keras: Keras freezing on last batch of first epoch (can't move to second epoch)

I’m using Keras 2.1.1 and Tensorflow 1.4, Python 3.6, Windows 7.

I’m attempting transfer learning using the Inception model. The code is straight from the Keras Application API, just a few tweaks (using my data).

Here is the code

from keras.preprocessing import image```
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
from keras import optimizers


img_width, img_height = 299, 299
train_data_dir = r'C:\Users\Moondra\Desktop\Keras Applications\data\train'
total_samples = 13581
batch_size = 3
epochs = 5


train_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
zoom_range = 0.1,
rotation_range=15)



train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size = (img_height, img_width),
batch_size = batch_size, 
class_mode = 'categorical')  #class_mode = 'categorical'


# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(12, activation='softmax')(x)

# this is the model we will train
model = Model(input=base_model.input, output=predictions)

# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
    layer.trainable = False

# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer=optimizers.SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics = ['accuracy'])

# train the model on the new data for a few epochs
model.fit_generator(
train_generator,
steps_per_epoch = 20,
epochs = epochs)


# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.

# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
   print(i, layer.name)

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 172 layers and unfreeze the rest:
for layer in model.layers[:249]:
   layer.trainable = False
for layer in model.layers[249:]:
   layer.trainable = True

# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics = ['accuracy'])

# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(
train_generator,
steps_per_epoch = 25,
epochs = epochs)`


Output is

Found 13581 images belonging to 12 classes.

Warning (from warnings module):
  File "C:\Users\Moondra\Desktop\Keras Applications\keras_transfer_learning_inception_problem_one_epoch.py", line 44
    model = Model(input=base_model.input, output=predictions)
UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor("in..., outputs=Tensor("de...)`
Epoch 1/5

 1/20 [>.............................] - ETA: 38s - loss: 2.8652 - acc: 0.0000e+00
 3/20 [===>..........................] - ETA: 12s - loss: 2.6107 - acc: 0.1111    
 4/20 [=====>........................] - ETA: 8s - loss: 2.6454 - acc: 0.0833 
 5/20 [======>.......................] - ETA: 6s - loss: 2.6483 - acc: 0.0667
 6/20 [========>.....................] - ETA: 5s - loss: 2.6863 - acc: 0.0556
 7/20 [=========>....................] - ETA: 4s - loss: 2.6230 - acc: 0.0952
 8/20 [===========>..................] - ETA: 3s - loss: 2.6212 - acc: 0.0833
 9/20 [============>.................] - ETA: 3s - loss: 2.6192 - acc: 0.1111
10/20 [==============>...............] - ETA: 2s - loss: 2.6223 - acc: 0.1000
11/20 [===============>..............] - ETA: 2s - loss: 2.6626 - acc: 0.0909
12/20 [=================>............] - ETA: 2s - loss: 2.6562 - acc: 0.1111
13/20 [==================>...........] - ETA: 1s - loss: 2.6436 - acc: 0.1282
14/20 [====================>.........] - ETA: 1s - loss: 2.6319 - acc: 0.1190
15/20 [=====================>........] - ETA: 1s - loss: 2.6343 - acc: 0.1111
Warning (from warnings module):
  File "C:\Users\Moondra\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\callbacks.py", line 116
    % delta_t_median)
UserWarning: Method on_batch_end() is slow compared to the batch update (0.102000). Check your callbacks.

16/20 [=======================>......] - ETA: 0s - loss: 2.6310 - acc: 0.1042
17/20 [========================>.....] - ETA: 0s - loss: 2.6207 - acc: 0.1176
18/20 [==========================>...] - ETA: 0s - loss: 2.6063 - acc: 0.1296
19/20 [===========================>..] - ETA: 0s - loss: 2.6056 - acc: 0.1228




It just hangs at the 19/20.

I already asked on stack overflow but no help.

https://stackoverflow.com/questions/47382952/cant-get-past-first-epoch-just-hangs-keras-transfer-learning-inception