In [1]:

    

from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K

K.clear_session()
sess = tf.Session()
K.set_session(sess)


    

    




Using TensorFlow backend.

In [2]:

    

from tensorflow.core.framework import graph_pb2
with open('../data/dog_breed_graph_v2.pb', "rb") as f:
    output_graph_def = graph_pb2.GraphDef()
    output_graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(output_graph_def, name="")


    

In [3]:

    

x = sess.graph.get_tensor_by_name('input_3:0')


    

In [4]:

    

y = sess.graph.get_tensor_by_name('softmax0:0')


    

In [5]:

    

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

img = plt.imread('dalmatian.png')
plt.imshow(img)


    

    
Out[5]:





<matplotlib.image.AxesImage at 0x12d083e80>






    

In [6]:

    

def preprocess_input(x):
    x_copy = np.copy(x)
    x_copy -= 0.5
    x_copy *= 2.
    return x_copy


    

In [7]:

    

img_processed = preprocess_input(img)
plt.imshow(img_processed)


    

    
Out[7]:





<matplotlib.image.AxesImage at 0x12dc839e8>






    

In [8]:

    

imgs = np.expand_dims(img_processed, 0)


    

In [9]:

    

new_scores = sess.run(y, feed_dict={x: imgs})


    

In [10]:

    

# Dalmatian is 56
print(np.argmax(new_scores))


    

    




56