这是 main.py:
# pylint: disable=missing-docstringfrom __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport timefrom six.moves import xrange # pylint: disable=redefined-builtinimport tensorflow as tffrom pylab import *import cnn# 作为外部标志的基本模型参数.flags = tf.app.flagsFLAGS = flags.FLAGSflags.DEFINE_float('learning_rate', 0.01, '初始学习率。')flags.DEFINE_integer('max_steps', 2000, '运行训练器的步骤数。')flags.DEFINE_integer('batch_size', 1000, '批量大小。必须能整除数据集大小。')def placeholder_inputs(batch_size): images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, cnn.IMAGE_WIDTH, cnn.IMAGE_HEIGHT, 1)) labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) return images_placeholder, labels_placeholderdef fill_feed_dict(data_set, images_pl, labels_pl): data_set = loadtxt("../dataset/images") images = data_set[:,:115*25] labels_feed = data_set[:,115*25:] images_feed = tf.reshape(images, [batch_size, cnn.IMAGE_WIDTH, cnn.IMAGE_HEIGHT, 1]) feed_dict = { images_pl: images_feed, labels_pl: labels_feed, } return feed_dictdef run_training(): with tf.Graph().as_default(): images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size) logits = cnn.inference(images_placeholder) loss = cnn.loss(logits, labels_placeholder) train_op = cnn.training(loss, FLAGS.learning_rate) eval_correct = cnn.evaluation(logits, labels_placeholder) summary_op = tf.merge_all_summaries() init = tf.initialize_all_variables() saver = tf.train.Saver() sess = tf.Session() summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph) sess.run(init) feed_dict = fill_feed_dict(data_sets.train, images_placeholder, labels_placeholder) # 开始训练循环。 for step in xrange(FLAGS.max_steps): start_time = time.time() _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict) duration = time.time() - start_time # 经常写入摘要并打印概览。 if step % 100 == 0: # 向标准输出打印状态。 print('步骤 %d: 损失 = %.2f (%.3f 秒)' % (step, loss_value, duration)) # 更新事件文件。 summary_str = sess.run(summary_op, feed_dict=feed_dict) summary_writer.add_summary(summary_str, step) summary_writer.flush() predictions = sess.run(logits, feed_dict=feed_dict) savetxt("predictions", predictions)def main(_): run_training()if __name__ == '__main__': tf.app.run()然后，这是 cnn.py:
from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport mathimport tensorflow as tfNUM_OUTPUT = 4IMAGE_WIDTH = 115IMAGE_HEIGHT = 25IMAGE_PIXELS = IMAGE_WIDTH * IMAGE_HEIGHTdef inference(images): # 卷积层 1 with tf.name_scope('conv1'): kernel = tf.Variable(tf.truncated_normal(stddev = 1.0 / math.sqrt(float(IMAGE_PIXELS)), name='weights', shape=[5, 5, 1, 10])) conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID') biases = tf.Variable(tf.constant(0.0, name='biases', shape=[10])) bias = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(bias, name='conv1') # 池化层 1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding='VALID', name='pool1') # 卷积层 2 with tf.name_scope('conv2'): kernel = tf.Variable(tf.truncated_normal(stddev = 1.0 / math.sqrt(float(IMAGE_PIXELS)), name='weights', shape=[5, 5, 10, 20])) conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID') biases = tf.Variable(tf.constant(0.1, name='biases', shape=[20])) bias = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(bias, name='conv2') # 池化层 2 pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding='VALID', name='pool2') # 身份层 with tf.name_scope('identity'): weights = tf.Variable(tf.truncated_normal([11, NUM_OUTPUT], stddev=1.0 / math.sqrt(float(11))), name='weights') biases = tf.Variable(tf.zeros([NUM_OUTPUT], name='biases')) logits = tf.matmul(pool2, weights) + biases return outputdef loss(outputs, labels): rmse = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(targets, outputs))), name="rmse") return rmsedef training(loss, learning_rate): tf.scalar_summary(loss.op.name, loss) optimizer = tf.train.GradientDescentOptimizer(learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step=global_step) return train_op我遇到了这个错误:
Traceback (most recent call last): File "main.py", line 84, in <module> tf.app.run() File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run sys.exit(main(sys.argv)) File "main.py", line 81, in main run_training() File "main.py", line 47, in run_training logits = cnn.inference(images_placeholder) File "/home/andrea/test/python/cnn.py", line 31, in inference conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID') File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 394, in conv2d data_format=data_format, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2262, in create_op set_shapes_for_outputs(ret) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1702, in set_shapes_for_outputs shapes = shape_func(op) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/common_shapes.py", line 230, in conv2d_shape input_shape[3].assert_is_compatible_with(filter_shape[2]) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 108, in assert_is_compatible_with % (self, other))ValueError: Dimensions 1 and 10 are not compatible不明白为什么。维度看起来对我来说都没问题。输入图像是1000个样本，每个样本尺寸为115(宽)x25(高)x1(颜色)。我使用’VALID’作为填充，并且我手动双重检查了计算。不确定不匹配从哪里来。谁能帮我？TensorFlow rc0.9 在 Ubuntu 14.04 上（注意：代码中可能存在我尚未意识到的其他错误，请忽略它们）
回答：
简单打字错误:
在你的第二次卷积中:
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='VALID')将images改为pool1:
conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='VALID')