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나는 MNIST 자습서를 수행하는 Tensorflow 라이브러리로 공부했습니다. 이제는 내 자신의 데이터로 공부하고 싶었습니다. (이미지 크기 28x28 -> 188x188 및 3 개의 클래스).tensorflow mnist (이미지 크기 변경)
하지만 체중 (모양 매개 변수)을 계산하는 방법을 모르겠습니다.
알고 있습니다 .. 28 * 28 = 784 -> 188 * 188 = 35344 .. 그게 전부입니다. 도와주세요!
[코드 수정]
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}))
[오류 메시지] 내가 위에서 주어진 코드에서
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()