Question

我是整个tensorflow和深度学习的新手。

我已经创建了一个自定义损失函数，但是似乎在该自定义损失函数中，未启用急切执行。以下是我的自定义损失函数（不起作用）：

def custom_error_finder(y_actual,y_pred):
    print(tf.executing_eagerly())
    count = 0
    qw = tf.py_function((y_actual).numpy())
    ya = ((y_actual[0].numpy()).decode())
    yp = ((y_pred[0].numpy()).decode())
    for i,j in ya,yp:
        if i!=j:
            count = count+1
    mse = pow(count,2)/len(ya)
    return mse

让我感到困扰的是，在此函数之外，每当我运行print(tf.executing_eagerly())时，它都返回true，但是在函数中，它返回false。

我已经尝试了所有可以找到的修复程序：

-在model.compile（）函数中传递run_eagerly = True

-在编译函数之后添加model.run_eagerly() = True

在损失函数中

-运行tf.compat.v1.enable_eager_execution()至少强制那里渴望执行。

以上修复程序均无效。

Answer 1

我能够如下重现该问题。您可以从here下载程序中我正在使用的数据集。我在程序中添加了List<Team> sortedList = teams.stream() .sorted( (t1, t2) -> t2.getRanking().compareTo(t1.getRanking()) ) .collect(Collectors.toList());语句以跟踪更改。

代码-

print("tf.executing_eagerly() Results")

输出-

%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
import numpy as np
from numpy import loadtxt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras import backend as K

print("tf.executing_eagerly() Results")
print("Before loading dataset :",tf.executing_eagerly())

# load pima indians dataset
dataset = np.loadtxt("/content/pima-indians-diabetes.csv", delimiter=",")

# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]

# define model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

print("After building model :",tf.executing_eagerly())

def weighted_binary_crossentropy(y_true, y_pred):
  print("In loss function :",tf.executing_eagerly())
  return K.mean(K.binary_crossentropy(y_pred, y_true))

# compile model
model.compile(loss=weighted_binary_crossentropy, optimizer='adam', metrics=['accuracy'])

print("After compiling model :",tf.executing_eagerly())

# Fit the model
model.fit(X, Y, epochs=1, batch_size=150, verbose=0)

# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

解决方案-按照documentation。它提到，

run_eagerly-可设置属性，指示模型是否应快速运行。热情地运行意味着您的模型将像Python代码一样一步一步地运行。您的模型运行速度可能较慢，但是通过逐步进入各个层调用，您应该可以更轻松地对其进行调试。 默认情况下，我们将尝试将您的模型编译为静态图，以提供最佳的执行性能。

如果我们使用2.2.0 tf.executing_eagerly() Results Before loading dataset : True After building model : True After compiling model : True In loss function : False In loss function : False In loss function : False accuracy: 34.90%参数修改model.compile，则可以解决此问题。下面显示的是修改后的run_eagerly = True代码，

model.compile

固定代码-

model.compile(loss=weighted_binary_crossentropy, run_eagerly = True, optimizer='adam', metrics=['accuracy'])

输出-

%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
import numpy as np
from numpy import loadtxt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras import backend as K

print("tf.executing_eagerly() Results")
print("Before loading dataset :",tf.executing_eagerly())

# load pima indians dataset
dataset = np.loadtxt("/content/pima-indians-diabetes.csv", delimiter=",")

# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]

# define model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

print("After building model :",tf.executing_eagerly())

def weighted_binary_crossentropy(y_true, y_pred):
  print("In loss function :",tf.executing_eagerly())
  return K.mean(K.binary_crossentropy(y_pred, y_true))

# compile model
model.compile(loss=weighted_binary_crossentropy, run_eagerly = True, optimizer='adam', metrics=['accuracy'])

print("After compiling model :",tf.executing_eagerly())

# Fit the model
model.fit(X, Y, epochs=1, batch_size=150, verbose=0)

# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

希望这能回答您的问题。学习愉快。

在Tensorflow 2.1.0中强制执行急切的执行

1 个答案: