python - CNN、GAN，生成器如何知道它应该绘制哪个类？

Question

我有一个 GAN 网络。生成器正在绘制 mnist 数字。效果很好。但我不明白它是如何知道应该绘制哪个数字的。这是生成器:

def build_generator(latent_size):
    # we will map a pair of (z, L), where z is a latent vector and L is a
    # label drawn from P_c, to image space (..., 1, 28, 28)
    cnn = Sequential()

    cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))
    cnn.add(Dense(128 * 7 * 7, activation='relu'))
    cnn.add(Reshape((128, 7, 7)))

    # upsample to (..., 14, 14)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(256, 5, padding='same',
                   activation='relu',
                   kernel_initializer='glorot_normal'))

    # upsample to (..., 28, 28)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(128, 5, padding='same',
                   activation='relu',
                   kernel_initializer='glorot_normal'))

    # take a channel axis reduction
    cnn.add(Conv2D(1, 2, padding='same',
                   activation='tanh',
                   kernel_initializer='glorot_normal'))

    # this is the z space commonly refered to in GAN papers
    latent = Input(shape=(latent_size, ))

    # this will be our label
    image_class = Input(shape=(1,), dtype='int32')

    cls = Flatten()(Embedding(num_classes, latent_size,
                              embeddings_initializer='glorot_normal')(image_class))

    # hadamard product between z-space and a class conditional embedding
    h = layers.multiply([latent, cls])

    fake_image = cnn(h)

    return Model([latent, image_class], fake_image)

输入是一个潜在数组

noise = np.random.uniform(-1, 1, (batch_size, latent_size))

标签是随机生成的。

所以我的问题是。网络嵌入标签后。它们应该看起来像这样

所以，现在。如果我给网络更多的潜在数组和标签。他将潜在数组(噪声)与(标签的)嵌入相乘:所以我的期望是:

所以网络知道，什么新数组代表什么数字。

但是 np.multiply(noise,embedded_label) 的输出是这样的:

那么网络如何知道应该绘制什么数字？

编辑:

这是完整的代码。它有效。但为什么？代码中的latent_size是100。我的图片中的latent_size是2，因为我想将它们可视化。但我认为，如果我将 2 维空间或 100 维空间中的噪声相乘，这不会改变任何事情。最后，带有标签“1”的新点与带有标签“1”的其他点不接近。其他数字相同(“0”，“1”，“2”，“3”，...)

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Train an Auxiliary Classifier Generative Adversarial Network (ACGAN) on the
MNIST dataset. See https://arxiv.org/abs/1610.09585 for more details.

You should start to see reasonable images after ~5 epochs, and good images
by ~15 epochs. You should use a GPU, as the convolution-heavy operations are
very slow on the CPU. Prefer the TensorFlow backend if you plan on iterating,
as the compilation time can be a blocker using Theano.

Timings:

Hardware           | Backend | Time / Epoch
-------------------------------------------
 CPU               | TF      | 3 hrs
 Titan X (maxwell) | TF      | 4 min
 Titan X (maxwell) | TH      | 7 min

Consult https://github.com/lukedeo/keras-acgan for more information and
example output
"""
from __future__ import print_function

from collections import defaultdict
try:
    import cPickle as pickle
except ImportError:
    import pickle
from PIL import Image

from six.moves import range

import keras.backend as K
from keras.datasets import mnist
from keras import layers
from keras.layers import Input, Dense, Reshape, Flatten, Embedding, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils.generic_utils import Progbar
import numpy as np
import time, os
np.random.seed(1337)

K.set_image_data_format('channels_first')

num_classes = 10


def build_generator(latent_size):
    # we will map a pair of (z, L), where z is a latent vector and L is a
    # label drawn from P_c, to image space (..., 1, 28, 28)
    cnn = Sequential()

    cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))
    cnn.add(Dense(128 * 7 * 7, activation='relu'))
    cnn.add(Reshape((128, 7, 7)))

    # upsample to (..., 14, 14)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(256, 5, padding='same',
                   activation='relu',
                   kernel_initializer='glorot_normal'))

    # upsample to (..., 28, 28)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(128, 5, padding='same',
                   activation='relu',
                   kernel_initializer='glorot_normal'))

    # take a channel axis reduction
    cnn.add(Conv2D(1, 2, padding='same',
                   activation='tanh',
                   kernel_initializer='glorot_normal'))

    # this is the z space commonly refered to in GAN papers
    latent = Input(shape=(latent_size, ))

    # this will be our label
    image_class = Input(shape=(1,), dtype='int32')

    cls = Flatten()(Embedding(num_classes, latent_size,
                              embeddings_initializer='glorot_normal')(image_class))

    # hadamard product between z-space and a class conditional embedding
    h = layers.multiply([latent, cls])

    fake_image = cnn(h)

    return Model([latent, image_class], fake_image)


def build_discriminator():
    # build a relatively standard conv net, with LeakyReLUs as suggested in
    # the reference paper
    cnn = Sequential()

    cnn.add(Conv2D(32, 3, padding='same', strides=2,
                   input_shape=(1, 28, 28)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Conv2D(64, 3, padding='same', strides=1))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Conv2D(128, 3, padding='same', strides=2))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Conv2D(256, 3, padding='same', strides=1))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Flatten())

    image = Input(shape=(1, 28, 28))

    features = cnn(image)

    # first output (name=generation) is whether or not the discriminator
    # thinks the image that is being shown is fake, and the second output
    # (name=auxiliary) is the class that the discriminator thinks the image
    # belongs to.
    fake = Dense(1, activation='sigmoid', name='generation')(features) # fake oder nicht fake
    aux = Dense(num_classes, activation='softmax', name='auxiliary')(features) #welche klasse ist es

    return Model(image, [fake, aux])

if __name__ == '__main__':
    start_time_string = time.strftime("%Y_%m_%d_%H_%M_%S", time.gmtime())
    os.mkdir('history/' + start_time_string)
    os.mkdir('images/' + start_time_string)
    os.mkdir('acgan/' + start_time_string)
    # batch and latent size taken from the paper
    epochs = 50
    batch_size = 100
    latent_size = 100

    # Adam parameters suggested in https://arxiv.org/abs/1511.06434
    adam_lr = 0.00005
    adam_beta_1 = 0.5

    # build the discriminator
    discriminator = build_discriminator()
    discriminator.compile(
        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']
    )

    # build the generator
    generator = build_generator(latent_size)
    generator.compile(optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
                      loss='binary_crossentropy')

    latent = Input(shape=(latent_size, ))
    image_class = Input(shape=(1,), dtype='int32')

    # get a fake image
    fake = generator([latent, image_class])

    # we only want to be able to train generation for the combined model
    discriminator.trainable = False
    fake, aux = discriminator(fake)
    combined = Model([latent, image_class], [fake, aux])

    combined.compile(
        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']
    )

    # get our mnist data, and force it to be of shape (..., 1, 28, 28) with
    # range [-1, 1]
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = (x_train.astype(np.float32) - 127.5) / 127.5
    x_train = np.expand_dims(x_train, axis=1)

    x_test = (x_test.astype(np.float32) - 127.5) / 127.5
    x_test = np.expand_dims(x_test, axis=1)

    num_train, num_test = x_train.shape[0], x_test.shape[0]

    train_history = defaultdict(list)
    test_history = defaultdict(list)

    for epoch in range(1, epochs + 1):
        print('Epoch {}/{}'.format(epoch, epochs))

        num_batches = int(x_train.shape[0] / batch_size)
        progress_bar = Progbar(target=num_batches)

        epoch_gen_loss = []
        epoch_disc_loss = []

        for index in range(num_batches):
            # generate a new batch of noise
            noise = np.random.uniform(-1, 1, (batch_size, latent_size))

            # get a batch of real images
            image_batch = x_train[index * batch_size:(index + 1) * batch_size]
            label_batch = y_train[index * batch_size:(index + 1) * batch_size]

            # sample some labels from p_c
            sampled_labels = np.random.randint(0, num_classes, batch_size)

            # generate a batch of fake images, using the generated labels as a
            # conditioner. We reshape the sampled labels to be
            # (batch_size, 1) so that we can feed them into the embedding
            # layer as a length one sequence
            generated_images = generator.predict(
                [noise, sampled_labels.reshape((-1, 1))], verbose=0)

            x = np.concatenate((image_batch, generated_images))
            y = np.array([1] * batch_size + [0] * batch_size)
            aux_y = np.concatenate((label_batch, sampled_labels), axis=0)

            # see if the discriminator can figure itself out...
            epoch_disc_loss.append(discriminator.train_on_batch(x, [y, aux_y]))

            # make new noise. we generate 2 * batch size here such that we have
            # the generator optimize over an identical number of images as the
            # discriminator
            noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))
            sampled_labels = np.random.randint(0, num_classes, 2 * batch_size)

            # we want to train the generator to trick the discriminator
            # For the generator, we want all the {fake, not-fake} labels to say
            # not-fake
            trick = np.ones(2 * batch_size)

            epoch_gen_loss.append(combined.train_on_batch(
                [noise, sampled_labels.reshape((-1, 1))],
                [trick, sampled_labels]))

            progress_bar.update(index + 1)

        print('Testing for epoch {}:'.format(epoch))

        # evaluate the testing loss here

        # generate a new batch of noise
        noise = np.random.uniform(-1, 1, (num_test, latent_size))

        # sample some labels from p_c and generate images from them
        sampled_labels = np.random.randint(0, num_classes, num_test)
        generated_images = generator.predict(
            [noise, sampled_labels.reshape((-1, 1))], verbose=False)

        x = np.concatenate((x_test, generated_images))
        y = np.array([1] * num_test + [0] * num_test)
        aux_y = np.concatenate((y_test, sampled_labels), axis=0)

        # see if the discriminator can figure itself out...
        discriminator_test_loss = discriminator.evaluate(
            x, [y, aux_y], verbose=False)

        discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)

        # make new noise
        noise = np.random.uniform(-1, 1, (2 * num_test, latent_size))
        sampled_labels = np.random.randint(0, num_classes, 2 * num_test)

        trick = np.ones(2 * num_test)

        generator_test_loss = combined.evaluate(
            [noise, sampled_labels.reshape((-1, 1))],
            [trick, sampled_labels], verbose=False)

        generator_train_loss = np.mean(np.array(epoch_gen_loss), axis=0)

        # generate an epoch report on performance
        train_history['generator'].append(generator_train_loss)
        train_history['discriminator'].append(discriminator_train_loss)

        test_history['generator'].append(generator_test_loss)
        test_history['discriminator'].append(discriminator_test_loss)

        print('{0:<22s} | {1:4s} | {2:15s} | {3:5s}'.format(
            'component', *discriminator.metrics_names))
        print('-' * 65)

        ROW_FMT = '{0:<22s} | {1:<4.2f} | {2:<15.2f} | {3:<5.2f}'
        print(ROW_FMT.format('generator (train)',
                             *train_history['generator'][-1]))
        print(ROW_FMT.format('generator (test)',
                             *test_history['generator'][-1]))
        print(ROW_FMT.format('discriminator (train)',
                             *train_history['discriminator'][-1]))
        print(ROW_FMT.format('discriminator (test)',
                             *test_history['discriminator'][-1]))

        # save weights every epoch
        generator.save_weights(
            'acgan/'+ start_time_string +'/params_generator_epoch_{0:03d}.hdf5'.format(epoch), True)
        discriminator.save_weights(
            'acgan/'+ start_time_string +'/params_discriminator_epoch_{0:03d}.hdf5'.format(epoch), True)

        # generate some digits to display
        noise = np.random.uniform(-1, 1, (100, latent_size))

        sampled_labels = np.array([
            [i] * num_classes for i in range(num_classes)
        ]).reshape(-1, 1)

        # get a batch to display
        generated_images = generator.predict(
            [noise, sampled_labels], verbose=0)

        # arrange them into a grid
        img = (np.concatenate([r.reshape(-1, 28)
                               for r in np.split(generated_images, num_classes)
                               ], axis=-1) * 127.5 + 127.5).astype(np.uint8)

        Image.fromarray(img).save(
            'images/'+ start_time_string +'/plot_epoch_{0:03d}_generated.png'.format(epoch))

    pickle.dump({'train': train_history, 'test': test_history},
                open('history/'+ start_time_string +'/acgan-history.pkl', 'wb'))

Answer 1

您的噪音太大，并且具有负值。

您不应该将噪声相乘，而是将其相加(并使其小很多)。通过+1和-1相乘，你可以完全改变输入。这就是 reality 中出现完全分散的图像的原因。 .

如果即使使用奇怪的分散输入，模型仍然能够识别您想要的数字，那么它可能使用的潜在向量的某些维度超过其实际值。

如果仔细观察散点图，它有一些有趣的模式，例如:

• 0 - 垂直线。它仅使用某个维度为零。
• 4 - 另一条垂直线。
• 7 - 一条水平线。
• 3 - 似乎是对角线，不确定。

如果我们可以看到一个模式(即使在隐藏实际 100 个维度的 2D 图中)，模型也可以看到一个模式。如果我们能看到所有 100 个维度，这种模式可能会非常明显。

因此，您的嵌入可能会通过消除某些维度组中为零的随机因素来对狂野的随机因素进行补偿。这使得直线遵循特定的轴。零维度与不同维度的某些组合可以识别标签。

示例:

• 对于标签 0，您的嵌入可能会创建 [0,0,0,0,1,1,1,1,1,1,1,1,...]
• 对于标签 1，它可能会创建 [1,1,1,1,0,0,0,0,1,1,1,1,1....]
• 对于标签 2，它可能正在创建 [1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1...]<

那么随机因子将永远不会改变这些零，并且模型可以通过检查示例中的四个零组成的组来识别数字。

当然，这只是一种假设...模型可能有许多其他可能的方法来解决随机因素...但如果存在一种，就足以表明模型可以找到它。