generate-image-to-maximize-output.py

from keras import optimizers
from keras import losses
import numpy as np

input_img_data = np.random.random((1,) + K.int_shape(model.inputs[0])[1:])

input_img = K.variable(input_img_data)
inp = Input(tensor=input_img, batch_shape=input_img_data.shape)
out = model(inp)

y_true = np.zeros((1, 10,))
y_true[:, 5] = 1.0

loss = losses.categorical_crossentropy(y_true, out)
#loss = K.mean(K.square(out - y_true))

opt = optimizers.SGD(nesterov=True, momentum=0.8, lr=10.0)
#opt = optimizers.Adam(lr=0.5)

updates = opt.get_updates([input_img], [], loss)
iterate = K.function([K.learning_phase()], [loss], updates)

for i in range(20):
    loss_value = iterate([False])[0]
    print('Current loss value:', loss_value)

input_img_data = K.get_value(input_img)