Can GradCAM be different when all conditons are same without batch_size?
0
I Use CNN-architecture.
And i used gradCAM using keras-vis.
I found something strange.
When i just changed input image's batch_size, it's result is different.
(same batch_size, same result)
I don't know why these things happen.
in function visualize_cam_with_losses, can 'grads' be different if model and input image is same?
penultimate_output = penultimate_layer.output
opt = Optimizer(input_tensor, losses, wrt_tensor=penultimate_output,
norm_grads=False)
_, grads, penultimate_output_value = opt.minimize(seed_input, max_iter=1,
grad_modifier=grad_modifier, verbose=False)
# For numerical stability. Very small grad values along with small penultimate_output_value can cause
# w * penultimate_output_value to zero out, even for reasonable fp precision of float32.
grads = grads / (np.max(grads) + K.epsilon())
# Average pooling across all feature maps.
# This captures the importance of feature map (channel) idx to the output.
channel_idx = 1 if K.image_data_format() == 'channels_first' else -1
other_axis = np.delete(np.arange(len(grads.shape)), channel_idx)
weights = np.mean(grads, axis=tuple(other_axis))
# Generate heatmap by computing weight * output over feature maps
output_dims = utils.get_img_shape(penultimate_output)[2:]
heatmap = np.zeros(shape=output_dims, dtype=K.floatx())
for i, w in enumerate(weights):
if channel_idx == -1:
heatmap += w * penultimate_output_value[0, ..., i]
else:
heatmap += w * penultimate_output_value[0, i, ...]
# ReLU thresholding to exclude pattern mismatch information (negative gradients).
heatmap = np.maximum(heatmap, 0)
# The penultimate feature map size is definitely smaller than input image.
input_dims = utils.get_img_shape(input_tensor)[2:]
heatmap = imresize(heatmap, input_dims, interp='bicubic', mode='F')
# Normalize and create heatmap.
heatmap = utils.normalize(heatmap)
return heatmap, np.uint8(cm.jet(heatmap)[..., :3] * 255)
keras deep-learning conv-neural-network visualize
asked Nov 18 '18 at 9:22
김민우김민우
11
add a comment |
0
I Use CNN-architecture.
And i used gradCAM using keras-vis.
I found something strange.
When i just changed input image's batch_size, it's result is different.
(same batch_size, same result)
I don't know why these things happen.
in function visualize_cam_with_losses, can 'grads' be different if model and input image is same?
penultimate_output = penultimate_layer.output
opt = Optimizer(input_tensor, losses, wrt_tensor=penultimate_output,
norm_grads=False)
_, grads, penultimate_output_value = opt.minimize(seed_input, max_iter=1,
grad_modifier=grad_modifier, verbose=False)
# For numerical stability. Very small grad values along with small penultimate_output_value can cause
# w * penultimate_output_value to zero out, even for reasonable fp precision of float32.
grads = grads / (np.max(grads) + K.epsilon())
# Average pooling across all feature maps.
# This captures the importance of feature map (channel) idx to the output.
channel_idx = 1 if K.image_data_format() == 'channels_first' else -1
other_axis = np.delete(np.arange(len(grads.shape)), channel_idx)
weights = np.mean(grads, axis=tuple(other_axis))
# Generate heatmap by computing weight * output over feature maps
output_dims = utils.get_img_shape(penultimate_output)[2:]
heatmap = np.zeros(shape=output_dims, dtype=K.floatx())
for i, w in enumerate(weights):
if channel_idx == -1:
heatmap += w * penultimate_output_value[0, ..., i]
else:
heatmap += w * penultimate_output_value[0, i, ...]
# ReLU thresholding to exclude pattern mismatch information (negative gradients).
heatmap = np.maximum(heatmap, 0)
# The penultimate feature map size is definitely smaller than input image.
input_dims = utils.get_img_shape(input_tensor)[2:]
heatmap = imresize(heatmap, input_dims, interp='bicubic', mode='F')
# Normalize and create heatmap.
heatmap = utils.normalize(heatmap)
return heatmap, np.uint8(cm.jet(heatmap)[..., :3] * 255)
keras deep-learning conv-neural-network visualize
asked Nov 18 '18 at 9:22
김민우김민우
11
add a comment |
0
0
0
I Use CNN-architecture.
And i used gradCAM using keras-vis.
I found something strange.
When i just changed input image's batch_size, it's result is different.
(same batch_size, same result)
I don't know why these things happen.
in function visualize_cam_with_losses, can 'grads' be different if model and input image is same?
penultimate_output = penultimate_layer.output
opt = Optimizer(input_tensor, losses, wrt_tensor=penultimate_output,
norm_grads=False)
_, grads, penultimate_output_value = opt.minimize(seed_input, max_iter=1,
grad_modifier=grad_modifier, verbose=False)
# For numerical stability. Very small grad values along with small penultimate_output_value can cause
# w * penultimate_output_value to zero out, even for reasonable fp precision of float32.
grads = grads / (np.max(grads) + K.epsilon())
# Average pooling across all feature maps.
# This captures the importance of feature map (channel) idx to the output.
channel_idx = 1 if K.image_data_format() == 'channels_first' else -1
other_axis = np.delete(np.arange(len(grads.shape)), channel_idx)
weights = np.mean(grads, axis=tuple(other_axis))
# Generate heatmap by computing weight * output over feature maps
output_dims = utils.get_img_shape(penultimate_output)[2:]
heatmap = np.zeros(shape=output_dims, dtype=K.floatx())
for i, w in enumerate(weights):
if channel_idx == -1:
heatmap += w * penultimate_output_value[0, ..., i]
else:
heatmap += w * penultimate_output_value[0, i, ...]
# ReLU thresholding to exclude pattern mismatch information (negative gradients).
heatmap = np.maximum(heatmap, 0)
# The penultimate feature map size is definitely smaller than input image.
input_dims = utils.get_img_shape(input_tensor)[2:]
heatmap = imresize(heatmap, input_dims, interp='bicubic', mode='F')
# Normalize and create heatmap.
heatmap = utils.normalize(heatmap)
return heatmap, np.uint8(cm.jet(heatmap)[..., :3] * 255)
keras deep-learning conv-neural-network visualize
asked Nov 18 '18 at 9:22
김민우김민우
11
I Use CNN-architecture.
And i used gradCAM using keras-vis.
I found something strange.
When i just changed input image's batch_size, it's result is different.
(same batch_size, same result)
I don't know why these things happen.
in function visualize_cam_with_losses, can 'grads' be different if model and input image is same?
penultimate_output = penultimate_layer.output
opt = Optimizer(input_tensor, losses, wrt_tensor=penultimate_output,
norm_grads=False)
_, grads, penultimate_output_value = opt.minimize(seed_input, max_iter=1,
grad_modifier=grad_modifier, verbose=False)
# For numerical stability. Very small grad values along with small penultimate_output_value can cause
# w * penultimate_output_value to zero out, even for reasonable fp precision of float32.
grads = grads / (np.max(grads) + K.epsilon())
# Average pooling across all feature maps.
# This captures the importance of feature map (channel) idx to the output.
channel_idx = 1 if K.image_data_format() == 'channels_first' else -1
other_axis = np.delete(np.arange(len(grads.shape)), channel_idx)
weights = np.mean(grads, axis=tuple(other_axis))
# Generate heatmap by computing weight * output over feature maps
output_dims = utils.get_img_shape(penultimate_output)[2:]
heatmap = np.zeros(shape=output_dims, dtype=K.floatx())
for i, w in enumerate(weights):
if channel_idx == -1:
heatmap += w * penultimate_output_value[0, ..., i]
else:
heatmap += w * penultimate_output_value[0, i, ...]
# ReLU thresholding to exclude pattern mismatch information (negative gradients).
heatmap = np.maximum(heatmap, 0)
# The penultimate feature map size is definitely smaller than input image.
input_dims = utils.get_img_shape(input_tensor)[2:]
heatmap = imresize(heatmap, input_dims, interp='bicubic', mode='F')
# Normalize and create heatmap.
heatmap = utils.normalize(heatmap)
return heatmap, np.uint8(cm.jet(heatmap)[..., :3] * 255)
keras deep-learning conv-neural-network visualize
keras deep-learning conv-neural-network visualize
asked Nov 18 '18 at 9:22
김민우김민우
11
asked Nov 18 '18 at 9:22
김민우김민우
11
asked Nov 18 '18 at 9:22
김민우김민우
11
asked Nov 18 '18 at 9:22
김민우김민우
11
asked Nov 18 '18 at 9:22
김민우김민우
11
11
add a comment |
add a comment |
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