我有一个包含两张图片的文件夹和一个带有文件名和标签的csv，我已将其放入两个数组中：
filenames：array（['pic1.jpg'，'pic2.jpg']，dtype = object）; labels_i：array（[0,1]）
我创建了一个火车输入Funktion
def train_input_fn(filenames, labels_i):
filenames2 = tf.constant(filenames)
labels2 = tf.constant(labels_i, dtype=tf.int64)
dataset = tf.data.Dataset.from_tensor_slices((filenames2, labels2))
def _parse(filename, label):
image_string = tf.read_file(PATH+"data/"+filename)
image_decoded = tf.image.decode_jpeg(image_string)
image_resized = tf.image.resize_images(image_decoded, [28, 28])
return image_resized, label
dataset = dataset.map(_parse)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
我的模型与MNIST教程基本相同，因为RGB图片，我只修改输入图层以形成[-1,28,28,3]而不是[-1,28,28,1]
def my_model(features, labels, mode, params):
# Input Layer
input_layer = tf.reshape(features, [-1, 28, 28, 3])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=2)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes[:, tf.newaxis],
'probabilities': tf.nn.softmax(logits),
'logits': logits,
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Compute evaluation metrics.
accuracy = tf.metrics.accuracy(labels=labels,
predictions=predicted_classes,
name='acc_op')
metrics = {'accuracy': accuracy}
tf.summary.scalar('accuracy', accuracy[1])
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=metrics)
# Create training op.
assert mode == tf.estimator.ModeKeys.TRAIN
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
然后我尝试训练模型：
classifier = tf.estimator.Estimator(model_fn=my_model)
classifier.train(input_fn=lambda: train_input_fn(filenames, labels_i),steps=200)
但我得到以下错误：
InvalidArgumentError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [1,2].
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [1,2].
完成错误： INFO：tensorflow：调用model_fn。
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
685 graph_def_version, node_def_str, input_shapes, input_tensors,
--> 686 input_tensors_as_shapes, status)
687 except errors.InvalidArgumentError as err:
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
515 compat.as_text(c_api.TF_Message(self.status.status)),
--> 516 c_api.TF_GetCode(self.status.status))
517 # Delete the underlying status object from memory otherwise it stays alive
InvalidArgumentError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [?,2].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-13-6a3648164a55> in <module>()
1 # Train the Model.
----> 2 classifier.train(input_fn=lambda: train_input_fn(filenames, labels_i),steps=200)
3
~/pyft/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
350
351 saving_listeners = _check_listeners_type(saving_listeners)
--> 352 loss = self._train_model(input_fn, hooks, saving_listeners)
353 logging.info('Loss for final step: %s.', loss)
354 return self
~/pyft/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
810 worker_hooks.extend(input_hooks)
811 estimator_spec = self._call_model_fn(
--> 812 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
813
814 if self._warm_start_settings:
~/pyft/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
791
792 logging.info('Calling model_fn.')
--> 793 model_fn_results = self._model_fn(features=features, **kwargs)
794 logging.info('Done calling model_fn.')
795
<ipython-input-11-776f3754c5df> in my_model(features, labels, mode, params)
57
58 # Compute loss.
---> 59 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
60
61 # Compute evaluation metrics.
~/pyft/lib/python3.6/site-packages/tensorflow/python/ops/losses/losses_impl.py in sparse_softmax_cross_entropy(labels, logits, weights, scope, loss_collection, reduction)
829 # therefore, expected_rank_diff=1.
830 labels, logits, weights = _remove_squeezable_dimensions(
--> 831 labels, logits, weights, expected_rank_diff=1)
832 losses = nn.sparse_softmax_cross_entropy_with_logits(labels=labels,
833 logits=logits,
~/pyft/lib/python3.6/site-packages/tensorflow/python/ops/losses/losses_impl.py in _remove_squeezable_dimensions(labels, predictions, weights, expected_rank_diff)
758 """
759 labels, predictions = confusion_matrix.remove_squeezable_dimensions(
--> 760 labels, predictions, expected_rank_diff=expected_rank_diff)
761
762 if weights is not None:
~/pyft/lib/python3.6/site-packages/tensorflow/python/ops/confusion_matrix.py in remove_squeezable_dimensions(labels, predictions, expected_rank_diff, name)
73 rank_diff = predictions_rank - labels_rank
74 if rank_diff == expected_rank_diff + 1:
---> 75 predictions = array_ops.squeeze(predictions, [-1])
76 elif rank_diff == expected_rank_diff - 1:
77 labels = array_ops.squeeze(labels, [-1])
~/pyft/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py in squeeze(input, axis, name, squeeze_dims)
2566 if np.isscalar(axis):
2567 axis = [axis]
-> 2568 return gen_array_ops._squeeze(input, axis, name)
2569
2570
~/pyft/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py in _squeeze(input, axis, name)
5167 if _ctx.in_graph_mode():
5168 _, _, _op = _op_def_lib._apply_op_helper(
-> 5169 "Squeeze", input=input, squeeze_dims=axis, name=name)
5170 _result = _op.outputs[:]
5171 _inputs_flat = _op.inputs
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
785 op = g.create_op(op_type_name, inputs, output_types, name=scope,
786 input_types=input_types, attrs=attr_protos,
--> 787 op_def=op_def)
788 return output_structure, op_def.is_stateful, op
789
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
3271 op_def=op_def)
3272 self._create_op_helper(ret, compute_shapes=compute_shapes,
-> 3273 compute_device=compute_device)
3274 return ret
3275
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _create_op_helper(self, op, compute_shapes, compute_device)
3311 # compute_shapes argument.
3312 if op._c_op or compute_shapes: # pylint: disable=protected-access
-> 3313 set_shapes_for_outputs(op)
3314 # TODO(b/XXXX): move to Operation.__init__ once _USE_C_API flag is removed.
3315 self._add_op(op)
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in set_shapes_for_outputs(op)
2499 return _set_shapes_for_outputs_c_api(op)
2500 else:
-> 2501 return _set_shapes_for_outputs(op)
2502
2503
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _set_shapes_for_outputs(op)
2472 shape_func = _call_cpp_shape_fn_and_require_op
2473
-> 2474 shapes = shape_func(op)
2475 if shapes is None:
2476 raise RuntimeError(
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in call_with_requiring(op)
2402
2403 def call_with_requiring(op):
-> 2404 return call_cpp_shape_fn(op, require_shape_fn=True)
2405
2406 _call_cpp_shape_fn_and_require_op = call_with_requiring
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py in call_cpp_shape_fn(op, require_shape_fn)
625 res = _call_cpp_shape_fn_impl(op, input_tensors_needed,
626 input_tensors_as_shapes_needed,
--> 627 require_shape_fn)
628 if not isinstance(res, dict):
629 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).
~/pyft/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
689 missing_shape_fn = True
690 else:
--> 691 raise ValueError(err.message)
692
693 if missing_shape_fn:
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [?,2].
答案 0 :(得分：0)
错误是由于错误尺寸的输入被送到sparse_softmax_cross_entropy。它希望logits的排名比标签大一些，但在你的情况下，排名的差异是2（见
if rank_diff == expected_rank_diff + 1:
---> 75 predictions = array_ops.squeeze(predictions, [-1])
。 expected_rank_diff为1。）
看看你的代码，我的猜测是，这是因为你的labels是一个标量 - 零维度。代码不够智能，无法尝试将其重塑为矢量。您应该尝试将labels_i指定为array([[0], [1]])而不是array([0, 1])。