Question

我绝对是Python的初学者。最近，我将MNIST手写数字数据库加载到了生成对抗网络中。该程序运行良好，但我想知道如何修改下面的代码，以便可以加载自己的训练数据，JPG文件夹而不是MNIST数据库。有没有一种简单的方法可以用此代码做到这一点？

我知道我需要将图像转换为MNIST格式，但除此之外，我不知道我必须包括和/或编辑哪些行才能加载文件夹。

感谢您的帮助！

import os
import numpy as np
import matplotlib
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
from tqdm import tqdm
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import initializers
os.environ["KERAS_BACKEND"] = "tensorflow"
np.random.seed(10)
random_dim = 100
def load_minst_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = (x_train.astype(np.float32) - 127.5)/127.5
    x_train = x_train.reshape(60000, 784)
    return (x_train, y_train, x_test, y_test)
def get_optimizer():
        return Adam(lr=0.0002, beta_1=0.5)
def get_generator(optimizer):
        generator = Sequential()
        generator.add(Dense(256, input_dim=random_dim, 
kernel_initializer=initializers.RandomNormal(stddev=0.02)))
        generator.add(LeakyReLU(0.2))
        generator.add(Dense(512))
        generator.add(LeakyReLU(0.2))
        generator.add(Dense(1024))
        generator.add(LeakyReLU(0.2))
        generator.add(Dense(784, activation='tanh'))
        generator.compile(loss='binary_crossentropy', optimizer=optimizer)
        return generator
def get_discriminator(optimizer):
        discriminator = Sequential()
        discriminator.add(Dense(1024, input_dim=784, 
kernel_initializer=initializers.RandomNormal(stddev=0.02)))
        discriminator.add(LeakyReLU(0.2))
        discriminator.add(Dropout(0.3))
        discriminator.add(Dense(512))
        discriminator.add(LeakyReLU(0.2))
        discriminator.add(Dropout(0.3))
        discriminator.add(Dense(256))
        discriminator.add(LeakyReLU(0.2))
        discriminator.add(Dropout(0.3))
        discriminator.add(Dense(1, activation='sigmoid'))
        discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
       return discriminator
def get_gan_network(discriminator, random_dim, generator, optimizer):
        discriminator.trainable = False
        gan_input = Input(shape=(random_dim,))
        x = generator(gan_input)
        gan_output = discriminator(x)
        gan = Model(inputs=gan_input, outputs=gan_output)
        gan.compile(loss='binary_crossentropy', optimizer=optimizer)
        return gan
def plot_generated_images(epoch, generator, examples=100, dim=(10, 10), 
figsize=(10, 10)):
        noise = np.random.normal(0, 1, size=[examples, random_dim])
        generated_images = generator.predict(noise)
        generated_images = generated_images.reshape(examples, 28, 28)
        plt.figure(figsize=figsize)
        for i in range(generated_images.shape[0]):
                plt.subplot(dim[0], dim[1], i+1)
                plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
                plt.axis('off')
        plt.tight_layout()
        plt.savefig('gan_generated_image_epoch_%d.png' % epoch)
def train(epochs=1, batch_size=128):
         x_train, y_train, x_test, y_test = load_minst_data()
         batch_count = x_train.shape[0] // batch_size
         adam = get_optimizer()
         generator = get_generator(adam)
         discriminator = get_discriminator(adam)
         gan = get_gan_network(discriminator, random_dim, generator, adam)
         for e in range(1, epochs+1):
                 print ('-'*15, 'Epoch %d' % e, '-'*15)
                 for _ in tqdm(range(batch_count)):
                         noise = np.random.normal(0, 1, size=[batch_size, random_dim])
                         image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
                         generated_images = generator.predict(noise)
                         X = np.concatenate([image_batch, generated_images])
                         y_dis = np.zeros(2*batch_size)
                         y_dis[:batch_size] = 0.9
                         discriminator.trainable = True
                         discriminator.train_on_batch(X, y_dis)
                         noise = np.random.normal(0, 1, size=[batch_size, random_dim])
                         y_gen = np.ones(batch_size)
                         discriminator.trainable = False
                         gan.train_on_batch(noise, y_gen)
                 if e == 1 or e % 20 == 0:
                         plot_generated_images(e, generator)
if __name__ == '__main__':
         train(400, 128)

Answer 1

MNIST是数据集而不是格式。

以下行是代码作者加载数据集的位置：

x_train, y_train, x_test, y_test = load_minst_data()

在：

def train(epochs=1, batch_size=128):
         x_train, y_train, x_test, y_test = load_minst_data()
         ...

这将调用函数：

def load_minst_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = (x_train.astype(np.float32) - 127.5)/127.5
    x_train = x_train.reshape(60000, 784)
    return (x_train, y_train, x_test, y_test)

您可以进行哪些修改以加载数据集。

此外，由于代码期望x_train, y_train, x_test, y_test分别在以下位置包含四个变量：

(x_train, y_train), (x_test, y_test) = mnist.load_data()

我建议使用train_test_split。该函数将帮助您将数据拆分为上述变量。

以下行：

x_train = (x_train.astype(np.float32) - 127.5)/127.5

Normalizes训练样本。

并在：

x_train = x_train.reshape(60000, 784)

作者将60000的样本展平为一个大小为784的向量，以便将它们提供给模型。

P。 S。由于MNIST最初是784个像素，因此有可能重塑为28x28。

您还需要在以下位置修改数据形状或更改input_dim：

discriminator.add(Dense(1024, input_dim=784,kernel_initializer=initializers.RandomNormal(stddev=0.02)))

并且大概每个784的存在。

我应该如何将自己的训练数据加载到这个“生成对抗网络”中？

1 个答案: