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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 TensorFlow Inception-v3 Feature Extraction setup
Step1:  Some Functions for working with Inception-v3
Step2:  Putting it to use!
Step3:  How about more than one image
If you put your images folders sorted by class (e.g.
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Python Code:
import os
import tensorflow as tf
# import tensorflow.python.platform
from tensorflow.python.platform import gfile
import numpy as np
import pandas as pd
Explanation: TensorFlow Inception-v3 Feature Extraction setup:
Special thanks to KERNIX for their extremely helpful blog
http://www.kernix.com/blog/image-classification-with-a-pre-trained-deep-neural-network_p11
You can follow along at home!
install tensorflow:
conda install -c conda-forge tensorflow
navigate to tensorflow directory:
cd anaconda/lib/python2.7/site-packages/tensorflow/models/image/imagenet
run python command to set up imagenet neural network:
python classify_image.py --model_dir &lt;desired model location&gt;/imagenet
in my case this was:
python classify_image.py --model_dir ~/coradek/CNW_Wildlife_Identification/imagenet
End of explanation
# Create the TensorFlow graph
def create_graph():
    model_dir = './CNW_Wildlife_Identification/imagenet'
    with gfile.FastGFile(os.path.join(
        model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')
# Start a TensorFlow Session and choose our "tensor" for feature extraction
#     ('pool_3:0' is the last layer before classification)
def setup():
    
    create_graph()  # Only needs to run the first time
    with tf.Session() as sess:
        # Get the last feature layer (preclassification) from inception-v3
        next_to_last_tensor = sess.graph.get_tensor_by_name('pool_3:0')
        s = sess
        t = next_to_last_tensor
        return s,t
# Get the actual features!
def get_features(image, session, tensor):
    if not gfile.Exists(image):
        tf.logging.fatal('File does not exist %s', image)
    image_data = gfile.FastGFile(image, 'rb').read()
    predictions = session.run(tensor,
                        {'DecodeJpeg/contents:0': image_data})
    features = np.squeeze(predictions)
    return features.reshape(1,-1)
Explanation: Some Functions for working with Inception-v3
End of explanation
session, tensor = setup()
features = get_features('CNW_Wildlife_Identification/data/first_sample/EK000026-2.JPG',
                        session, tensor)
print features
Explanation: Putting it to use!
End of explanation
directory = 'CNW_Wildlife_Identification/data/first_sample'
image_list = []
for p, dirs, files in os.walk(directory):
    for ff in files:
        if ff[-4:].lower() == '.jpg':
            image_list.append(p+'/'+ff)
image_list
# In practice this is only run once - so setup() is included as part of this function
def get_features_repeatedly(image_list):
    '''take list of image file paths
    return numpy array of features
    '''
    
    create_graph()
    with tf.Session() as session:
        # Get the last feature layer (preclassification) from inception-v3
        tensor = session.graph.get_tensor_by_name('pool_3:0')
    nb_features = 2048
    features = np.empty((len(image_list),nb_features))
    for ind, image in enumerate(image_list):
        
#         if (ind%50 == 0):
#             print('Processing %s...' % (image))
        
        print('Processing %s...' % (image))
        if not gfile.Exists(image):
            tf.logging.fatal('File does not exist %s', image)
        image_data = gfile.FastGFile(image, 'rb').read()
        predictions = session.run(tensor,
                            {'DecodeJpeg/contents:0': image_data})
        features[ind,:] = np.squeeze(predictions)
    return features
lotsafeatures = get_features_repeatedly(image_list)
Explanation: How about more than one image
If you put your images folders sorted by class (e.g.
End of explanation
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