keras does not finish image recognition

from keras.applications.acception_v3importInceptionV3
from keras.applications.acceptance_v3 import preprocess_input
from keras.models import sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution 2D, MaxPooling 2D, ZeroPadding 2D, GlobalAveragePooling 2D, AveragePooling 2D
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler,ReduceLROnPlateau
from keras.optimizers import SGD
from keras.regularizers import l2
import matplotlib.image asmpimg
from scipy.misc import immediate
import numpy as np
import keras.backend as K
import path

K.clear_session()
img_size=299
# training data extension
train_datagen=ImageGenerator(
        featurewise_center=False,
        samplewise_center=False,
        featurewise_std_normalization=False,
        samplewise_std_normalization=False,
        rotation_range = 10,
        width_shift_range = 0.2,
        height_shift_range = 0.2,
        horizontal_flip = True,
        vertical_flip = False,
        zoom_range=[.8,1],
        channel_shift_range = 30,
        fill_mode='reflect')

test_dataGen= ImageDataGenerator()

# Image loading
defload_images(root,nb_img):
    all_imgs=[]
    all_classes=[ ]

    for i in range(nb_img):
        img_name="%s/dog.%d.jpg"%(root,i+1)
        img_arr=mpimg.imread(img_name)
        resize_img_ar=imresize(img_arr, (img_size, img_size))
        all_imgs.append(resize_img_ar)
        all_classes.append(0)
    for i in range(nb_img):
        img_name="%s/cat.%d.jpg"%(root,i+1)
        img_arr=mpimg.imread(img_name)
        resize_img_ar=imresize(img_arr, (img_size, img_size))
        all_imgs.append(resize_img_ar)
        all_classes.append(1)
    return np.array(all_imgs), np.array(all_classes)

X_train,y_train=load_images('./train',1000)
X_test,y_test=load_images('./train',400)
train_generator=train_datagen.flow(X_train,y_train,batch_size=64,seed=13)
test_generator=test_datagen.flow(X_test,y_test,batch_size=64,seed=13)

# Load Inception v3 model.Do not load the final layer
base_model=InceptionV3 (weights='imagenet', include_top=False)
# Final Layer Configuration
x = base_model.output
x = GlobalAveragePooling 2D()(x)
predictions=Dense(1, kernel_initializer="glorot_uniform", activation="sigmoid", kernel_regularizer=l2(.0005))(x)

model=Model(inputs=base_model.input, outputs=predictions)

#base_model does not update weights
for layer in base_model.layers:
    layer.trainable=False

opt=SGD(lr=.01, momentum=.9)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])

checkpoint=ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=True)
csv_logger = CSVLogger('model.log')

reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                  US>patience=5,min_lr=0.001)

history=model.fit_generator(train_generator,
                    steps_per_epoch = 2000,
                    epochs = 10,
                    validation_data=test_generator,
                    validation_steps = 800,
                    verbose = 1,
                    callbacks = [reduce_lr, csv_logger, checkpointer])

Epoch 1/2
1/5 [=====>............] - ETA: 264s-loss: 0.7831 - acc: 0.6094
2/5 [===========>........] - ETA: 154s-loss: 0.7622-acc: 0.5859
3/5 [=================>........] - ETA:85s-loss:0.7396-acc:0.5729 
4/5 [=======================>...] - ETA:38s-loss:0.7270-acc:0.5703