visulization_eff.py

# plot feature map of first conv layer for given image
from keras.applications.EfficientNetB0 import EfficientNetB0
from keras.applications.efficientnet import preprocess_input
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.models import Model
from matplotlib import pyplot
from numpy import expand_dims
# load the model
model = EfficientNetB0()
# redefine model to output right after the first hidden layer
model = Model(inputs=model.inputs, outputs=model.layers[1].output)
model.summary()
# load the image with the required shape
img = load_img('bird.jpg', target_size=(224, 224))
# convert the image to an array
img = img_to_array(img)
# expand dimensions so that it represents a single 'sample'
img = expand_dims(img, axis=0)
# prepare the image (e.g. scale pixel values for the vgg)
img = preprocess_input(img)
# get feature map for first hidden layer
feature_maps = model.predict(img)
# plot all 64 maps in an 8x8 squares
square = 8
ix = 1
for _ in range(square):
    for _ in range(square):
        # specify subplot and turn of axis
        ax = pyplot.subplot(square, square, ix)
        ax.set_xticks([])
        ax.set_yticks([])
        # plot filter channel in grayscale
        pyplot.imshow(feature_maps[0, :, :, ix-1], cmap='gray')
        ix += 1
# show the figure
pyplot.savefig('plots/visulization/test.png')