```
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Input

# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = '/Users/michael/testdata/train' #contains two classes cats and dogs
validation_data_dir = '/Users/michael/testdata/validation' #contains two classes cats and dogs

nb_train_samples = 1200
nb_validation_samples = 800
nb_epoch = 50

# 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(200, 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='rmsprop', loss='categorical_crossentropy')
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
        rescale=1./255)#,
 #       shear_range=0.2,
 #       zoom_range=0.2,
 #       horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=16,
    class_mode='categorical'
)

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=16,
    class_mode='categorical'
)

print "start history model"
history = model.fit_generator(
    train_generator,
    nb_epoch=nb_epoch,
    samples_per_epoch=128,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples) #1020

# 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[:172]:
   layer.trainable = False
for layer in model.layers[172:]:
   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')

# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
#model.fit_generator(...)
# fine-tune the model
model.fit_generator(
        train_generator,
        samples_per_epoch=nb_train_samples,
        nb_epoch=nb_epoch,
        validation_data=validation_generator,
        nb_val_samples=nb_validation_samples)

model.save("inception_retrained_model1")


```