如何在Keras模型中使用TensorFlow度量函数？

Question

如何在Keras模型中使用TensorFlow度量函数？

pythontensorflowmachine-learningneural-networkkeras

6

使用 Python 3.5.2 TensorFlow RC 1.1

我正在尝试在 Keras 中使用 TensorFlow 的度量函数。所需的函数接口似乎是相同的，但调用：

import pandas
import numpy
import tensorflow.contrib.keras as keras
import tensorflow


def target_function(row):
    return float(row[0] - row[1] < 0.5)

df = pandas.DataFrame(numpy.random.rand(10000,2))
label = df.apply(target_function, axis=1)

input_layer = keras.layers.Input(shape=(2,))
net = keras.layers.Dense(1)(input_layer)

model = keras.models.Model(inputs=[input_layer], outputs=[net])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[tensorflow.metrics.auc])

model.fit(df.as_matrix(), label.as_matrix(), epochs=10, validation_split=0.2, batch_size=100)

带有错误的结果：

Using TensorFlow backend.
Traceback (most recent call last):
  File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 49, in <module>
    metrics=[precision, recall, tensorflow.metrics.auc]
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/engine/training.py", line 956, in compile
    metric_result = masked_metric_fn(y_true, y_pred, mask=masks[i])
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/engine/training.py", line 489, in masked
    return K.mean(score_array)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1120, in mean
    axis = _normalize_axis(axis, ndim(x))
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 437, in ndim
    dims = x.get_shape()._dims
AttributeError: 'tuple' object has no attribute 'get_shape'

Process finished with exit code 1

编辑

在采纳了Marcin Możejko的建议后，代码如下：

import pandas
import numpy
import tensorflow.contrib.keras as keras
import tensorflow


def metric_function(y_true, y_pred):
    return tensorflow.metrics.precision(y_true,y_pred)[0]


def target_function(row):
    return float(row[0] - row[1] < 0.5)

df = pandas.DataFrame(numpy.random.rand(10000,2))
label = df.apply(target_function, axis=1)

input_layer = keras.layers.Input(shape=(2,))
net = keras.layers.Dense(1)(input_layer)

model = keras.models.Model(inputs=[input_layer], outputs=[net])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[metric_function])

model.fit(df.as_matrix(), label.as_matrix(), epochs=10, validation_split=0.2, batch_size=100)

错误信息如下：

/Users/ophir/anaconda3/envs/p3/bin/python /Users/ophir/dev/ophir/tf_keras_metrics.py
Train on 8000 samples, validate on 2000 samples
Epoch 1/10
2017-04-04 16:05:30.959006: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:30.959022: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:30.959026: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:30.959031: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:31.124262: W tensorflow/core/framework/op_kernel.cc:1152] Failed precondition: Attempting to use uninitialized value precision/true_positives/count
     [[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]
Traceback (most recent call last):
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1051, in _do_call
    return fn(*args)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1033, in _run_fn
    status, run_metadata)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/contextlib.py", line 66, in __exit__
    next(self.gen)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value precision/true_positives/count
     [[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 23, in <module>
    model.fit(df.as_matrix(), label.as_matrix(), epochs=10, validation_split=0.2, batch_size=100)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 1494, in fit
    initial_epoch=initial_epoch)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 1138, in _fit_loop
    outs = f(ins_batch)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/backend.py", line 2245, in __call__
    updated = session.run(self.outputs + [self.updates_op], feed_dict=feed_dict)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 786, in run
    run_metadata_ptr)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 994, in _run
    feed_dict_string, options, run_metadata)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1044, in _do_run
    target_list, options, run_metadata)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1064, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value precision/true_positives/count
     [[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]

Caused by op 'precision/true_positives/count/read', defined at:
  File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 21, in <module>
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[metric_function])
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 958, in compile
    metric_result = masked_metric_fn(y_true, y_pred, mask=masks[i])
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 487, in masked
    score_array = fn(y_true, y_pred)
  File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 8, in metric_function
    return tensorflow.metrics.precision(y_true,y_pred)[0]
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 1377, in precision
    updates_collections=None, name=None)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 1274, in true_positives
    updates_collections)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 1211, in _count_condition
    count = _create_local('count', shape=[])
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 197, in _create_local
    validate_shape=validate_shape)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 197, in __init__
    expected_shape=expected_shape)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 316, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1343, in identity
    result = _op_def_lib.apply_op("Identity", input=input, name=name)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
    op_def=op_def)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
    self._traceback = _extract_stack()

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value precision/true_positives/count
     [[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]

Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x1140626d8>>
Traceback (most recent call last):
  File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 595, in __del__
AttributeError: 'NoneType' object has no attribute 'TF_NewStatus'

Process finished with exit code 1

- Ophir Yoktan

你可以提供你的“模型”定义吗？ - Marcin Możejko

添加了一个虚拟代码片段，以重现问题。 - Ophir Yoktan

你可以试试：model = keras.models.Model(inputs=input_layer, outputs=net)？ - Marcin Możejko

尝试过了 - 同样的错误 - Ophir Yoktan

没有这个auc，它能正常工作吗？ - Marcin Możejko

4个回答

1

由 K.switch 编写
导入 keras.backend as K

def k_mean_iou(NUM_CLASSES):
        """
        assume 0 is background for labels and prediction
        labels,prediction with shape of [batch,height,width,class_number]

        """
        def switch_mean_iou(labels, predictions):
            mean_iou = K.variable(0.0)
            seen_classes = K.variable(0.0)

            for c in range(1,NUM_CLASSES):
                labels_c = K.cast(K.equal(labels, c), K.floatx())
                pred_c = K.cast(K.equal(predictions, c), K.floatx())

                labels_c_sum = K.sum(labels_c)
                pred_c_sum = K.sum(pred_c)

                intersect = K.sum(labels_c*pred_c)
                union = labels_c_sum + pred_c_sum - intersect
                iou = intersect / union
                condition = K.equal(union, 0)
                mean_iou = K.switch(condition,
                                    mean_iou,
                                    mean_iou+iou)
                seen_classes = K.switch(condition,
                                        seen_classes,
                                        seen_classes+1)

            mean_iou = K.switch(K.equal(seen_classes, 0),
                                mean_iou,
                                mean_iou/seen_classes)
            return mean_iou

        return switch_mean_iou

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=k_mean_iou(class_number))

- 王佳欣

1

我认为你应该能够像这样做：

def tf_auc_score(y_true, y_pred):
    return tensorflow.metrics.auc(y_true, y_pred)[1]

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[tf_auc_score])

from keras import backend as K
K.get_session().run(tf.local_variables_initializer())

model.fit(...)

看起来以前是 tf.global_variables_initializer()，但现在 tf.local_variables_initializer() 可以工作，至少对我来说是这样。

- wordsforthewise

0

关于新错误：通常情况下，当在会话中运行tf.global_variables_initializer()时，会出现FailedPreconditionsErrors。请确保您没有遗漏它。

- parsethis

这个修复 https://github.com/fchollet/keras/issues/4402 解决了 FailedPreconditionsError。然而，无论我使用什么指标，我总是得到一个值为0。 - danst_18

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- Marcin Możejko · Accepted Answer

4

好的 - 我认为我找到了一个错误。这个函数返回一个带有(auc, 其他内容)的tuple。你需要通过定义自己的auc来克服这个问题，方法如下：

def auc_tf_metric(y_true, y_pred):
    return tensorflow.metrics.auc(y_true, y_pred)[0]

- Marcin Możejko

3

现在我得到了一个错误：FailedPreconditionError（请参见上面的跟踪信息）：尝试使用未初始化的值auc/true_positives。[[节点：auc / true_positives / read = Identity [T = DT_FLOAT，_class = [“loc：@ auc / true_positives”]，_device =“/ job：localhost / replica：0 / task：0 / cpu：0”]（auc / true_positives）]]。 - Ophir Yoktan

它失败了，但是在不同的地方。 - Ophir Yoktan

尚未 - 该问题包含更新程序和错误代码。 - Ophir Yoktan

如果我提供给你一个非常简单的调整，你能试一下吗？ - Marcin Możejko

检查我的答案。 - Marcin Możejko