cnn
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
asked Jan 23 '18 at 16:10
Daniel ZapataDaniel Zapata
214
214
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
In order to "tap" intermediate layers of an existing model you could do the following:
# get your feature layer
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
feature_layer = cnn_model.get_layer('fc7')
# stack your LSTM and other layers below, e.g.:
lstm_layer = TimeDistributed(LSTM(...), input_shape = ...)(feature_layer)
output = Dense(...)(lstm_layer)
# create a combined model
model = Model(inputs = cnn_model.input, outputs = [output])
answered 6 hours ago
m0nzderrm0nzderr
663
$endgroup$
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
In order to "tap" intermediate layers of an existing model you could do the following:
# get your feature layer
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
feature_layer = cnn_model.get_layer('fc7')
# stack your LSTM and other layers below, e.g.:
lstm_layer = TimeDistributed(LSTM(...), input_shape = ...)(feature_layer)
output = Dense(...)(lstm_layer)
# create a combined model
model = Model(inputs = cnn_model.input, outputs = [output])
answered 6 hours ago
m0nzderrm0nzderr
663
$endgroup$
add a comment |
$begingroup$
In order to "tap" intermediate layers of an existing model you could do the following:
# get your feature layer
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
feature_layer = cnn_model.get_layer('fc7')
# stack your LSTM and other layers below, e.g.:
lstm_layer = TimeDistributed(LSTM(...), input_shape = ...)(feature_layer)
output = Dense(...)(lstm_layer)
# create a combined model
model = Model(inputs = cnn_model.input, outputs = [output])
answered 6 hours ago
m0nzderrm0nzderr
663
$endgroup$
add a comment |
$begingroup$
In order to "tap" intermediate layers of an existing model you could do the following:
# get your feature layer
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
feature_layer = cnn_model.get_layer('fc7')
# stack your LSTM and other layers below, e.g.:
lstm_layer = TimeDistributed(LSTM(...), input_shape = ...)(feature_layer)
output = Dense(...)(lstm_layer)
# create a combined model
model = Model(inputs = cnn_model.input, outputs = [output])
answered 6 hours ago
m0nzderrm0nzderr
663
$endgroup$
In order to "tap" intermediate layers of an existing model you could do the following:
# get your feature layer
cnn_model = load_model('weights/pre-trained_CNN.hdf5')
feature_layer = cnn_model.get_layer('fc7')
# stack your LSTM and other layers below, e.g.:
lstm_layer = TimeDistributed(LSTM(...), input_shape = ...)(feature_layer)
output = Dense(...)(lstm_layer)
# create a combined model
model = Model(inputs = cnn_model.input, outputs = [output])
answered 6 hours ago
m0nzderrm0nzderr
663
answered 6 hours ago
m0nzderrm0nzderr
663
answered 6 hours ago
m0nzderrm0nzderr
663
answered 6 hours ago
m0nzderrm0nzderr
663
663
add a comment |
add a comment |
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