Question

我尝试实现像yolo这样的对象检测器。它使用复杂的自定义损失功能。所以我需要打印/调试它的张量。我了解，python代码只能建立计算图，因此标准打印在不急于工作的模式下将无法正常工作。张量流1.12.0 keras 2.2.4

我尝试了这些帖子Keras custom loss function not printing value of tensor，Debugging keras tensor values中的所有方法，但没有任何效果。我尝试了tf.Print，tf.print，回调，K.tensor_print-相同的结果。在控制台中，我仅看到标准输出消息。我什至不确定是否调用了损失函数。这篇帖子Keras - printing intermediate tensors in loss function (tf.Print and K.print_tensor do not work...)的回答说，有时甚至没有调用损失函数！好的，但是如何使用tf.contrib.eager.defun装饰器呢？该示例用于纯张量流，不了解如何在keras中使用它。

import tensorflow as tf
from keras.datasets import fashion_mnist
import matplotlib.pyplot as plt
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers import Flatten, Dense, Dropout
from keras.models import Sequential
from keras import optimizers
import numpy as np
from random import randrange
from keras.callbacks import LambdaCallback
import keras.backend as K
import keras

print(tf.__version__)
print(keras.__version__)


num_filters = 64

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

#reshape
x_train = x_train.reshape(60000,28,28,1)[:1000,...]
x_test = x_test.reshape(10000,28,28,1)[:100,...]

# One-hot encode the labels
y_train = tf.keras.utils.to_categorical(y_train, 10)[:1000,...]
y_test = tf.keras.utils.to_categorical(y_test, 10)[:100,]


labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']


model = Sequential()
model.add(Conv2D(input_shape=(28,28,1), filters=num_filters,kernel_size=3,strides=(1, 1),padding="valid", activation='relu', use_bias=True))  
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))

model.add(Conv2D(filters=num_filters,kernel_size=3,strides=(1, 1),padding="valid", activation='relu', use_bias=True))  
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))

model.add(Flatten())
model.add(Dense(256, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation = 'softmax'))


#model.summary()


#loss 1
def customLoss(yTrue,yPred):
    d = yPred-yTrue
    d = K.print_tensor(d)
    return K.mean(K.square(d), axis=-1)

#loss 2
def cat_loss(y_true, y_pred):
    d = y_true - y_pred
    d = tf.Print(d, [d], "Inside loss function")
    return tf.reduce_mean(tf.square(d))      


model.compile(loss=customLoss,
             optimizer='adam')



import keras.callbacks as cbks

# 3 try to print with callback
class CustomMetrics(cbks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        for k in logs:
            if k.endswith('cat_loss'):
               print(logs[k])   


#checkpointer = ModelCheckpoint(filepath='model.weights.best.hdf5', verbose = 1, save_best_only=True)
model.fit(x_train,
         y_train,
         #verbose=1,
         batch_size=16,
         epochs=10,
         validation_data=(x_test, y_test),
        callbacks=[CustomMetrics()])


# Evaluate the model on test set
score = model.evaluate(x_test, y_test, verbose=0)

# Print test accuracy
print('\n', 'Test accuracy:', score)


rand_img = randrange(100)

result = np.argmax(model.predict(x_test[rand_img].reshape(1,28,28,1)))
plt.imshow(x_test[rand_img].reshape(28,28), cmap='gray')
plt.title(labels[result])

==========>......] - ETA: 0s - loss: 0.0243
832/1000 [=======================>......] - ETA: 0s - loss: 0.0242
Warning (from warnings module):
 File "C:\Python36\lib\site-packages\keras\callbacks.py", line 122
   % delta_t_median)
UserWarning: Method on_batch_end() is slow compared to the batch update (0.101474). Check your callbacks.

976/1000 [============================>.] - ETA: 0s - loss: 0.0238
992/1000 [============================>.] - ETA: 0s - loss: 0.0236
1000/1000 [==============================] - 3s 3ms/step - loss: 0.0239 - val_loss: 0.0352

Test accuracy: 0.035189545452594756```

Answer 1

真相就在附近。空闲不会将tf.Print以及K.print_tensor（）输出到它的外壳，所以当我使用cmd.exe python train.py时，我看到了张量输出。

Keras自定义损失函数打印张量值

1 个答案: