November 2018
Intermediate to advanced
492 pages
12h 19m
English
Content preview from Hands-On Image Processing with Python
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Visualizing the intermediate layers
Now let's visualize the image feature maps (64 features with 64 filters) that are learned with the convolutional layer for a couple of images using the following code block:
from keras.models import Modelimport matplotlib.pylab as pylabimport numpy as npintermediate_layer_model = Model(inputs=model.input, outputs=model.get_layer('conv2d_1').output)intermediate_output = intermediate_layer_model.predict(X_train)print(model.input.shape, intermediate_output.shape)fig = pylab.figure(figsize=(15,15))fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05) pylab.gray()i = 1 for c in range(64): pylab.subplot(8, 8, c+1), pylab.imshow(intermediate_output[i,:,:,c]), pylab.axis('off')pylab.show() ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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