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Keras - visualize classes on CNN

To generate Google-Dreamthese images, I try to change the input image, optimizing the network inceptionV3with a gradient ascent .

Desired effect: https://github.com/google/deepdream/blob/master/dream.ipynb

(For more information about this, see [ https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html .)

In this regard, I completely configured the initial network using the method transfer learningand created a model: inceptionv3-ft.model

model.summary() prints the following architecture (abbreviated here due to space limitations):

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_4 (Activation)       (None, None, None, 8 0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, None, None, 1 138240      activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, None, None, 1 576         conv2d_5[0][0]                   
__________________________________________________________________________________________________
activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_5[0][0]               
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, None, None, 6 192         conv2d_9[0][0]                   
__________________________________________________________________________________________________
activation_9 (Activation)       (None, None, None, 6 0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, None, None, 9 55296       activation_9[0][0]               
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, None, None, 4 144         conv2d_7[0][0]                   
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  
__________________________________________________________________________________________________
activation_7 (Activation)       (None, None, None, 4 0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, None, None, 1 0           max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            
__________________________________________________________________________________________________

(...) 

mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_88[0][0]              
                                                                         activation_89[0][0]              
        __________________________________________________________________________________________________
        concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_92[0][0]              
                                                                         activation_93[0][0]              
        __________________________________________________________________________________________________
        activation_94 (Activation)      (None, None, None, 1 0           batch_normalization_94[0][0]     
        __________________________________________________________________________________________________
        mixed10 (Concatenate)           (None, None, None, 2 0           activation_86[0][0]              
                                                                         mixed9_1[0][0]                   
                                                                         concatenate_2[0][0]              
                                                                         activation_94[0][0]              
        __________________________________________________________________________________________________
        global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    
        __________________________________________________________________________________________________
        dense_1 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_1[0][0] 
        __________________________________________________________________________________________________
        dense_2 (Dense)                 (None, 1)            1025        dense_1[0][0]                    
        ==================================================================================================
        Total params: 23,901,985
        Trainable params: 18,315,137
        Non-trainable params: 5,586,848
        ____________________________________

, , :

settings = {
    'features': {
        'mixed2': 0.,
        'mixed3': 0.,
        'mixed4': 0.,
        'mixed10': 0., #highest
    },
}
model = load_model('inceptionv3-ft.model')

#Get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers])

#Define the loss.
loss = K.variable(0.)

for layer_name in settings['features']:
    # Add the L2 norm of the features of a layer to the loss.
    assert layer_name in layer_dict.keys(), 'Layer ' + layer_name + ' not found in model.'

    coeff = settings['features'][layer_name]
    x = layer_dict[layer_name].output
    print (x)
    # We avoid border artifacts by only involving non-border pixels in the loss.
    scaling = K.prod(K.cast(K.shape(x), 'float32'))

    if K.image_data_format() == 'channels_first':
        loss += coeff * K.sum(K.square(x[:, :, 2: -2, 2: -2])) / scaling
    else:
        loss += coeff * K.sum(K.square(x[:, 2: -2, 2: -2, :])) / scaling

# Compute the gradients of the dream wrt the loss.
grads = K.gradients(loss, dream)[0]
# Normalize gradients.
grads /= K.maximum(K.mean(K.abs(grads)), K.epsilon())

# Set up function to retrieve the value
# of the loss and gradients given an input image.
outputs = [loss, grads]
fetch_loss_and_grads = K.function([dream], outputs)

def eval_loss_and_grads(x):
    outs = fetch_loss_and_grads([x])
    loss_value = outs[0]
    grad_values = outs[1]
    return loss_value, grad_values


def resize_img(img, size):
    img = np.copy(img)
    if K.image_data_format() == 'channels_first':
        factors = (1, 1,
                   float(size[0]) / img.shape[2],
                   float(size[1]) / img.shape[3])
    else:
        factors = (1,
                   float(size[0]) / img.shape[1],
                   float(size[1]) / img.shape[2],
                   1)
    return scipy.ndimage.zoom(img, factors, order=1)


def gradient_ascent(x, iterations, step, max_loss=None):
    for i in range(iterations):
        loss_value, grad_values = eval_loss_and_grads(x)
        if max_loss is not None and loss_value > max_loss:
            break
        print('..Loss value at', i, ':', loss_value)
        x += step * grad_values
    return x


def save_img(img, fname):
    pil_img = deprocess_image(np.copy(img))
    scipy.misc.imsave(fname, pil_img)


"""Process:
- Load the original image.
- Define a number of processing scales (i.e. image shapes),
    from smallest to largest.
- Resize the original image to the smallest scale.
- For every scale, starting with the smallest (i.e. current one):
    - Run gradient ascent
    - Upscale image to the next scale
    - Reinject the detail that was lost at upscaling time
- Stop when we are back to the original size.
To obtain the detail lost during upscaling, we simply
take the original image, shrink it down, upscale it,
and compare the result to the (resized) original image.
"""


# Playing with these hyperparameters will also allow you to achieve new effects
step = 0.01  # Gradient ascent step size
num_octave = 3  # Number of scales at which to run gradient ascent
octave_scale = 1.4  # Size ratio between scales
iterations = 20  # Number of ascent steps per scale
max_loss = 10.

img = preprocess_image(base_image_path)

if K.image_data_format() == 'channels_first':
    original_shape = img.shape[2:]
else:
    original_shape = img.shape[1:3]
successive_shapes = [original_shape]

for i in range(1, num_octave):
    shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
    successive_shapes.append(shape)

successive_shapes = successive_shapes[::-1]
original_img = np.copy(img)
shrunk_original_img = resize_img(img, successive_shapes[0])

for shape in successive_shapes:
    print('Processing image shape', shape)
    img = resize_img(img, shape)
    img = gradient_ascent(img,
                          iterations=iterations,
                          step=step,
                          max_loss=max_loss)
    upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)
    same_size_original = resize_img(original_img, shape)
    lost_detail = same_size_original - upscaled_shrunk_original_img

    img += lost_detail
    shrunk_original_img = resize_img(original_img, shape)

save_img(img, fname=result_prefix + '.png')

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