Keras 实现一个简单GAN

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))