Переключение с Keras на tf.keras спамит мой экран с # 010

Question

Я создал простую модель Keras для экспериментов в Amazon SageMaker.Я использую Python 3.5 TensorFlow 1.12.0.Недавно я переключил свою модель на использование TensorFlow.keras, но это привело к печати #010, за которой следовали #015, при загрузке нетто-веса изображения и отображении точности партии во время вызова подгонки.

Например, с многословным = 1 в model.fit:

Эпоха 1/1

015 1/1563 [..............................] - ETA: 5:50:36 - потери: 2,2798 - в соотв. 0,1875 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 #010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 #010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 #010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 #010 # 010 # 010 # 015

3/1563 [..............................] - ETA: 1:57:18 - потеря: 2.3002 - в соответствии: 0,1146 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010# # 010 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 #010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 #010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 015 5/1563[..............................] - ETA: 1:10:36 - потери: 2,3088 - счета: 0,1062 # 010# 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010# 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010# 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010# 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010 # 010

Кто-нибудь знает, почему это может происходить или как я могу предотвратить это?Воспроизведение с минимальным примером может потребовать запуска через SageMaker, но код, который я переключил с Keras на tf.keras, взят из в этом примере и, в частности, из файла trainer/start.py:

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
from __future__ import print_function

import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
import numpy as np

from trainer.environment import create_trainer_environment

NUM_CLASSES = 10
EPOCHS = 10
NUM_PREDICTIONS = 20
MODEL_NAME = 'keras_cifar10_trained_model.h5'

# the trainer environment contains useful information about
env = create_trainer_environment()
print('creating SageMaker trainer environment:\n%s' % str(env))

# getting the hyperparameters
batch_size = env.hyperparameters.get('batch_size', object_type=int)
data_augmentation = env.hyperparameters.get('data_augmentation', default=True, object_type=bool)
learning_rate = env.hyperparameters.get('learning_rate', default=.0001, object_type=float)
width_shift_range = env.hyperparameters.get('width_shift_range', object_type=float)
height_shift_range = env.hyperparameters.get('height_shift_range', object_type=float)
EPOCHS = env.hyperparameters.get('epochs', default=10, object_type=int)

# reading data from train and test channels
train_data = np.load(os.path.join(env.channel_dirs['train'], 'cifar-10-npz-compressed.npz'))
(x_train, y_train) = train_data['x'], train_data['y']

test_data = np.load(os.path.join(env.channel_dirs['test'], 'cifar-10-npz-compressed.npz'))
(x_test, y_test) = test_data['x'], test_data['y']


model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(NUM_CLASSES))
model.add(Activation('softmax'))

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=learning_rate, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

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

if not data_augmentation:
    print('Not using data augmentation.')
    model.fit(x_train, y_train, batch_size=batch_size, epochs=EPOCHS, validation_data=(x_test, y_test), shuffle=True)
else:
    print('Using real-time data augmentation.')
    # This will do preprocessing and real time data augmentation:
    data_generator = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=width_shift_range,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=height_shift_range,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

    # Compute quantities required for feature-wise normalization
    # (std, mean, and principal components if ZCA whitening is applied).
    data_generator.fit(x_train)

    # Fit the model on the batches generated by data_generator.flow().
    data_generator_flow = data_generator.flow(x_train, y_train, batch_size=batch_size)
    model.fit_generator(data_generator_flow, epochs=EPOCHS, validation_data=(x_test, y_test), workers=4)

# Save model and weights
model_path = os.path.join(env.model_dir, MODEL_NAME)
model.save(model_path)
print('Saved trained model at %s ' % model_path)

# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])