quick example

import keras
from keras import backend as K
import keras.models
#---------------------------------------------------------------------------------------------------
model = keras.models.Sequential()
model.add(keras.layers.Convolution2D(8, 5, 5,  W_constraint = keras.constraints.maxnorm(), input_shape=(1, 100, 100)))
# Commenting out the next line reemoves the MissingInputError
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(2))

for k in model.layers:
    if type(k) is keras.layers.Dropout:
        model.layers.remove(k)

print(model.layers)

input_img = model.layers[0].input
layer_output = model.layers[-1].output   
from keras import backend as K
loss = K.mean(layer_output)
grads = K.gradients(loss, input_img)[0]        
iterate = K.function([input_img], [loss, grads])   # Will blow up on this line.