Calculating saliency maps for text classification
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I'm following the text classification with movie reviews TensorFlow tutorial, and wanted to extend the project by looking, for a certain input, which words influenced the classification the most.
I understand this is called a saliency map, but I'm having trouble calculating it. I believe that I need to calculate the gradients of the output with respect to the input. I tried to implement code similar to the code in this answer to no avail. A confounding issue is that the model uses an embedding layer, which doesn't propagate the gradient, so I think one needs to calculate the gradients with the input being the output of the embedding layer.
It's probably wrong for all sorts of reasons, but this is the closest I've gotten with the Python code:
# Create the saliency function
input_tensors = [model.layers[1].input, keras.backend.learning_phase()]
model_input = model.layers[1].input
model_output = model.output
gradients = model.optimizer.get_gradients(model_output, model_input)
compute_gradients = keras.backend.function(inputs=input_tensors, outputs=gradients)
# Word encoding
idx = 0 # Calculate the saliency for the first training example
embeddings = model.layers[0].get_weights()[0]
embedded_training_data = embeddings[train_data[idx]]
matrix = compute_gradients([embedded_training_data.reshape(sum([(1,), embedded_training_data.shape], ())), train_labels[idx]])
But the final matrix is the same row repeated and I'm not sure how to interpret it. Any help would be greatly appreciated. Thankfully, as this is extending a tutorial, there is a complete working example of the code!
machine-learning python deep-learning tensorflow
asked 6 hours ago
Marc JonesMarc Jones
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm following the text classification with movie reviews TensorFlow tutorial, and wanted to extend the project by looking, for a certain input, which words influenced the classification the most.
I understand this is called a saliency map, but I'm having trouble calculating it. I believe that I need to calculate the gradients of the output with respect to the input. I tried to implement code similar to the code in this answer to no avail. A confounding issue is that the model uses an embedding layer, which doesn't propagate the gradient, so I think one needs to calculate the gradients with the input being the output of the embedding layer.
It's probably wrong for all sorts of reasons, but this is the closest I've gotten with the Python code:
# Create the saliency function
input_tensors = [model.layers[1].input, keras.backend.learning_phase()]
model_input = model.layers[1].input
model_output = model.output
gradients = model.optimizer.get_gradients(model_output, model_input)
compute_gradients = keras.backend.function(inputs=input_tensors, outputs=gradients)
# Word encoding
idx = 0 # Calculate the saliency for the first training example
embeddings = model.layers[0].get_weights()[0]
embedded_training_data = embeddings[train_data[idx]]
matrix = compute_gradients([embedded_training_data.reshape(sum([(1,), embedded_training_data.shape], ())), train_labels[idx]])
But the final matrix is the same row repeated and I'm not sure how to interpret it. Any help would be greatly appreciated. Thankfully, as this is extending a tutorial, there is a complete working example of the code!
machine-learning python deep-learning tensorflow
asked 6 hours ago
Marc JonesMarc Jones
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm following the text classification with movie reviews TensorFlow tutorial, and wanted to extend the project by looking, for a certain input, which words influenced the classification the most.
I understand this is called a saliency map, but I'm having trouble calculating it. I believe that I need to calculate the gradients of the output with respect to the input. I tried to implement code similar to the code in this answer to no avail. A confounding issue is that the model uses an embedding layer, which doesn't propagate the gradient, so I think one needs to calculate the gradients with the input being the output of the embedding layer.
It's probably wrong for all sorts of reasons, but this is the closest I've gotten with the Python code:
# Create the saliency function
input_tensors = [model.layers[1].input, keras.backend.learning_phase()]
model_input = model.layers[1].input
model_output = model.output
gradients = model.optimizer.get_gradients(model_output, model_input)
compute_gradients = keras.backend.function(inputs=input_tensors, outputs=gradients)
# Word encoding
idx = 0 # Calculate the saliency for the first training example
embeddings = model.layers[0].get_weights()[0]
embedded_training_data = embeddings[train_data[idx]]
matrix = compute_gradients([embedded_training_data.reshape(sum([(1,), embedded_training_data.shape], ())), train_labels[idx]])
But the final matrix is the same row repeated and I'm not sure how to interpret it. Any help would be greatly appreciated. Thankfully, as this is extending a tutorial, there is a complete working example of the code!
machine-learning python deep-learning tensorflow
asked 6 hours ago
Marc JonesMarc Jones
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm following the text classification with movie reviews TensorFlow tutorial, and wanted to extend the project by looking, for a certain input, which words influenced the classification the most.
I understand this is called a saliency map, but I'm having trouble calculating it. I believe that I need to calculate the gradients of the output with respect to the input. I tried to implement code similar to the code in this answer to no avail. A confounding issue is that the model uses an embedding layer, which doesn't propagate the gradient, so I think one needs to calculate the gradients with the input being the output of the embedding layer.
It's probably wrong for all sorts of reasons, but this is the closest I've gotten with the Python code:
# Create the saliency function
input_tensors = [model.layers[1].input, keras.backend.learning_phase()]
model_input = model.layers[1].input
model_output = model.output
gradients = model.optimizer.get_gradients(model_output, model_input)
compute_gradients = keras.backend.function(inputs=input_tensors, outputs=gradients)
# Word encoding
idx = 0 # Calculate the saliency for the first training example
embeddings = model.layers[0].get_weights()[0]
embedded_training_data = embeddings[train_data[idx]]
matrix = compute_gradients([embedded_training_data.reshape(sum([(1,), embedded_training_data.shape], ())), train_labels[idx]])
But the final matrix is the same row repeated and I'm not sure how to interpret it. Any help would be greatly appreciated. Thankfully, as this is extending a tutorial, there is a complete working example of the code!
machine-learning python deep-learning tensorflow
machine-learning python deep-learning tensorflow
asked 6 hours ago
Marc JonesMarc Jones
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
Marc JonesMarc Jones
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
Marc JonesMarc Jones
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
Marc JonesMarc Jones
1
asked 6 hours ago
Marc JonesMarc Jones
1
1
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Marc Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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