| # encoder | |
| from tensorflow.models.rnn.rnn_cell import LSTMCell | |
| read_size = 2*read_n*read_n if FLAGS.read_attn else 2*img_size | |
| lstm_enc = LSTMCell(enc_size, read_size+dec_size) # encoder Op | |
| def encode(state,input): | |
| with tf.variable_scope("encoder",reuse=DO_SHARE): | |
| return lstm_enc(input,state) |
| def read_attn(x,x_hat,h_dec_prev): | |
| Fx,Fy,gamma=attn_window("read",h_dec_prev,read_n) | |
| def filter_img(img,Fx,Fy,gamma,N): | |
| Fxt=tf.transpose(Fx,perm=[0,2,1]) | |
| img=tf.reshape(img,[-1,B,A]) | |
| glimpse=tf.batch_matmul(Fy,tf.batch_matmul(img,Fxt)) | |
| glimpse=tf.reshape(glimpse,[-1,N*N]) | |
| return glimpse*tf.reshape(gamma,[-1,1]) | |
| x=filter_img(x,Fx,Fy,gamma,read_n) # batch x (read_n*read_n) | |
| x_hat=filter_img(x_hat,Fx,Fy,gamma,read_n) |
| def filterbank(gx, gy, sigma2,delta, N): | |
| grid_i = tf.reshape(tf.cast(tf.range(N), tf.float32), [1, -1]) | |
| mu_x = gx + (grid_i - N / 2 - 0.5) * delta # eq 19 | |
| mu_y = gy + (grid_i - N / 2 - 0.5) * delta # eq 20 | |
| a = tf.reshape(tf.cast(tf.range(A), tf.float32), [1, 1, -1]) | |
| b = tf.reshape(tf.cast(tf.range(B), tf.float32), [1, 1, -1]) | |
| mu_x = tf.reshape(mu_x, [-1, N, 1]) | |
| mu_y = tf.reshape(mu_y, [-1, N, 1]) | |
| sigma2 = tf.reshape(sigma2, [-1, 1, 1]) | |
| Fx = tf.exp(-tf.square((a - mu_x) / (2*sigma2))) # 2*sigma2? |
| def linear(x,output_dim): | |
| """ | |
| affine transformation Wx+b | |
| assumes x.shape = (batch_size, num_features) | |
| """ | |
| w=tf.get_variable("w", [x.get_shape()[1], output_dim]) | |
| b=tf.get_variable("b", [output_dim], initializer=tf.constant_initializer(0.0)) | |
| return tf.matmul(x,w)+b | |
| def attn_window(scope,h_dec,N): |
| def read_no_attn(x,x_hat,h_dec_prev): | |
| return tf.concat(1,[x,x_hat]) |
| # applying read filter. inspired by https://github.com/ikostrikov/TensorFlow-VAE-GAN-DRAW/blob/master/main-draw.py | |
| Fxt=tf.transpose(Fx, [0,2,1]) # batch x N x A | |
| Fxt=tf.reshape(Fxt, [-1,1,A,N,1]) # batch x 1 x A x N x 1 | |
| Fxt=tf.tile(Fxt, [1,N,1,1,1]) # batch x N x A x N x 1 (repmat'ed along dim=1) | |
| Fy=tf.reshape(Fy, [-1,N,B,1,1]) # batch x N x B x 1 x 1 | |
| x=tf.reshape(x,[-1,1,B,A,1]) # batch x 1 x B x A x 1 | |
| x=tf.tile(x,[1,N,1,1,1]) # batch x N x B x A x 1 | |
| Fydotx=tf.reduce_sum(Fy*x,2) # batch x N x A x 1 | |
| Fydotx=tf.reshape(x,[-1,N,A,1,1]) # batch x N x A x 1 x 1 | |
| FydotxdotFxt=tf.reduce_sum(Fydotx*Fxt,2) # batch x N x N x 1 |
| cs,mus,logsigmas,sigmas=[0]*T,[0]*T,[0]*T,[0]*T # parameters we'll need to access later | |
| # initial states | |
| DO_SHARE=False | |
| h_dec_prev=tf.zeros((batch_size,dec_size)) | |
| enc_state=lstm_enc.zero_state(batch_size, tf.float32) | |
| dec_state=lstm_dec.zero_state(batch_size, tf.float32) | |
| # build the graph | |
| for t in range(T): | |
| c_prev = tf.zeros((batch_size,img_size)) if t==0 else cs[t-1] | |
| x_hat=x-tf.sigmoid(c_prev) # error image |
| for i in range(TRAIN_ITERS): | |
| x= np.random.normal(mu,sigma,M) # sample minibatch from p_data | |
| z= np.linspace(-5.0,5.0,M)+np.random.random(M)*0.01 # sample minibatch from noise prior | |
| sess.run(opt_d, {x_node: x, z_node: z}) # update discriminator D | |
| z= np.linspace(-5.0,5.0,M)+np.random.random(M)*0.01 # sample noise prior | |
| sess.run(opt_g, {z_node: z}) # update generator G |
Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.
First, install https://www.docker.com/docker-toolbox
Since this is Windows, creating the Docker group "docker" is not necessary.
| import requests | |
| from bs4 import BeautifulSoup | |
| import re | |
| for v in range(1,18): # update chapter numbers as needed | |
| fname = 'Volume_%02d.jpg' % v | |
| url = 'http://prison-school.wikia.com/wiki/Category:Volume_Cover_Images?file=%s' % fname | |
| res = requests.get(url) | |
| soup = BeautifulSoup(res.text, 'html.parser') | |
| pattern = r'http:\/\/vignette\d.wikia.nocookie.net\/prison-school\/images\/\w*\/\w*\/' + fname + '\/revision\/latest\/' | |
| r = re.compile(pattern) |