Comments (2)
To any researchers reading this, sleep is important. Go take a nap.
So, turns out that this was a very silly question.
Here's a horrible hacky way of doing it for now, I'll work on something better - soonish.
def visualise_first_layer(model, convout1, test_data):
get_layer_output = K.function([model.layers[0].input], [convout1.get_output(train=False)])
layer_output = get_layer_output([test_data])[0]
f, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(1, 6, sharey=True)
ax1.imshow(layer_output[0,0,1,:])
ax2.imshow(layer_output[0,10,1,:])
ax3.imshow(layer_output[0,20,1,:])
ax4.imshow(layer_output[0,30,1,:])
ax5.imshow(layer_output[0,40,1,:])
ax6.imshow(layer_output[0,50,1,:])
plt.show()
from keras-extra.
Bit of a better approach here.
def visualise_first_layer(model, convout1, test_data):
get_layer_output = K.function([model.layers[0].input], [convout1.get_output(train=False)])
layer_output = get_layer_output([test_data])[0]
for i in range(layer_output.shape[1]):
p = plt.imshow(layer_output[0,i,1,:])
plt.pause(0.1)
This should display the features from each timeframe sequentially using a single window - I guess you could call it a slide show.
from keras-extra.
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from keras-extra.