在为ModelNet10设置3D-GAN时，遇到了以下错误信息：
InvalidArgumentError: 重塑操作的输入是一个包含27000个值的张量，但请求的形状需要810000 [Op:Reshape]
在我看来，批次没有正确创建，因此张量的形状无效。我尝试了不同的方法，但无法正确设置批次。我非常感谢任何关于如何清理我的代码的建议！提前感谢！
import timeimport numpy as npimport tensorflow as tfnp.random.seed(1)from tensorflow.keras import layersfrom IPython import display# 加载数据modelnet_path = '/modelnet10.npz'data = np.load(modelnet_path)X, Y = data['X_train'], data['y_train']X_test, Y_test = data['X_test'], data['y_test']X = X.reshape(X.shape[0], 30, 30, 30, 1).astype('float32')#超参数BUFFER_SIZE = 3991BATCH_SIZE = 30LEARNING_RATE = 4e-4BETA_1 = 5e-1EPOCHS = 100#用于图像生成的随机种子n_examples = 16noise_dim = 100seed = tf.random.normal([n_examples, noise_dim])train_dataset = tf.data.Dataset.from_tensor_slices(X).batch(BATCH_SIZE)# 构建网络def make_discriminator_model(): model = tf.keras.Sequential() model.add(layers.Reshape((30, 30, 30, 1), input_shape=(30, 30, 30))) model.add(layers.Conv3D(16, 6, strides=2, activation='relu')) model.add(layers.Conv3D(64, 5, strides=2, activation='relu')) model.add(layers.Conv3D(64, 5, strides=2, activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(10)) return modeldiscriminator = make_discriminator_model()def make_generator_model(): model = tf.keras.Sequential() model.add(layers.Dense(15*15*15*128, use_bias=False,input_shape=(100,))) model.add(layers.BatchNormalization()) model.add(layers.ReLU()) model.add(layers.Reshape((15,15,15,128))) model.add(layers.Conv3DTranspose(64, (5,5,5), strides=(1,1,1), padding='valid', use_bias=False)) model.add(layers.BatchNormalization()) model.add(layers.ReLU()) model.add(layers.Conv3DTranspose(32, (5,5,5), strides=(2,2,2), padding='valid', use_bias=False, activation='tanh')) return modelgenerator = make_generator_model()#优化器和损失函数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_lossdef generator_loss(fake_output): return cross_entropy(tf.ones_like(fake_output), fake_output)optimizer = tf.keras.optimizers.Adam(lr=LEARNING_RATE, beta_1=BETA_1)#训练def train_step(shapes): noise = tf.random.normal([BATCH_SIZE, noise_dim]) with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape: generated_shapes = generator(noise, training=True) real_output = discriminator(shapes, training=True) fake_output = discriminator(generated_shapes, training=True) gen_loss = generator_loss(fake_output) disc_loss = discriminator_loss(real_output, fake_output) gen_gradients = gen_tape.gradient(gen_loss, generator.trainable_variables) disc_gradients = disc_tape.gradient(disc_loss, discriminator.trainable_variables) optimizer.apply_gradients(zip(gen_gradients, generator.trainable_variables)) optimizer.apply_gradients(zip(disc_gradients, discriminator.trainable_variables))def train(dataset, epochs): for epoch in range(epochs): start = time.time() for shape_batch in dataset: train_step(shape_batch) display.clear_output(wait=True) print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start)) display.clear_output(wait=True) train(X_test, EPOCHS)回答：
X_test只是一个列表，所以在你的训练循环中，只有一个样本（30*30*30=27000）被输入到模型中，但模型本身需要30（批次大小）* 30 * 30 * 30=810000。
modelnet_path = '/modelnet10.npz'data = np.load(modelnet_path)X, Y = data['X_train'], data['y_train']X_test, Y_test = data['X_test'], data['y_test']X = X.reshape(X.shape[0], 30, 30, 30, 1).astype('float32')...train_dataset = tf.data.Dataset.from_tensor_slices(X).batch(BATCH_SIZE)...def train(dataset, epochs): for epoch in range(epochs): start = time.time() for shape_batch in dataset: train_step(shape_batch) display.clear_output(wait=True) print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start)) display.clear_output(wait=True) train(X_test, EPOCHS)考虑使用你创建的train_dataset进行训练，或者将X_test生成为tf.dataset。
train(train_dataset , EPOCHS)