尝试运行此代码，这是我自己编写的函数：
def next_batch(batch_size):
label = [0, 1, 0, 0, 0]
X = []
Y = []
for i in range(0, batch_size):
rand = random.choice(os.listdir(mnist))
rand = mnist + rand
img = cv2.imread(str(rand), 0)
img = np.array(img)
img = img.ravel()
X.append(img)
Y.append(label)
X = np.array(X)
Y = np.array(Y)
return X, Y
然后，我想使用X和Y数组进行网络训练。 我使用以下代码运行它：（主要是 def train（train_model）的底部全部消失
def train(train_model=True):
"""
Used to train the autoencoder by passing in the necessary inputs.
:param train_model: True -> Train the model, False -> Load the latest trained model and show the image grid.
:return: does not return anything
"""
with tf.variable_scope(tf.get_variable_scope()):
encoder_output = encoder(x_input)
# Concat class label and the encoder output
decoder_input = tf.concat([y_input, encoder_output], 1)
decoder_output = decoder(decoder_input)
with tf.variable_scope(tf.get_variable_scope()):
d_real = discriminator(real_distribution)
d_fake = discriminator(encoder_output, reuse=True)
with tf.variable_scope(tf.get_variable_scope()):
decoder_image = decoder(manual_decoder_input, reuse=True)
# Autoencoder loss
autoencoder_loss = tf.reduce_mean(tf.square(x_target - decoder_output))
# Discriminator Loss
dc_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_real), logits=d_real))
dc_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake))
dc_loss = dc_loss_fake + dc_loss_real
# Generator loss
generator_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake))
all_variables = tf.trainable_variables()
dc_var = [var for var in all_variables if 'dc_' in var.name]
en_var = [var for var in all_variables if 'e_' in var.name]
# Optimizers
autoencoder_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(autoencoder_loss)
discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(dc_loss, var_list=dc_var)
generator_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(generator_loss, var_list=en_var)
init = tf.global_variables_initializer()
# Reshape images to display them
input_images = tf.reshape(x_input, [-1, 368, 432, 1])
generated_images = tf.reshape(decoder_output, [-1, 368, 432, 1])
# Tensorboard visualization
tf.summary.scalar(name='Autoencoder Loss', tensor=autoencoder_loss)
tf.summary.scalar(name='Discriminator Loss', tensor=dc_loss)
tf.summary.scalar(name='Generator Loss', tensor=generator_loss)
tf.summary.histogram(name='Encoder Distribution', values=encoder_output)
tf.summary.histogram(name='Real Distribution', values=real_distribution)
tf.summary.image(name='Input Images', tensor=input_images, max_outputs=10)
tf.summary.image(name='Generated Images', tensor=generated_images, max_outputs=10)
summary_op = tf.summary.merge_all()
# Saving the model
saver = tf.train.Saver()
step = 0
with tf.Session() as sess:
if train_model:
tensorboard_path, saved_model_path, log_path = form_results()
sess.run(init)
writer = tf.summary.FileWriter(logdir=tensorboard_path, graph=sess.graph)
for i in range(n_epochs):
# print(n_epochs)
n_batches = int(10000 / batch_size)
print("------------------Epoch {}/{}------------------".format(i, n_epochs))
for b in range(1, n_batches+1):
# print("In the loop")
z_real_dist = np.random.randn(batch_size, z_dim) * 5.
batch_x, batch_y = next_batch(batch_size)
# print("Created the batches")
sess.run(autoencoder_optimizer, feed_dict={x_input: batch_x, x_target: batch_x, y_input: batch_y})
print("batch_x", batch_x)
print("x_input:", x_input)
print("x_target:", x_target)
print("y_input:", y_input)
sess.run(discriminator_optimizer,
feed_dict={x_input: batch_x, x_target: batch_x, real_distribution: z_real_dist})
sess.run(generator_optimizer, feed_dict={x_input: batch_x, x_target: batch_x})
# print("setup the session")
if b % 50 == 0:
a_loss, d_loss, g_loss, summary = sess.run(
[autoencoder_loss, dc_loss, generator_loss, summary_op],
feed_dict={x_input: batch_x, x_target: batch_x,
real_distribution: z_real_dist, y_input: batch_y})
writer.add_summary(summary, global_step=step)
print("Epoch: {}, iteration: {}".format(i, b))
print("Autoencoder Loss: {}".format(a_loss))
print("Discriminator Loss: {}".format(d_loss))
print("Generator Loss: {}".format(g_loss))
with open(log_path + '/log.txt', 'a') as log:
log.write("Epoch: {}, iteration: {}\n".format(i, b))
log.write("Autoencoder Loss: {}\n".format(a_loss))
log.write("Discriminator Loss: {}\n".format(d_loss))
log.write("Generator Loss: {}\n".format(g_loss))
step += 1
saver.save(sess, save_path=saved_model_path, global_step=step)
else:
# Get the latest results folder
all_results = os.listdir(results_path)
all_results.sort()
saver.restore(sess, save_path=tf.train.latest_checkpoint(results_path + '/' +
all_results[-1] + '/Saved_models/'))
generate_image_grid(sess, op=decoder_image)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Autoencoder Train Parameter")
parser.add_argument('--train', '-t', type=bool, default=True,
help='Set to True to train a new model, False to load weights and display image grid')
args = parser.parse_args()
train(train_model=args.train)
获取此错误消息：
回溯（最近通话最近）： 文件“ /Users/frederikcalsius/Desktop/adv/supervised_adversarial_autoencoder.py”，第290行，在 火车（train_model = args.train） 火车上的文件“ /Users/frederikcalsius/Desktop/adv/supervised_adversarial_autoencoder.py”，第249行 sess.run（autoencoder_optimizer，feed_dict = {x_input：batch_x，x_target：batch_x，y_input：batch_y}） 运行中的文件“ /Users/frederikcalsius/Library/Python/3.7/lib/python/site-packages/tensorflow/python/client/session.py”，第929行 run_metadata_ptr） 文件“ /Users/frederikcalsius/Library/Python/3.7/lib/python/site-packages/tensorflow/python/client/session.py”，行1121，在_run中 np_val = np.asarray（subfeed_val，dtype = subfeed_dtype） 格式为“ /Users/frederikcalsius/Library/Python/3.7/lib/python/site-packages/numpy/core/numeric.py”的第538行 返回数组（a，dtype，copy = False，order = order）
ValueError：设置具有序列的数组元素。
以退出代码1完成的过程
我真的没有得到这个错误。有人可以帮我吗？