kerasで学習が終わりません

Question

https://qiita.com/slowsingle/items/64cc927bb29a49a7af14←こちらのコードを使用しました
http://ai-coordinator.jp/ssd-keras-train
https://qiita.com/slowsingle/items/9006383145a650c84cb0
こちらの記事を参考にして同じことをやっているのですが、途中で学習が止まってしまい、次のエポックへ移行しません。

こちらがterminalの出力です。最後の7/8から進みません。

/Users/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Epoch 1/2
7/8 [=========================>....] - ETA: 21s - loss: 6.7430 

import cv2
import keras
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.models import Model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
import pickle
from random import shuffle
from scipy.misc import imread
from scipy.misc import imresize
import tensorflow as tf

from ssd import SSD300
from ssd_training import MultiboxLoss
from ssd_utils import BBoxUtility

plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'

np.set_printoptions(suppress=True)

# 21
NUM_CLASSES = 21 #4
input_shape = (300, 300, 3)

priors = pickle.load(open('prior_boxes_ssd300.pkl', 'rb'))
bbox_util = BBoxUtility(NUM_CLASSES, priors)

# gt = pickle.load(open('gt_pascal.pkl', 'rb'))
gt = pickle.load(open('VOC2007.pkl', 'rb'))
keys = sorted(gt.keys())
num_train = int(round(0.8 * len(keys)))
train_keys = keys[:num_train]
val_keys = keys[num_train:]
num_val = len(val_keys)

##teratailの文字数制限のため省略、省略箇所を確認したい場合はお手数ですが連絡をください

nb_epoch = 2
history = model.fit_generator(gen.generate(True), gen.train_batches,
                              nb_epoch, verbose=1,
                              callbacks=callbacks,
                              validation_data=gen.generate(False),
                              nb_val_samples=gen.val_batches,
                              nb_worker=1)

inputs = []
images = []
img_path = path_prefix + sorted(val_keys)[0]
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
inputs = preprocess_input(np.array(inputs))

preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds)

for i, img in enumerate(images):
    # Parse the outputs.
    det_label = results[i][:, 0]
    det_conf = results[i][:, 1]
    det_xmin = results[i][:, 2]
    det_ymin = results[i][:, 3]
    det_xmax = results[i][:, 4]
    det_ymax = results[i][:, 5]

    # Get detections with confidence higher than 0.6.
    top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]

    top_conf = det_conf[top_indices]
    top_label_indices = det_label[top_indices].tolist()
    top_xmin = det_xmin[top_indices]
    top_ymin = det_ymin[top_indices]
    top_xmax = det_xmax[top_indices]
    top_ymax = det_ymax[top_indices]

    colors = plt.cm.hsv(np.linspace(0, 1, NUM_CLASSES)).tolist()

    plt.imshow(img / 255.)
    currentAxis = plt.gca()

    for i in range(top_conf.shape[0]):
        xmin = int(round(top_xmin[i] * img.shape[1]))
        ymin = int(round(top_ymin[i] * img.shape[0]))
        xmax = int(round(top_xmax[i] * img.shape[1]))
        ymax = int(round(top_ymax[i] * img.shape[0]))
        score = top_conf[i]
        label = int(top_label_indices[i])
        # label_name = voc_classes[label - 1]
        display_txt = '{:0.2f}, {}'.format(score, label)
        coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
        color = colors[label]
        currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
        currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})

    plt.show()