CODEWITHSUNDEEP
tensorflowfilterconv-neural-network
Lionel
Lionel

Reputation: 331

How to display the convolution filters used on a CNN with Tensorflow?

I would like to produce figures similar to this one: convolution filters

To do that, with Tensorflow I load my model and then, using this code I am about to select the variable with filters from one layer :

# search for the name of the specific layer with the filters I want to display
for v in tf.trainable_variables():
    print(v.name)
# store the filters into a variable
var = [v for v in tf.trainable_variables() if v.name == "model/center/kernel:0"][0]

doing var.eval() I am able to store var into a numpy array. This numpy array have this shape: (3, 3, 512, 512) which correspond to the kernel size: 3x3 and the number of filters: 512.

My problem is the following: How can I extract 1 filter from this 3,3,512,512 array to display it ? If I understand how to do that, I will find how to display the 512 filters

Upvotes: 1

Views: 2066

Answers (1)

user11530462
user11530462

Reputation:

Since you are using Tensorflow, you might be using tf.keras.Sequential for building the CNN Model, and model.summary() gives the names of all the Layers, along with Shapes, as shown below:

enter image description here

Once you have the Layer Name, you can Visualize the Convolutional Filters of that Layer of CNN as shown in the code below:

#-------------------------------------------------
#Utility function for displaying filters as images
#-------------------------------------------------

def deprocess_image(x):

    x -= x.mean()
    x /= (x.std() + 1e-5)
    x *= 0.1
    x += 0.5
    x = np.clip(x, 0, 1)
    x *= 255
    x = np.clip(x, 0, 255).astype('uint8')
    return x

#---------------------------------------------------------------------------------------------------
#Utility function for generating patterns for given layer starting from empty input image and then 
#applying Stochastic Gradient Ascent for maximizing the response of particular filter in given layer
#---------------------------------------------------------------------------------------------------

def generate_pattern(layer_name, filter_index, size=150):

    layer_output = model.get_layer(layer_name).output
    loss = K.mean(layer_output[:, :, :, filter_index])
    grads = K.gradients(loss, model.input)[0]
    grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)
    iterate = K.function([model.input], [loss, grads])
    input_img_data = np.random.random((1, size, size, 3)) * 20 + 128.
    step = 1.
    for i in range(80):
        loss_value, grads_value = iterate([input_img_data])
        input_img_data += grads_value * step

    img = input_img_data[0]
    return deprocess_image(img)

#------------------------------------------------------------------------------------------
#Generating convolution layer filters for intermediate layers using above utility functions
#------------------------------------------------------------------------------------------

layer_name = 'conv2d_4'
size = 299
margin = 5
results = np.zeros((8 * size + 7 * margin, 8 * size + 7 * margin, 3))

for i in range(8):
    for j in range(8):
        filter_img = generate_pattern(layer_name, i + (j * 8), size=size)
        horizontal_start = i * size + i * margin
        horizontal_end = horizontal_start + size
        vertical_start = j * size + j * margin
        vertical_end = vertical_start + size
        results[horizontal_start: horizontal_end, vertical_start: vertical_end, :] = filter_img

plt.figure(figsize=(20, 20))
plt.savefig(results)

The above code Visualizes only 64 filters of a Layer. You can change it accordingly.

For more information, you can refer this article.

Upvotes: 1

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