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plot_data.py
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plot_data.py
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# takes data saved by DRAW model and generates animations
# example usage: python plot_data.py noattn /tmp/draw/draw_data.npy
import matplotlib
import sys
import numpy as np
interactive=False # set to False if you want to write images to file
if not interactive:
matplotlib.use('Agg') # Force matplotlib to not use any Xwindows backend.
import matplotlib.pyplot as plt
def xrecons_grid(X,B,A):
"""
plots canvas for single time step
X is x_recons, (batch_size x img_size)
assumes features = BxA images
batch is assumed to be a square number
"""
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
batch_size=X.shape[0]
N=int(np.sqrt(batch_size))
X=X.reshape((N,N,B,A))
img=np.ones((N*ph,N*pw))*padval
for i in range(N):
for j in range(N):
startr=i*ph+padsize
endr=startr+B
startc=j*pw+padsize
endc=startc+A
img[startr:endr,startc:endc]=X[i,j,:,:]
return img
if __name__ == '__main__':
prefix=sys.argv[1]
out_file=sys.argv[2]
[C,Lxs,Lzs]=np.load(out_file)
T,batch_size,img_size=C.shape
X=1.0/(1.0+np.exp(-C)) # x_recons=sigmoid(canvas)
B=A=int(np.sqrt(img_size))
if interactive:
f,arr=plt.subplots(1,T)
for t in range(T):
img=xrecons_grid(X[t,:,:],B,A)
if interactive:
arr[t].matshow(img,cmap=plt.cm.gray)
arr[t].set_xticks([])
arr[t].set_yticks([])
else:
plt.matshow(img,cmap=plt.cm.gray)
imgname='%s_%d.png' % (prefix,t) # you can merge using imagemagick, i.e. convert -delay 10 -loop 0 *.png mnist.gif
plt.savefig(imgname)
print(imgname)
f=plt.figure()
plt.plot(Lxs,label='Reconstruction Loss Lx')
plt.plot(Lzs,label='Latent Loss Lz')
plt.xlabel('iterations')
plt.legend()
if interactive:
plt.show()
else:
plt.savefig('%s_loss.png' % (prefix))