[英]calling keras custom metrics for prediction
I am working on multilabel text classification using Keras.我正在使用 Keras 进行多标签文本分类。 I have defined custom metrics for F1 score, recall, and precision and used them while compile().
我为 F1 分数、召回率和精度定义了自定义指标,并在 compile() 时使用它们。 but now I want to call my metrics function to predict the score I get the following error.
但现在我想调用我的指标 function 来预测分数我得到以下错误。
from keras import backend as K
import tensorflow as tf
def recall_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def f1_m(y_true, y_pred):
precision = precision_m(y_true, y_pred)
recall = recall_m(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
model.compile('Adam', 'binary_crossentropy', metrics = [f1_m, precision_m,
recall_m])
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
callbacks=[early_stopping, chk], validation_data=(x_valid, y_valid))
after training i want to predict my results as follows:训练后我想预测我的结果如下:
y_pred=np.asarray(model.predict(x_test))>0.3
y_pred.round()
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 1., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float16)
then i call my metrics:然后我调用我的指标:
r=recall_m(y_test, y_pred)
it gives me the following error:它给了我以下错误:
------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-33-5cc75ee76627> in <module>()
----> 1 r=recall_m(y_test, y_pred)
6 frames
<ipython-input-27-4b9c46f5a788> in recall_m(y_true, y_pred)
6 true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
7 possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
----> 8 recall = true_positives / (possible_positives + K.epsilon())
9 return recall
10
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_ops.py
in binary_op_wrapper(x, y)
1162 with ops.name_scope(None, op_name, [x, y]) as name:
1163 try:
-> 1164 return func(x, y, name=name)
1165 except (TypeError, ValueError) as e:
1166 # Even if dispatching the op failed, the RHS may be a
tensor aware
/usr/local/lib/python3.7/dist-
packages/tensorflow/python/util/dispatch.py
in wrapper(*args, **kwargs)
199 """Call target, and fall back on dispatchers if there is a
TypeError."""
200 try:
--> 201 return target(*args, **kwargs)
202 except (TypeError, ValueError):
203 # Note: convert_to_eager_tensor currently raises a
ValueError,
not a
/usr/local/lib/python3.7/dist-
packages/tensorflow/python/ops/math_ops.py in _add_dispatch(x, y, name)
1484 return gen_math_ops.add(x, y, name=name)
1485 else:
-> 1486 return gen_math_ops.add_v2(x, y, name=name)
1487
1488
/usr/local/lib/python3.7/dist-
packages/tensorflow/python/ops/gen_math_ops.py in add_v2(x, y, name)
470 return _result
471 except _core._NotOkStatusException as e:
--> 472 _ops.raise_from_not_ok_status(e, name)
473 except _core._FallbackException:
474 pass
/usr/local/lib/python3.7/dist-
packages/tensorflow/python/framework/ops.py in
raise_from_not_ok_status(e, name)
6860 message = e.message + (" name: " + name if name is not None
else "")
6861 # pylint: disable=protected-access
-> 6862 six.raise_from(core._status_to_exception(e.code, message),
None)
6863 # pylint: enable=protected-access
6864
/usr/local/lib/python3.7/dist-packages/six.py in raise_from(value,
from_value)
InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was
expected to be a int64 tensor but is a float tensor [Op:AddV2]
kindly help好心的帮助
TensorFlow does not do implicit casting. TensorFlow 不进行隐式转换。 When you add Tensors together, they need to have the same type.
当您将张量添加在一起时,它们需要具有相同的类型。 Your
y_true
tensor has a int64 type, while K.espilon
is a float32
.您的
y_true
张量具有 int64 类型,而K.espilon
是float32
。 Cast the inputs before doing the calculations, for example:在进行计算之前转换输入,例如:
def recall_m(y_true, y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(y_pred, tf.float32)
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
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