def show_recons_samples(sess, ckptfile):
# visualize sample reconstructions
e2c.saver.restore(sess, ckptfile) # restore variable values
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
(x_val,u_val,x_next_val)=dataset.sample(e2c.batch_size)
xr,xp=sess.run([e2c.x_recons, e2c.x_predict],feed_dict={e2c.x:x_val,e2c.u:u_val,e2c.x_next:x_next_val})
#xr,xp=sess.run([e2c.x_recons0, e2c.x_predict0],feed_dict={e2c.x0:x_val,e2c.u0:u_val,e2c.x1:x_next_val})
A,B=e2c.A,e2c.B
def getimgs(x,xnext):
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
img=np.ones((10*ph,2*pw))*padval
for i in range(10):
startr=i*ph+padsize
img[startr:startr+B,padsize:padsize+A]=x[i,:].reshape((A,B))
for i in range(10):
startr=i*ph+padsize
img[startr:startr+B,pw+padsize:pw+padsize+A]=xnext[i,:].reshape((A,B))
return img
fig,arr=plt.subplots(1,2)
arr[0].matshow(getimgs(x_val,x_next_val),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[0].set_title('Data')
arr[1].matshow(getimgs(xr,xp),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[1].set_title('Reconstruction')
plt.show()
def viz_z(sess, ckptfile):
e2c.saver.restore(sess,ckptfile) # restore variable values
dataset=PlaneData("data/plane1.npz","data/env1.png")
Ps,NPs=dataset.getPSpace()
batch_size=e2c.batch_size
n0=NPs.shape[0]
if False:
Ps=np.vstack((Ps,NPs))
xy=np.zeros([Ps.shape[0], 2])
xy[:,0]=Ps[:,1]
xy[:,1]=20-Ps[:,0] # for the purpose of computing theta, map centered @ origin
Zs=np.zeros([Ps.shape[0], e2c.z_dim])
theta=np.arctan(xy[:,1]/xy[:,0])
for i in range(Ps.shape[0] // batch_size):
print("batch %d" % i)
x_val=dataset.getXPs(Ps[i*batch_size:(i+1)*batch_size,:])
Zs[i*batch_size:(i+1)*batch_size,:]=sess.run(e2c.z, {e2c.x:x_val})
# last remaining points may not fit precisely into 1 minibatch.
x_val=dataset.getXPs(Ps[-batch_size:,:])
Zs[-batch_size:,:]=sess.run(e2c.z, {e2c.x:x_val})
if False:
theta[-n0:]=1;
fig,arr=plt.subplots(1,2)
arr[0].scatter(Ps[:,1], 40-Ps[:,0], c=(np.pi+theta)/(2*np.pi))
arr[0].set_title('True State Space')
arr[1].scatter(Zs[:,0],Zs[:,1], c=(np.pi+theta)/(2*np.pi))
arr[1].set_title('Latent Space Z')
#plt.show()
return fig
def show_recons_seq(sess, ckptfile):
e2c.saver.restore(sess, ckptfile) # restore variable values
dataset = PlaneData("data/plane1.npz", "data/env1.png")
dataset.initialize()
T = e2c.T
print(T)
(x_vals, u_vals) = dataset.sample_seq(e2c.batch_size, T)
feed_dict = {}
for t in range(T):
feed_dict[e2c.xs[t]] = x_vals[t]
for t in range(T - 1):
feed_dict[e2c.us[t]] = u_vals[t]
fetches = e2c.x_recons + e2c.x_predicts
results = sess.run(fetches, feed_dict)
xr = results[:T - 1]
xp = results[T - 1:]
A, B = e2c.A, e2c.B
def getimgs(x):
padsize = 1
padval = .5
ph = B + 2 * padsize
pw = A + 2 * padsize
img = np.ones((ph, len(x) * pw)) * padval
for t in range(len(x)):
startc = t * pw + padsize
img[padsize:padsize + B, startc:startc + A] = x[t][1, :].reshape(
(A, B))
return img
fig, arr = plt.subplots(3, 1)
arr[0].matshow(getimgs(x_vals), cmap=plt.cm.gray, vmin=0, vmax=1)
arr[0].set_title('X')
arr[1].matshow(getimgs(xr), cmap=plt.cm.gray, vmin=0, vmax=1)
arr[1].set_title('Reconstruction')
arr[2].matshow(getimgs(xp), cmap=plt.cm.gray, vmin=0, vmax=1)
arr[2].set_title('Prediction')
plt.show()
def show_recons_seq(sess, ckptfile):
e2c.saver.restore(sess, ckptfile) # restore variable values
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
T=e2c.T
print(T)
(x_vals,u_vals)=dataset.sample_seq(e2c.batch_size,T)
feed_dict={}
for t in range(T):
feed_dict[e2c.xs[t]] = x_vals[t]
for t in range(T-1):
feed_dict[e2c.us[t]] = u_vals[t]
fetches=e2c.x_recons + e2c.x_predicts
results=sess.run(fetches,feed_dict)
xr=results[:T-1]
xp=results[T-1:]
A,B=e2c.A,e2c.B
def getimgs(x):
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
img=np.ones((ph,len(x)*pw))*padval
for t in range(len(x)):
startc=t*pw+padsize
img[padsize:padsize+B, startc:startc+A]=x[t][1,:].reshape((A,B))
return img
fig,arr=plt.subplots(3,1)
arr[0].matshow(getimgs(x_vals),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[0].set_title('X')
arr[1].matshow(getimgs(xr),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[1].set_title('Reconstruction')
arr[2].matshow(getimgs(xp),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[2].set_title('Prediction')
plt.show()
tf.scalar_summary("loss", loss)
tf.scalar_summary("L_x", tf.reduce_mean(L_x))
tf.scalar_summary("L_x_next", tf.reduce_mean(L_x_next))
tf.scalar_summary("L_z", tf.reduce_mean(L_z))
tf.scalar_summary("KL", tf.reduce_mean(KL))
all_summaries = tf.merge_all_summaries()
# TRAIN
if __name__ == "__main__":
init = tf.initialize_all_variables()
sess = tf.InteractiveSession()
sess.run(init)
# WRITER
writer = tf.train.SummaryWriter("/ltmp/e2c", sess.graph_def)
dataset = PlaneData("data/plane1.npz", "data/env1.png")
dataset.initialize()
# tmp
# (x_val,u_val,x_next_val)=dataset.sample(batch_size, replace=False)
# feed_dict={
# x:x_val,
# u:u_val,
# x_next:x_next_val
# }
# results=sess.run([L_x,L_x_next,L_z,L_bound,KL],feed_dict)
# pdb.set_trace()
# resume training
#saver.restore(sess, "/ltmp/e2c-plane-83000.ckpt")
train_iters = 2e5 # 5K iters
for i in range(int(train_iters)):
tf.scalar_summary("loss", loss)
tf.scalar_summary("L_x", tf.reduce_mean(L_x))
tf.scalar_summary("L_x_next", tf.reduce_mean(L_x_next))
tf.scalar_summary("L_z", tf.reduce_mean(L_z))
tf.scalar_summary("KL",tf.reduce_mean(KL))
all_summaries = tf.merge_all_summaries()
# TRAIN
if __name__=="__main__":
init=tf.initialize_all_variables()
sess=tf.InteractiveSession()
sess.run(init)
# WRITER
writer = tf.train.SummaryWriter("/ltmp/e2c", sess.graph_def)
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
# tmp
# (x_val,u_val,x_next_val)=dataset.sample(batch_size, replace=False)
# feed_dict={
# x:x_val,
# u:u_val,
# x_next:x_next_val
# }
# results=sess.run([L_x,L_x_next,L_z,L_bound,KL],feed_dict)
# pdb.set_trace()
# resume training
#saver.restore(sess, "/ltmp/e2c-plane-83000.ckpt")
train_iters=2e5 # 5K iters
for i in range(int(train_iters)):
# tf.scalar_summary("L_x", tf.reduce_mean(L_x))
# tf.scalar_summary("L_x_next", tf.reduce_mean(L_x_next))
# tf.scalar_summary("L_z", tf.reduce_mean(L_z))
# tf.scalar_summary("KL",tf.reduce_mean(KL))
all_summaries = tf.merge_all_summaries()
# TRAIN
if __name__=="__main__":
init=tf.initialize_all_variables()
sess=tf.InteractiveSession()
sess.run(init)
# WRITER
writer = tf.train.SummaryWriter("/ltmp/e2c", sess.graph_def)
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
# resume training
saver.restore(sess, "/ltmp/e2c-plane-single1.ckpt")
# test to make sure samples are actually trajectories
def getimgs(x):
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
img=np.ones((ph,len(x)*pw))*padval
for t in range(len(x)):
startc=t*pw+padsize
# tf.scalar_summary("L_x", tf.reduce_mean(L_x))
# tf.scalar_summary("L_x_next", tf.reduce_mean(L_x_next))
# tf.scalar_summary("L_z", tf.reduce_mean(L_z))
# tf.scalar_summary("KL",tf.reduce_mean(KL))
all_summaries = tf.merge_all_summaries()
# TRAIN
if __name__=="__main__":
init=tf.initialize_all_variables()
sess=tf.InteractiveSession()
sess.run(init)
# WRITER
writer = tf.train.SummaryWriter("/ltmp/e2c", sess.graph_def)
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
# resume training
saver.restore(sess, "/ltmp/e2c-plane-single1.ckpt")
# test to make sure samples are actually trajectories
def getimgs(x):
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
img=np.ones((ph,len(x)*pw))*padval
for t in range(len(x)):
startc=t*pw+padsize