Keras custom loss function print tensor values - tensorflow

Question

I try to implement object detector like yolo. It uses complex custom loss function. So I need to print/debug its tensors. I understand, that python code only builds computing graph so standard print won't work in not eager mode.
tensorflow 1.12.0
keras 2.2.4
I tried all methods from these posts 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 Keras - printing intermediate tensors in loss function (tf.Print and K.print_tensor do not work...) says that loss function sometimes doesn't even called! Ok, but how to use tf.contrib.eager.defun decorator then? The example is for pure tensorflow and dont't understand how to use it in keras.
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

The truth was somewhere near. Idle doesn't output tf.Print and therefore K.print_tensor() to it's shell, so when i used cmd.exe python train.py, i saw tensor output.