Question

我一直在使用Tensorflow库进行MNIST教程。现在我想用我自己的数据学习。（图像大小28x28 - > 188x188和3个类）。

但我不知道如何计算重量（形状参数？）。

我知道.. 28 * 28 = 784 - ＆gt; 188 * 188 = 35344 ..那就是它。救救我！

[代码修改]

sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 35344])
y_ = tf.placeholder(tf.float32, shape=[None, 3])

W = tf.Variable(tf.zeros([35344,3]))
b = tf.Variable(tf.zeros([3]))

sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x,W) + b)

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')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1,188,188,1])
#x_image = tf.reshape(x, [-1,28,28,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# Second layer
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)
h_pool2 = max_pool_2x2(h_conv2)

# Densely Connected Layer

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)

# Dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# Readout layer
W_fc2 = weight_variable([1024, 3])
b_fc2 = bias_variable([3])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# Train and Evaluate the Model

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
for i in range(2000):
  batch = mnist.train.next_batch(35)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

[错误讯息]

Traceback (most recent call last):
  File "class3.py", line 253, in <module>
    x:batch[0], y_: batch[1], keep_prob: 1.0})
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 555, in eval
    return _eval_using_default_session(self, feed_dict, self.graph, session)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3498, in _eval_using_default_session
    return session.run(tensors, feed_dict)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 372, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 636, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 708, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 728, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: Input to reshape is a tensor with 4948160 values, but the requested shape requires a multiple of 3136
     [[Node: Reshape_1 = Reshape[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](MaxPool_1, Reshape_1/shape)]]
Caused by op u'Reshape_1', defined at:
  File "class3.py", line 229, in <module>
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1383, in reshape
    name=name)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2260, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1230, in __init__
    self._traceback = _extract_stack()

Answer 1

从上面给出的代码我发现了一些错误：

如果您想研究自己的图像数据集188x188，则需要从数据集中提供图像而不是批处理= mnist.train.next_batch（35）

您需要阅读tensorflow网站上的一些示例，以了解如何将数据读入图表 https://www.tensorflow.org/versions/r0.11/how_tos/reading_data/index.html

也可能不需要x_image = tf.reshape（x，[ - 1,188,188,1]），具体取决于您如何读取数据。每个图像在tensorflow.examples上的预处理的mnist数据集具有形状（784，），这就是为什么我们需要用（-1,28,28,1）重新形成它，这将形状的张量（784，）转换为带有1个通道的2d图像28x28

tensorflow mnist（更改图像大小）

1 个答案: