#model creation
base_model = applications.inception_v3.InceptionV3(include_top=False,
                                                   weights='imagenet',
                                                   pooling='avg',
                                                   input_shape=(img_rows, img_cols, img_channel))
#Adding custom Layers
add_model = Sequential()
add_model.add(Dense(128, activation='relu',input_shape=base_model.output_shape[1:],
                    kernel_regularizer=regularizers.l2(0.001)))
add_model.add(Dropout(0.60))
add_model.add(Dense(2, activation='sigmoid'))
# creating the final model
model = Model(inputs=base_model.input, outputs=add_model(base_model.output))

model = load_model(os.path.join(ROOT_DIR,'model_1','model_cervigrams_all.h5'))
#remove the last two layers
#remove dense_2
model.layers[-1].pop()
#remove dropout_1
model.layers[-1].pop()
model.summary() # last alyer output shape is : (None, 128), so the removal worked
#predict
model.predict(np.reshape(image,[1,image.shape[0],image.shape[1],3])) #output only two values