我试图为孟加拉字母制作角色识别分类器。图像大小为50x50。共有50个班级。使用以下CNN模型进行训练，但我遇到此错误：" ValueReror：验证大小应在0到0之间。接收到：5000。"我如何解决这个问题？
模型
# Python 3.6.0
# tensorflow 1.1.0
import os
import os.path as path
import tensorflow as tf
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib
from tensorflow.examples.tutorials.mnist import input_data
MODEL_NAME = 'mnist_convnet'
NUM_STEPS = 3000
BATCH_SIZE = 16
def model_input(input_node_name, keep_prob_node_name):
x = tf.placeholder(tf.float32, shape=[None, 50*50], name=input_node_name)
keep_prob = tf.placeholder(tf.float32, name=keep_prob_node_name)
y_ = tf.placeholder(tf.float32, shape=[None, 50])
return x, keep_prob, y_
def build_model(x, keep_prob, y_, output_node_name):
x_image = tf.reshape(x, [-1, 50, 50, 1])
# 50*50*1
conv1 = tf.layers.conv2d(x_image, 64, 3, 1, 'same', activation=tf.nn.relu)
# 50*50*64
pool1 = tf.layers.max_pooling2d(conv1, 2, 2, 'same')
# 14*14*64
conv2 = tf.layers.conv2d(pool1, 128, 3, 1, 'same', activation=tf.nn.relu)
# 14*14*128
pool2 = tf.layers.max_pooling2d(conv2, 2, 2, 'same')
# 7*7*128
conv3 = tf.layers.conv2d(pool2, 256, 3, 1, 'same', activation=tf.nn.relu)
# 7*7*256
pool3 = tf.layers.max_pooling2d(conv3, 2, 2, 'same')
# 4*4*256
flatten = tf.reshape(pool3, [-1, 4*4*256])
fc = tf.layers.dense(flatten, 1024, activation=tf.nn.relu)
dropout = tf.nn.dropout(fc, keep_prob)
logits = tf.layers.dense(dropout, 50)
outputs = tf.nn.softmax(logits, name=output_node_name)
# loss
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=logits))
# train step
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)
# accuracy
correct_prediction = tf.equal(tf.argmax(outputs, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("loss", loss)
tf.summary.scalar("accuracy", accuracy)
merged_summary_op = tf.summary.merge_all()
return train_step, loss, accuracy, merged_summary_op
def train(x, keep_prob, y_, train_step, loss, accuracy,
merged_summary_op, saver):
print("training start...")
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
tf.train.write_graph(sess.graph_def, 'out',
MODEL_NAME + '.pbtxt', True)
# op to write logs to Tensorboard
summary_writer = tf.summary.FileWriter('logs/',
graph=tf.get_default_graph())
for step in range(NUM_STEPS):
batch = mnist.train.next_batch(BATCH_SIZE)
if step % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print('step %d, training accuracy %f' % (step, train_accuracy))
_, summary = sess.run([train_step, merged_summary_op],
feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
summary_writer.add_summary(summary, step)
saver.save(sess, 'out/' + MODEL_NAME + '.chkp')
test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images,
y_: mnist.test.labels,
keep_prob: 1.0})
print('test accuracy %g' % test_accuracy)
print("training finished!")
def export_model(input_node_names, output_node_name):
freeze_graph.freeze_graph('out/' + MODEL_NAME + '.pbtxt', None, False,
'out/' + MODEL_NAME + '.chkp', output_node_name, "save/restore_all",
"save/Const:0", 'out/frozen_' + MODEL_NAME + '.pb', True, "")
input_graph_def = tf.GraphDef()
with tf.gfile.Open('out/frozen_' + MODEL_NAME + '.pb', "rb") as f:
input_graph_def.ParseFromString(f.read())
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def, input_node_names, [output_node_name],
tf.float32.as_datatype_enum)
with tf.gfile.FastGFile('out/opt_' + MODEL_NAME + '.pb', "wb") as f:
f.write(output_graph_def.SerializeToString())
print("graph saved!")
def main():
if not path.exists('out'):
os.mkdir('out')
input_node_name = 'input'
keep_prob_node_name = 'keep_prob'
output_node_name = 'output'
x, keep_prob, y_ = model_input(input_node_name, keep_prob_node_name)
train_step, loss, accuracy, merged_summary_op = build_model(x, keep_prob, y_, output_node_name)
saver = tf.train.Saver()
train(x, keep_prob, y_, train_step, loss, accuracy, merged_summary_op, saver)
export_model([input_node_name, keep_prob_node_name], output_node_name)
if __name__ == '__main__':
main()错误
ValueError Traceback (most recent call last)
<ipython-input-2-2015e0ea466d> in <module>()
136
137 if __name__ == '__main__':
--> 138 main()
<ipython-input-2-2015e0ea466d> in main()
131 saver = tf.train.Saver()
132
--> 133 train(x, keep_prob, y_, train_step, loss, accuracy, merged_summary_op, saver)
134
135 export_model([input_node_name, keep_prob_node_name], output_node_name)
<ipython-input-2-2015e0ea466d> in train(x, keep_prob, y_, train_step, loss, accuracy, merged_summary_op, saver)
67 print("training start...")
68
---> 69 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
70
71 init_op = tf.global_variables_initializer()
/anaconda3/envs/nlpTFnltk/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py in read_data_sets(train_dir, fake_data, one_hot, dtype, reshape, validation_size)
247 raise ValueError(
248 'Validation size should be between 0 and {}. Received: {}.'
--> 249 .format(len(train_images), validation_size))
250
251 validation_images = train_images[:validation_size]
ValueError: Validation size should be between 0 and 0. Received: 5000.您正在使用MNIST教程代码，该代码从此处调用read_data_sets；请注意，5000的validation_size来自该函数的默认参数。期望从以下文件获取数据：
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
通常，如果没有找到这些文件，它将尝试下载这些文件，但是您获得的validation_size of 0表明它不是这样做的。无论如何，这对您无济于事，因为您不想使用MNIST数据。
即使您将火车和测试文件重命名以匹配上述文件名，您的代码也无法正常工作，因为MNIST代码也在调用extract_labels，该extract_labels在您希望为50时具有默认参数num_classes=10。可能是要完全摆脱MNIST的完全导入，并阅读如何设置输入管道；与您已经完成的事情相比，这并不困难。