Visualizing ConvNet filters using my own fine-tuned network resulting in a "NoneType" when running: K.gradients(loss, model.input)[0] I have a fine-tuned network that I created which uses vgg16 as it's base.  I am following section 5.4.2 Visualizing CovNet Filters in Deep Learning With Python (which is very similar to the guide on the Keras blog to visualize convnet filters here). 

The guide simply uses the vgg16 network.  My fine tuned model uses the vgg16 model as the base, for example: 

model.summary()

Layer (type)                 Output Shape              Param #
======================================================================= vgg16 (Model)                (None, 4, 4, 512)         14714688
________________________________________________________________________ flatten_1 (Flatten)          (None, 8192)              0
________________________________________________________________________ dense_7 (Dense)              (None, 256)               2097408
________________________________________________________________________ dense_8 (Dense)              (None, 3)                 771
======================================================================== Total params: 16,812,867 Trainable params: 16,812,867 Non-trainable params: 0

I'm running into an issue when I run this line: 
grads = K.gradients(loss, model.input)[0] where when I use my fine tuned network I get a result that's a "NoneType" 

Here is the code from the guide: 

&gt; from keras.applications import VGG16 from keras import backend as K
&gt; 
&gt; model = VGG16(weights='imagenet',
&gt;               include_top=False)
&gt; 
&gt; layer_name = 'block3_conv1' filter_index = 0
&gt; 
&gt; layer_output = model.get_layer(layer_name).output loss =
&gt; K.mean(layer_output[:, :, :, filter_index])
&gt; 
&gt; grads = K.gradients(loss, model.input)[0]


To reproduce the on my fine tuned model, I've used the exact same code, except I obviously changed the model that I imported: 

model = keras.models.load_model(trained_models_dir + 'fine_tuned_model.h5')

...and I also had to index into the nested Model object (my first layer is a Model object as is shown above) to get the 'block2_con1' layer: 

my_Model_object = 'vgg16'
layer_name = 'block3_conv1'
filter_index = 0

layer_output = 
model.get_layer(my_Model_object).get_layer(layer_name).output


any idea why running grads = K.gradients(loss, model.input)[0] on my fine tuned network would result in a "NoneType"??  

Thanks.
SOLVED:  I had to use: grads = K.gradients(loss, model.get_layer(my_Model_object).get_layer('input_1').input)[0] 

instead of just grads = K.gradients(loss, model.input)[0] 

which is confusing because both model.get_layer(my_Model_object).get_layer('input_1').input)[0] 
and 
model.input[0] print the same thing and are of the same type.