5
私は非常に小さなデータセット、わずか50画像しか持っていないと言います。私はRed Pillでチュートリアルからコードを再使用しますが、トレーニングの各バッチ内の画像の同じセットにランダム変換を適用したい、明るさにランダムな変化を言う、などコントラスト私はただ一つの関数を追加しました：Tensorflow Convolutionニューラルネット - 小規模なデータセットを使用したトレーニングで画像にランダムな変更を加える
def preprocessImages(x):
retValue = numpy.empty_like(x)
for i in range(50):
image = x[i]
image = tf.reshape(image, [28,28,1])
image = tf.image.random_brightness(image, max_delta=63)
#image = tf.image.random_contrast(image, lower=0.2, upper=1.8)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_whitening(image)
float_image_Mat = sess.run(float_image)
retValue[i] = float_image_Mat.reshape((28*28))
return retValue
小変更：
batch = mnist.train.next_batch(50)
for i in range(1000):
#batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:preprocessImages(batch[0]), y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: preprocessImages(batch[0]), y_: batch[1], keep_prob: 0.5})
最初の反復は、その後、それがクラッシュし、成功している：
step 0, training accuracy 0.02
W tensorflow/core/common_runtime/executor.cc:1027] 0x117e76c0 Compute status: Invalid argument: ReluGrad input is not finite. : Tensor had NaN values
[[Node: gradients_4/Relu_12_grad/Relu_12/CheckNumerics = CheckNumerics[T=DT_FLOAT, message="ReluGrad input is not finite.", _device="/job:localhost/replica:0/task:0/cpu:0"](add_16)]]
W tensorflow/core/common_runtime/executor.cc:1027] 0x117e76c0 Compute status: Invalid argument: ReluGrad input is not finite. : Tensor had NaN values
[[Node: gradients_4/Relu_13_grad/Relu_13/CheckNumerics = CheckNumerics[T=DT_FLOAT, message="ReluGrad input is not finite.", _device="/job:localhost/replica:0/task:0/cpu:0"](add_17)]]
W tensorflow/core/common_runtime/executor.cc:1027] 0x117e76c0 Compute status: Invalid argument: ReluGrad input is not finite. : Tensor had NaN values
[[Node: gradients_4/Relu_14_grad/Relu_14/CheckNumerics = CheckNumerics[T=DT_FLOAT, message="ReluGrad input is not finite.", _device="/job:localhost/replica:0/task:0/cpu:0"](add_18)]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/media/sf_Data/mnistConv.py", line 69, in <module>
train_step.run(feed_dict={x: preprocessImages(batch[0]), y_: batch[1], keep_prob: 0.5})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1267, in run
_run_using_default_session(self, feed_dict, self.graph, session)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2763, in _run_using_default_session
session.run(operation, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 345, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 419, in _do_run
e.code)
tensorflow.python.framework.errors.InvalidArgumentError: ReluGrad input is not finite. : Tensor had NaN values
[[Node: gradients_4/Relu_12_grad/Relu_12/CheckNumerics = CheckNumerics[T=DT_FLOAT, message="ReluGrad input is not finite.", _device="/job:localhost/replica:0/task:0/cpu:0"](add_16)]]
Caused by op u'gradients_4/Relu_12_grad/Relu_12/CheckNumerics', defined at:
File "<stdin>", line 1, in <module>
File "/media/sf_Data/mnistConv.py", line 58, in <module>
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 165, in minimize
gate_gradients=gate_gradients)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 205, in compute_gradients
loss, var_list, gate_gradients=(gate_gradients == Optimizer.GATE_OP))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients.py", line 414, in gradients
in_grads = _AsList(grad_fn(op_wrapper, *out_grads))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_grad.py", line 107, in _ReluGrad
t = _VerifyTensor(op.inputs[0], op.name, "ReluGrad input is not finite.")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_grad.py", line 100, in _VerifyTensor
verify_input = array_ops.check_numerics(t, message=msg)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 48, in check_numerics
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 988, in __init__
self._traceback = _extract_stack()
...which was originally created as op u'Relu_12', defined at:
File "<stdin>", line 1, in <module>
File "/media/sf_Data/mnistConv.py", line 34, in <module>
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 506, in relu
return _op_def_lib.apply_op("Relu", features=features, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 988, in __init__
self._traceback = _extract_stack()
これはまったく同じです50のトレーニングの例で私の個人的なデータセットを取得するとエラーが発生します。
のNaNのは、通常、あなたの学習率が高すぎることを意味する発散されることを意味し、問題を解決しました。あなたが画像を前処理するなら、最適な学習率はおそらく異なってくるでしょう –