Терминальная выходная ошибка экспорта

Я запускаю программу Python, которая использует VGG16 нейронная сеть, через keras пакет, для классификации изображений кошек и собак, от базы данных Kaggle. Чтобы сделать это, я использую стандартную терминальную команду: python program.py > output.txt. Я также испытал другие варианты, python program.py &> output.txt, или, tee команда, python program.py |& tee output.txt, но это, кажется, не работает. Для первой команды мой текстовый файл содержит просто:

Using TensorFlow backend.
2017-05-31 13:39:34.218034: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are
 available on your machine and could speed up CPU computations.
2017-05-31 13:39:34.226941: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are
 available on your machine and could speed up CPU computations.

но код имеет много из print операторы! Ожидаемое содержание output.txt файл (только первые 4-5 строк терминала производят показанный):

Using TensorFlow backend.
Defining all the path!

All paths defined!

Getting mean RGB and creating labels!

который отображен, когда я просто ввожу python program.py. Часть:

2017-05-31 13:39:34.218034: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are
 available on your machine and could speed up CPU computations.
2017-05-31 13:39:34.226941: W tensorflow/core/platform/cpu_feature_guard.cc:45] 
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are
 available on your machine and could speed up CPU computations.

часть существует намного позже терминального вывода. Я помещаю свой код здесь для ссылки, но это - 204 строки долго:

import keras
from keras.models import Sequential, Model
from keras.layers import Flatten, Dense, Dropout, Input, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.merge import Add
from keras.optimizers import SGD, Adam
import cv2, numpy as np
import glob
import csv

####################
## VGG16 Function ##
####################

def VGG_16(weights_path=None, classes=2):

    ######################################
    ## Input: 3x224x224 sized RGB Input ##
    ######################################

    inputs = Input(shape=(3,224,224))

    layer = 0
    #############
    ## Block 1 ##
    #############
    x = Conv2D(64, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block1_conv1')(inputs)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(64, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block1_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    #############
    ## Block 2 ##
    #############
    x = Conv2D(128, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block2_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(128, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block2_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    #############
    ## Block 3 ##
    #############
    x = Conv2D(256, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block3_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(256, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block3_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(256, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block3_conv3')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    #############
    ## Block 4 ##
    #############
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block4_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block4_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block4_conv3')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    #############
    ## Block 5 ##
    #############
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block5_conv1')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block5_conv2')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    x = Conv2D(512, (3, 3), data_format='channels_first', activation='relu', padding='same', name='block5_conv3')(x)
    layer += 1
    print ('Output shape for Layer ' +str(layer)+ ', is, ' +str(x.get_shape()))
    out = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    ###############
    ## Top layer ##
    ###############

    out = Flatten(name='flatten')(out)
    out = Dense(4096, activation='relu', name='fc1')(out)
    out = Dropout(0.5)(out)
    out = Dense(4096, activation='relu', name='fc2')(out)
    out = Dropout(0.5)(out)
    out = Dense(classes, activation='softmax', name='predictions')(out)

    if weights_path:
        model.load_weights(weights_path)

    model = Model(inputs, out, name='vgg-16')

    return model

###################
## Main Function ##
###################

if __name__ == "__main__":

    ################################################
    ## Get all the training and the testing paths ##
    ################################################

    print('Defining all the path!\n')
    cat_path = "./train/cat.*.jpg"
    dog_path = "./train/dog.*.jpg"
    train_path = "./train/*.jpg"
    test_path = "./test1/*.jpg"
    Mean_RGB = []
    x_train = []
    y_train = []
    x_test = []
    print('All paths defined!\n')

    ########################################################################
    ## Get training and testng data sizes, to find the average RGB values ##
    ########################################################################

    print('Getting mean RGB and creating labels!\n')
    for file in glob.glob(cat_path): # To get the sizes of all the cat images
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im = np.mean(im, axis=(0,1))
        Mean_RGB.append(tuple(im))
        y_train.append(0)
    for file in glob.glob(dog_path): # To get the sizes of all the dog images
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im = np.mean(im, axis=(0,1))
        Mean_RGB.append(tuple(im))
        y_train.append(1)
    y_train = np.array(y_train)
    Mean_RGB = tuple(np.mean(Mean_RGB, axis=0))
    print('Got mean RGB and created labels!\n')

    #########################################################################
    ## Load the training and testing images, after subtracting average RGB ##
    #########################################################################

    print('Loading images as numpy arrays!\n')
    for file in glob.glob(train_path):
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im_r = im-Mean_RGB
        im_r = im_r.transpose((2,0,1))
        #im_r = np.expand_dims(im_r, axis=0)
        x_train.append(im_r)
    y_train = y_train.reshape((-1,1))
    y_train = keras.utils.to_categorical(y_train, num_classes=2)
    x_train = np.array(x_train)
    for file in glob.glob(test_path):
        im = cv2.resize(cv2.imread(file), (224, 224)).astype(np.float32)
        im_r = im-Mean_RGB
        im_r = im_r.transpose((2,0,1))
        #im_r = np.expand_dims(im_r, axis=0)
        x_test.append(im_r)
    x_test = np.array(x_test)
    print('All images loaded!\n')

    ##############################
    ## Train and test the model ##
    ##############################

    print('Creating Neural Net!\n')
    model = VGG_16()
    print('\nNeural Net created!\n')
    adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
    model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])

    print('Training Neural Net!\n')
    ### Generating validation data split in training sample
    model.fit(x_train, y_train, batch_size=500, epochs=25, validation_split=0.2, shuffle=True)
    print('Neural Net trained!\n')
    print('Evaluating model on the training images!\n')
    score = model.evaluate(x_train, y_train, batch_size=500, verbose=1)
    print('Model score on training data: ' +str(score)+ '\n')
    print('Predicting class of test images!\n')
    pred = model.predict(x_test, batch_size=1, verbose=1)
    prediction = np.argmax(pred, axis = 1)
    print('Predictions done!\n')
    result = []
    print('Creating output CSV file!\n')
    result.append(['id', 'label'])
    for i in range(0,len(prediction)):
        result.append([i+1,prediction[i]])
    with open("cat-dog-output.csv","wb") as f:
        writer = csv.writer(f)
        writer.writerows(result)
    print('Created output CSV file!\n')

    print('Saving model parameters!\n')
    model.save('vgg16-sim-conn.h5')
    model.save_weights('vgg16-sim-conn-weights.h5')
    print('Model saved!\n')

Я не знаю то, что действительно продолжается, и любая справка в этом вопросе будет глубоко цениться!