Keras 自定义损失函数打印张量值

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函数。所以我需要 print/debug 它的张量。据我所知，python 代码仅构建计算图，因此标准打印在 not eager 模式下无法工作。张量流 1.12.0 喀拉斯 2.2.4

我尝试了这些帖子中的所有方法 Keras custom loss function not printing value of tensor, Debugging keras tensor values and nothing works. I tried tf.Print, tf.print, callback, K.tensor_print - the same result. In console i see only standart output messages. I`m not even sure if loss function been called. The answer from this post 说有时甚至不调用损失函数！好的，但是如何使用 tf.contrib.eager.defun 装饰器呢？该示例是纯tensorflow的，不明白如何在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。 Idle 不输出 tf.Print，因此 K.print_tensor() 是 shell，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

Keras custom loss function print tensor values

machine-learning

deep-learning

keras

tensorflow

loss-function