对于我的代码，基本上我使用的是this MNIST示例。我的图片不是28x28，而是120x50x3。我的标签不仅是MNIST的数字，而且还是120x50x3的图像。我在某个地方的代码中犯了一个错误。我认为这是sigmoid_cross_entropy函数的问题，它似乎需要一维参数。由于我已经在剧院问题上花费了数小时，因此我迷路了，不胜感激。
在发布代码之前，这是我得到的错误：
tensorflow.python.framework.errors_impl.InvalidArgumentError: Can not squeeze dim[1], expected a dimension of 1, got 18000 for 'remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [1024,18000].
这是我从中获取图像的功能：
def dataset(directory):
"""Download and parse MNIST dataset."""
images_file = load_data("/Users/pics/images/")
labels_file = load_data("/Users/pics/labels/")
def decode_image(image):
image = tf.cast(image, tf.float32)
return image
def decode_label(label):
label = tf.cast(label, tf.float32)
return label
images = tf.data.Dataset.from_tensor_slices(images_file).map(decode_image)
labels = tf.data.Dataset.from_tensor_slices(labels_file).map(decode_label)
return tf.data.Dataset.zip((images, labels))
def train(directory):
"""tf.data.Dataset object for MNIST training data."""
return dataset(directory)
def test(directory):
"""tf.data.Dataset object for MNIST test data."""
return dataset(directory)
这是我的model_fn：
def model_fn(features, labels, mode, params):
"""The model_fn argument for creating an Estimator."""
model = create_model(features, labels, mode, params)
image = features
if isinstance(image, dict):
image = features['image']
if mode == tf.estimator.ModeKeys.PREDICT:
logits = model(image, training=False)
predictions = {
'classes': tf.argmax(logits, axis=1),
'probabilities': tf.nn.softmax(logits),
}
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'classify': tf.estimator.export.PredictOutput(predictions)
})
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=4.25e-5)
# If we are running multi-GPU, we need to wrap the optimizer.
if params.get('multi_gpu'):
optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)
logits = model(image, training=True)
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits)
loss = tf.reduce_mean(cross_entropy)
accuracy = tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1))
# Name tensors to be logged with LoggingTensorHook.
tf.identity(LEARNING_RATE, 'learning_rate')
tf.identity(loss, 'cross_entropy')
tf.identity(accuracy[1], name='train_accuracy')
# Save accuracy scalar to Tensorboard output.
tf.summary.scalar('train_accuracy', accuracy[1])
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.TRAIN,
loss=loss,
train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))
if mode == tf.estimator.ModeKeys.EVAL:
logits = model(image, training=False)
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)
loss = tf.reduce_mean(cross_entropy)
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.EVAL,
loss=loss,
eval_metric_ops={
'accuracy':
tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1)),
})
这是完整的错误消息：
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "mnist.py", line 287, in <module>
absl_app.run(main)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/absl/app.py", line 278, in run
_run_main(main, args)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/absl/app.py", line 239, in _run_main
sys.exit(main(argv))
File "mnist.py", line 281, in main
run_mnist(flags.FLAGS)
File "mnist.py", line 263, in run_mnist
mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 366, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1119, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1132, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 1107, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "mnist.py", line 158, in model_fn
labels=labels, predictions=tf.argmax(logits, axis=1))
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 403, in accuracy
predictions=predictions, labels=labels, weights=weights)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 80, in _remove_squeezable_dimensions
labels, predictions)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/ops/confusion_matrix.py", line 72, in remove_squeezable_dimensions
labels = array_ops.squeeze(labels, [-1])
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 432, in new_func
return func(*args, **kwargs)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 2556, in squeeze
return gen_array_ops.squeeze(input, axis, name)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 7946, in squeeze
"Squeeze", input=input, squeeze_dims=axis, name=name)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op
op_def=op_def)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1756, in __init__
control_input_ops)
File "/Users/cezary/.pyenv/versions/3.6.4/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1592, in _create_c_op
raise ValueError(str(e))
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 18000 for 'remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [1024,18000].