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I have a Keras model and I want to do some cool visiualizations with it. It's an object recognition network.

So I thought, It would be cool to input a blank image into the network and treat the image as the variable and not the weights, and then train the network to always output an icecream for example.

So I wrote the following code:

#loading the model
model = load_model('model.h5')

#create the input image as a variable
w = tf.Variable(tf.zeros([1,224,224,3]))

#create the flowgraph with the variable input
pred = model.call(inputs=w)

#create the desired output distribution
desired = np.zeros((1000))
desired[928] = 1.0

err = tf.reduce_mean(tf.subtract(pred,desired))
lr = tf.placeholder(dtype=tf.float32, shape=None)

#create an optimizer that can only affect the inital input variable I created
optimizer = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(err, var_list=[w])

#train the network
for i in range(0,100):
    _,cost = sess.run([optimizer,err])
    print(cost)

So I thought the code would work well, but the cost literally doesn't change. It stays in place as if it's entirely unaffected.

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Try explicitly setting the variable as trainable by setting Trainable=True. Also after the network is created, check if the variable is in the list of trainables https://www.tensorflow.org/api_docs/python/tf/trainable_variables

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    $\begingroup$ Didn't work.. no change $\endgroup$ – ronsap123 Oct 6 '18 at 16:17

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