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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:
 This notebook is an attempt to test and prototype using a pretrained tensorflow CNN to do classification. The end goal is to be able to take add the following as an anime-specific feature
Step1:  Images are 224x224 pixels, with 3 channels. Batch size is 50. This is specified in the caffemodel but not in the tf class (mynet.py)
Step2:  Now to actually load the model.
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Python Code:
import numpy as np
import tensorflow as tf
import os.path as osp
Explanation: This notebook is an attempt to test and prototype using a pretrained tensorflow CNN to do classification. The end goal is to be able to take add the following as an anime-specific feature: a CNN's embeddings of the default image on MyAnimeList for that anime. The idea is that there is certain visual content that goes into a person's enjoyment of an anime: art style, character design, and color scheme, for example. I want that information via a high-level representation of the image in a deep CNN pipeline. This notebook is simply an attempt getting getting the CNN part to work. 
The CNN is is downloaded from Illustration2Vec (Saito & Matsui, 2015) and is pretrained on anime images. That means the feature space is uniquely suited to capturing relevant information from anime. However, this was done on Caffe, which I do not have installed. Consequently, I used the caffe-tensorflow tool (https://github.com/ethereon/caffe-tensorflow) to convert this model into a tensorflow model (via an Amazon EC2 instance).
There are examples but no clear tutorials on how to use the caffe-tensorflow tool, so this exploratory notebook is an attempt to get it to work.
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input_size = {50, 3, 224, 224} 
fake_data = np.random.rand(2, 224, 224, 3)
Explanation: Images are 224x224 pixels, with 3 channels. Batch size is 50. This is specified in the caffemodel but not in the tf class (mynet.py)
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from mynet import CaffeNet
images = tf.placeholder(tf.float32, [None, 224, 224, 3])
net = CaffeNet({'data':images})
sesh = tf.Session()
sesh.run(tf.global_variables_initializer())
# Load the data
net.load('mynet.npy', sesh)
# Forward pass
output = sesh.run(net.get_output(), feed_dict={images: fake_data})
sesh.close()
Explanation: Now to actually load the model.
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