Keras 实现一个简单GAN
代码中需提供：
Loss Function 参见Keras 或者 Tensorflow 文档
model_param_matrix 反向调整的模型参数/参数矩阵
epoch 迭代轮数
W 以及调整的方式
import numpy as np
from keras.preprocessing import image
from keras.applications import inception_v3
from keras import backend as K
from PIL import Image
import tensorflow as tf #Prepare the input
# Load the image
img = image.load_img("name.png", target_size=(299, 299))
original_image = image.img_to_array(img) # Scale the image so all pixel intensities are between [-1, 1] as the model expects
original_image /= 255.
original_image -= 0.5
original_image *= 2. # Add a 4th dimension for batch size (as Keras expects)
original_image = np.expand_dims(original_image, axis=0) # Create a copy of the input image to process
processed_image = np.copy(original_image) # How much to update the hacked image in each iteration
learning_rate = 0.01 # Define the cost function.
cost_function = #Loss Function# # We'll ask Keras to calculate the gradient based on the input image and the currently predicted class
#BP
gradient_function = K.gradients(cost_function, model_param_matrix)[0] # Create a Keras function that we can call to calculate the current cost and gradient
grab_cost_and_gradients_from_model = K.function([model_input_layer, K.learning_phase()],
[cost_function, gradient_function]) cost = 0.0 epoch = 1000
for iter in range(epoch):
# Check how close the image is to our target class and grab the gradients we
# can use to push it one more step in that direction.
# Note: It's really important to pass in '0' for the Keras learning mode here!
# Keras layers behave differently in prediction vs. train modes! cost, gradients = grab_cost_and_gradients_from_model([processed_image, 0]) # Adjust the params according to gradients (GD)
W -= gradients * learning_rate print("Model's predicted likelihood that the image is a XXX: {:.8}%".format(cost * 100))