Tensorflow.Keras: How to get gradient for an output class w.r.t a given input? I have implemented and trained a sequential model using tf.keras. Say I am given an input array of size 8X8 and an output [0,1,0,...(rest all 0)].
How to calculate the gradient of the input w.r.t to the given output?
model = ...
output = np.asarray([0, 1, 0, 0, 0, 0, 0, 0, 0, 0])
input = np.random.randn(1,64)
pred = model.predict(input)

gradient = somefunction(model,input,output,pred) 

Is there any way to achieve that?
I am trying to implement a project similar to Google Deep Dreams, so that given a random picture, if I mention a digit, then with each iteration I will update the input with its gradient to make the picture more like the digit that was asked.
I tried to follow other StackOverflow answers and Keras documentation. But as they are not mentioning the input shape, when someone writes input[:,:,something] I am having a hard time translating it to my requirement.
Can you provide a simple generic answer so that given an array as input and an expected output array, one can calculate the gradient of loss w.r.t the input for that exact output.
You can get that from the weight updates, not sure if it is the best approach
-Save the model
-Save the weights of the first layer
-Load the model and Compile the model with SGD w/o momentum
-Set all the weights = that of the previous model
-Train with the input and output i.e. the Array for epoch=1 and batch_size=1
-Get the weights again
-Calculate the respective gradient using two weights of 1st layer
-If Gradient is very small, it might become zero due to matching digits of both the weights
w0 = model.get_weights().copy()
w0_0 = w0[0]

optimizer = tf.keras.optimizers.SGD( learning_rate=0.01, momentum=0.0, nesterov=False, name=&quot;SGD&quot;)
model.compile(optimizer=optimizer, loss='mse')
model.fit(input, output, epochs=1, verbose=1, batch_size=1)

w0_new = model.get_weights().copy()
w0_0_new = w0_new[0]

'''
w_new = w0 - learning_rate * g
g = (w0 - w_new)/learning_rate
'''
grad = (w0_0 - w0_0_new)/0.01