请帮助我......
我根据教程专家学习使用我自己的数据进行张量流动。
关注我的代码：
#datasets define
NUM_CLASSES = 65535
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*1
#read datasets
with open(FLAGS.train, 'r') as f: # train.txt
train_image = []
train_label = []
num = 0
for line in f:
if num == 500:
break
line = line.rstrip()
l = line.split(',')
print(l[0])
img = cv2.imread(l[0])
img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
train_image.append(img.flatten().astype(np.float32)/255.0)
tmp = np.zeros(NUM_CLASSES)
tmp[int(l[1])] = 1
train_label.append(tmp)
num += 1
train_image = np.asarray(train_image)
train_label = np.asarray(train_label)
train_len = len(train_image)
def inference(images_placeholder, keep_prob):
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
x_images = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 1])
with tf.name_scope('conv1') as scope:
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_images, W_conv1) + b_conv1)
with tf.name_scope('pool1') as scope:
h_pool1 = max_pool_2x2(h_conv1)
with tf.name_scope('conv2') as scope:
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2') as scope:
h_pool2 = max_pool_2x2(h_conv2)
with tf.name_scope('fc1') as scope:
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
with tf.name_scope('fc2') as scope:
W_fc2 = weight_variable([1024, NUM_CLASSES])
b_fc2 = bias_variable([NUM_CLASSES])
with tf.name_scope('softmax') as scope:
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
return y_conv
#learn
with tf.Graph().as_default():
images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS))
labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
keep_prob = tf.placeholder("float")
logits = inference(images_placeholder, keep_prob)
loss_value = loss(logits, labels_placeholder)
train_op = training(loss_value, FLAGS.learning_rate)
print("train_op =", train_op)
acc = accuracy(logits, labels_placeholder)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.initialize_all_variables())
summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph_def)
if train_len % FLAGS.batch_size is 0:
train_batch = train_len/FLAGS.batch_size
else:
train_batch = (train_len/FLAGS.batch_size)+1
print("train_batch = %d",str(train_batch))
for step in range(FLAGS.max_steps):
for i in range(int(train_batch)):
batch = FLAGS.batch_size*i
batch_plus = FLAGS.batch_size*(i+1)
print("batch_plus =", batch_plus)
if batch_plus > train_len: batch_plus = train_len
sess.run(train_op, feed_dict={
images_placeholder: train_image[batch:batch_plus],
labels_placeholder: train_label[batch:batch_plus],
keep_prob: 0.5})
if step % 10 == 0:
train_accuracy = 0.0
for i in range(train_batch):
batch = FLAGS.batch_size*i
batch_plus = FLAGS.batch_size*(i+1)
if batch_plus > train_len: batch_plus = train_len
train_accuracy += sess.run(acc, feed_dict={
images_placeholder: train_image[batch:batch_plus],
labels_placeholder: train_label[batch:batch_plus],
keep_prob: 1.0})
if i is not 0: train_accuracy /= 2.0
#summary_str = sess.run(summary_op, feed_dict={
# images_placeholder: train_image,
# labels_placeholder: train_label,
# keep_prob: 1.0})
#summary_writer.add_summary(summary_str, step)
print("step %d, training accuracy %g",(step, train_accuracy))
if test_len % FLAGS.batch_size is 0:
test_batch = test_len/FLAGS.batch_size
else:
test_batch = (test_len/FLAGS.batch_size)+1
print("test_batch = ",str(test_batch))
test_accuracy = 0.0
for i in range(test_batch):
batch = FLAGS.batch_size*i
batch_plus = FLAGS.batch_size*(i+1)
if batch_plus > train_len: batch_plus = train_len
test_accuracy += sess.run(acc, feed_dict={
images_placeholder: test_image[batch:batch_plus],
labels_placeholder: test_label[batch:batch_plus],
keep_prob: 1.0})
if i is not 0: test_accuracy /= 2.0
print("test accuracy %g",(test_accuracy))
save_path = saver.save(sess, FLAGS.save_model)
但是当我尝试运行它时，我给了我一个错误： ValueError：无法为Tensor＆＃39; Placeholder：0＆＃39;提供形状值（50,2352），其形状为＆＃39;（？，784）＆＃39;
我觉得我忽视了一些小事，但我看不到它。
答案 0 :(得分：0)
编辑：很抱歉，如果您阅读我之前的分析，那就错了。
2352/3 = 784，我认为你保留了3个rgb颜色通道而不是单个像素强度（或者调整大小功能默认输出中有3个颜色通道）。
mnist示例的很多复杂性隐藏在数据加载中，请参阅tensorflow / tensorflow / contrib / learn / python / learn / datasets / mnist.py以更好地理解它并为您的案例制作类似的东西。它们以4D张量[索引，x，y，深度]加载图像，这就是他们如何看待采用索引子集的小批量。
祝你好运！