tensorflow - 无法计算 ConcatV2，因为输入 #1（从零开始）应该是一个浮点张量，但它是一个双张量 [Op:ConcatV2] 名称：concat

问题描述

导入库

%matplotlib inline
import tensorflow as tf
from tensorflow import keras
import numpy as np
import plot_utils
import matplotlib.pyplot as plt
from tqdm import tqdm
print('Tensorflow version:', tf.__version__)

任务 3：创建批量训练数据

batch_size = 32
# This dataset fills a buffer with buffer_size elements, 
#then randomly samples elements from this buffer, replacing the selected elements with new elements.
dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(1000)
#Combines consecutive elements of this dataset into batches.
dataset = dataset.batch(batch_size, drop_remainder=True).prefetch(1)
#Creates a Dataset that prefetches elements from this dataset


print(dataset)
output:<PrefetchDataset shapes: (32, 32, 32, 3), types: tf.float64>

任务 4：为 DCGAN 构建生成器网络

num_features = 100
generator = keras.models.Sequential([
    keras.layers.Dense(256*4*4, input_shape=[num_features]),
    keras.layers.Reshape([4,4,256]),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2DTranspose(128, (4,4), (2,2), padding="same", activation="selu"),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2DTranspose(128, (4,4), (2,2), padding="same", activation="selu"),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2DTranspose(128, (4,4), (2,2), padding="same", activation="selu"),
    keras.layers.BatchNormalization(),
    keras.layers.Conv2DTranspose(3, (3,3), padding="same", activation="tanh"),
])

import numpy as np
import matplotlib.pyplot as plt

def show(images, n_cols=None):
    n_cols = n_cols or len(images)
    n_rows = (len(images) - 1) // n_cols + 1
    if images.shape[-1] == 1:
        images = np.squeeze(images, axis=-1)
    plt.figure(figsize=(n_cols, n_rows))
    for index, image in enumerate(images):
        plt.subplot(n_rows, n_cols, index + 1)
        plt.imshow(image, cmap="binary")
        plt.axis("off")

noise = tf.random.normal(shape=[1, num_features])
generated_images = generator(noise, training=False)
show(generated_images,1)

任务 5：为 DCGAN 构建鉴别器网络

discriminator = keras.models.Sequential([
    keras.layers.Conv2D(64, (3,3), (2,2), padding="same", input_shape=[32,32,3]),
    keras.layers.LeakyReLU(0.2),
    keras.layers.Dropout(0.3),
    keras.layers.Conv2D(128, (3,3), (2,2), padding="same"),
    keras.layers.LeakyReLU(0.2),
    keras.layers.Dropout(0.3),
    keras.layers.Conv2D(256, (3,3), (2,2), padding="same"),
    keras.layers.LeakyReLU(0.2),
    keras.layers.Dropout(0.3),
    keras.layers.Flatten(),
    keras.layers.Dense(1, activation='sigmoid')
])

decision = discriminator(generated_images)
print(decision)
output:tf.Tensor([[0.5006197]], shape=(1, 1), dtype=float32)

任务 6：编译深度卷积生成对抗网络 (DCGAN)

discriminator.compile(loss="binary_crossentropy", optimizer="rmsprop")
discriminator.trainable = False
gan = keras.models.Sequential([generator, discriminator])
gan.compile(loss="binary_crossentropy", optimizer="rmsprop")


from IPython import display
from tqdm import tqdm
seed = tf.random.normal(shape=[batch_size, 100])

任务 7：定义培训程序

from tqdm import tqdm
def train_dcgan(gan, dataset, batch_size, num_features, epochs=5):
    generator, discriminator = gan.layers
    for epoch in tqdm(range(epochs)):
        print("Epoch {}/{}".format(epoch + 1, epochs))
        for X_batch in dataset:
            noise = tf.random.normal(shape=[batch_size, num_features])
            generated_images = generator(noise)
            X_fake_and_real = tf.concat([generated_images, X_batch], axis=0)
            y1 = tf.constant([[0.]] * batch_size + [[1.]] * batch_size)
            discriminator.trainable = True
            discriminator.train_on_batch(X_fake_and_real, y1)
            noise = tf.random.normal(shape=[batch_size, num_features])
            y2 = tf.constant([[1.]] * batch_size)
            discriminator.trainable = False
            gan.train_on_batch(noise, y2)
            # Produce images for the GIF as we go
        display.clear_output(wait=True)
        generate_and_save_images(generator, epoch + 1, seed)
        
    display.clear_output(wait=True)
    generate_and_save_images(generator, epochs, seed)


## Source https://www.tensorflow.org/tutorials/generative/dcgan#create_a_gif
def generate_and_save_images(model, epoch, test_input):
  # Notice `training` is set to False.
  # This is so all layers run in inference mode (batchnorm).
  predictions = model(test_input, training=False)

  fig = plt.figure(figsize=(10,10))

  for i in range(25):
      plt.subplot(5, 5, i+1)
      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='binary')
      plt.axis('off')

  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
  plt.show()

任务 8：训练 DCGAN

x_train_dcgan = x_train.reshape(-1, 32,32,3) * 2. - 1.

batch_size = 32
dataset = tf.data.Dataset.from_tensor_slices(x_train_dcgan)
dataset = dataset.shuffle(1000)
dataset = dataset.batch(batch_size, drop_remainder=True).prefetch(1)

这是主要问题

%%time
train_dcgan(gan, dataset, batch_size, num_features, epochs=10)**
output:
    7             noise = tf.random.normal(shape=[batch_size, num_features])
      8             generated_images = generator(noise)
----> 9             X_fake_and_real = tf.concat([generated_images, X_batch], axis=0)
     10             y1 = tf.constant([[0.]] * batch_size + [[1.]] * batch_size)
     11             discriminator.trainable = True
cannot compute ConcatV2 as input #1(zero-based) was expected to be a float tensor but is a double tensor [Op:ConcatV2] name: concat

这是 Cifar10 DCGAN 我真的不明白这个错误以及如何修复它。

标签： tensorflowdcgan

tensorflow - 无法计算 ConcatV2，因为输入 #1（从零开始）应该是一个浮点张量，但它是一个双张量 [Op:ConcatV2] 名称：concat

问题描述

解决方案

推荐阅读