Question

对于我的学士论文，我必须在 Tensorflow 2.4.0 中构建一个 DC-GAN。我已经创建了自己的数据集并对其进行了预处理，预处理后的数据很好，可以用来训练模型。现在我想用我的数据集训练我的简单 DC-GAN，但这不起作用。

我的问题是，为什么我的数据集不适用于模型？或者我必须对模型进行哪些更改才能使数据集能够使用它？

我收到以下错误：ValueError: Layer sequential_1 expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'images:0' shape=(416, 416, 3) dtype=float32>, <tf.Tensor 'images_1:0' shape=(100, 5) dtype=float32>]

我的数据集如下所示：<MapDataset shapes: ((416, 416, 3), (None, 5)), types: (tf.float32, tf.float32)>

似乎我的模型在我的数据集形状上挣扎，但我尝试了多种方法来重塑我的数据集，但我收到了不同的错误消息。

以下是我的算法摘要：

从自定义函数加载预处理数据集
定义并生成用于图像处理的生成器
定义并生成用于图像分类的鉴别器
训练两个模型

这是我的代码：

import tensorflow as tf
from tensorflow.keras import layers
import time
from dataHandler.dataLoader.loadData import load_dataset
from dataHandler.preProcessData.configuratorPath import tfRecordPath, classesPath

BUFFER_SIZE = 60000
BATCH_SIZE = 256
EPOCHS = 50
noise_dim = 100
num_examples_to_generate = 16
# return Dataset in form <MapDataset shapes: ((416, 416, 3), (None, 5)), types: (tf.float32, tf.float32)>
train_dataset=load_dataset(tfRecordPath,classesPath,size=416)

def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size

    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)

    return model

generator = make_generator_model()

def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                                     input_shape=[28, 28, 1]))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))

    model.add(layers.Flatten())
    model.add(layers.Dense(1))

    return model

discriminator = make_discriminator_model()

# This method returns a helper function to compute cross entropy loss
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)

generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)


# We will reuse this seed overtime (so it's easier)
# to visualize progress in the animated GIF)
seed = tf.random.normal([num_examples_to_generate, noise_dim])

# Notice the use of `tf.function`
# This annotation causes the function to be "compiled".
@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, noise_dim])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
      generated_images = generator(noise, training=True)

      real_output = discriminator(images, training=True)
      fake_output = discriminator(generated_images, training=True)

      gen_loss = generator_loss(fake_output)
      disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

def train(dataset, epochs):
  for epoch in range(epochs):
    start = time.time()

    for image_batch in dataset:
      train_step(image_batch)

    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))

train(train_dataset, EPOCHS)

如何在 tensorflow 中使用模型训练自定义数据集？

0 个答案: