To Visualize CNN Hidden layers

visualiize_cnn.py

# To Visualize CNN Layers for better interpretability. Base code obtained from:
# https://medium.com/@awjuliani/visualizing-neural-network-layer-activation-tensorflow-tutorial-d45f8bf7bbc4
def getActivations(layer,stimuli, filename, steer, acceleration):
    units = sess.run(layer,feed_dict={model.x:np.reshape(stimuli,[-1, 136, 240, 3],order='F'), model.y1_: np.reshape(steer, [-1, 1],order='F'), model.y2_: acceleration, model.keep_prob:1.0})
    plotNNFilter(units, filename)


def plotNNFilter(units, filename):
    filters = units.shape[3]
    plt.figure(1, figsize=(20,20))
    n_columns = 6
    n_rows = math.ceil(filters / n_columns) + 1
    for i in range(filters):
        plt.subplot(n_rows, n_columns, i+1)
        plt.title('Filter ' + str(i))
        plt.imshow(units[0,:,:,i], interpolation="nearest", cmap="gray")
    plt.savefig(filename)

xs = []
ys = []
accels = []
with open("indian_dataset/data.txt") as f:
    for line in f:
        xs.append("indian_dataset/circuit2_x264.mp4 " + str(line.split()[0]).zfill(5) + ".jpg")
        ys.append(float(line.split()[1]) * scipy.pi / 180)
        accels.append(float(line.split()[2]))

frameNum = 4805
full_image = scipy.misc.imread(xs[frameNum], mode="RGB")
image = scipy.misc.imresize(full_image[-150:], [136, 240]) / 255.0
cv2.imshow("Visualize CNN: input image", cv2.cvtColor(full_image, cv2.COLOR_RGB2BGR))

getActivations(model_steer.h_conv1,image, 'cnn-depict-conv1.jpg', ys[frameNum], accels[frameNum])
getActivations(model_steer.h_conv2,image, 'cnn-depict-conv2.jpg', ys[frameNum], accels[frameNum])
getActivations(model_steer.h_conv3,image, 'cnn-depict-conv3.jpg', ys[frameNum], accels[frameNum])
getActivations(model_steer.h_conv4,image, 'cnn-depict-conv4.jpg', ys[frameNum], accels[frameNum])
getActivations(model_steer.h_conv5,image, 'cnn-depict-conv5.jpg', ys[frameNum], accels[frameNum])
getActivations(model_steer.h_conv6,image, 'cnn-depict-conv6.jpg', ys[frameNum], accels[frameNum])