def train_rnn(params):
rng = RandomStreams(seed=1234)
(X_train, Y_train, S_Train_list, F_list_train, G_list_train, X_test,
Y_test, S_Test_list, F_list_test, G_list_test) = du.load_pose(params)
F_list_train, G_list_train = du.shuffle_in_unison_inplace(
F_list_train, G_list_train)
params["len_train"] = len(F_list_train)
params["len_test"] = len(F_list_test)
u.start_log(params)
batch_size = params['batch_size']
n_train_batches = len(F_list_train)
n_train_batches /= batch_size
n_test_batches = len(F_list_test)
n_test_batches /= batch_size
nb_epochs = params['n_epochs']
print("Batch size: %i, train batch size: %i, test batch size: %i" %
(batch_size, n_train_batches, n_test_batches))
u.log_write("Model build started", params)
if params['run_mode'] == 1:
model = model_provider.get_model_pretrained(params, rng)
u.log_write("Pretrained loaded: %s" % (params['mfile']), params)
else:
model = model_provider.get_model(params, rng)
u.log_write("Number of parameters: %s" % (model.n_param), params)
train_errors = np.ndarray(nb_epochs)
u.log_write("Training started", params)
val_counter = 0
best_loss = 1000
for epoch_counter in range(nb_epochs):
batch_loss = 0.
# H=C=np.zeros(shape=(batch_size,params['n_hidden']), dtype=dtype) # initial hidden state
sid = 0
for minibatch_index in range(n_train_batches):
x_lst = F_list_train[minibatch_index *
batch_size:(minibatch_index + 1) *
batch_size] #60*20*1024
y_lst = G_list_train[minibatch_index *
batch_size:(minibatch_index + 1) *
batch_size] #60*20*1024
x, y = du.load_batch(params, x_lst, y_lst)
# x=X_train[id_lst] #60*20*1024
# y=Y_train[id_lst]#60*20*54
is_train = 1
if (params["model"] == "blstmnp"):
x_b = np.asarray(map(np.flipud, x))
loss = model.train(x, x_b, y)
else:
loss = model.train(x, y, is_train)
batch_loss += loss
if params['shufle_data'] == 1:
F_list_train, G_list_train = du.shuffle_in_unison_inplace(
F_list_train, G_list_train)
train_errors[epoch_counter] = batch_loss
batch_loss /= n_train_batches
s = 'TRAIN--> epoch %i | error %f' % (epoch_counter, batch_loss)
u.log_write(s, params)
if (epoch_counter % 1 == 0):
print("Model testing")
batch_loss3d = []
H = C = np.zeros(
shape=(batch_size, params['n_hidden']),
dtype=dtype) # resetting initial state, since seq change
sid = 0
for minibatch_index in range(n_test_batches):
x_lst = F_list_test[minibatch_index *
batch_size:(minibatch_index + 1) *
batch_size] #60*20*1024
y_lst = G_list_test[minibatch_index *
batch_size:(minibatch_index + 1) *
batch_size] #60*20*1024
x, y = du.load_batch(params, x_lst, y_lst)
# tmp_sid=S_Test_list[(minibatch_index + 1) * batch_size-1]
# if(sid==0):
# sid=tmp_sid
# if(tmp_sid!=sid):
# sid=tmp_sid
# H=C=np.zeros(shape=(batch_size,params['n_hidden']), dtype=dtype) # resetting initial state, since seq change
# x=X_test[id_lst] #60*20*1024
# y=Y_test[id_lst]#60*20*54
is_train = 0
if (params["model"] == "blstmnp"):
x_b = np.asarray(map(np.flipud, x))
pred = model.predictions(x, x_b)
else:
pred = model.predictions(x, is_train)
loss3d = u.get_loss(params, y, pred)
batch_loss3d.append(loss3d)
batch_loss3d = np.nanmean(batch_loss3d)
if (batch_loss3d < best_loss):
best_loss = batch_loss3d
ext = str(epoch_counter) + "_" + str(batch_loss3d) + "_best.p"
u.write_params(model.params, params, ext)
else:
ext = str(val_counter % 2) + ".p"
u.write_params(model.params, params, ext)
val_counter += 1 #0.08
s = 'VAL--> epoch %i | error %f, %f' % (val_counter, batch_loss3d,
n_test_batches)
u.log_write(s, params)
print("Test sample size: %i, Batch size: %i, test batch size: %i"%(len(F_list_test),batch_size,n_test_batches))
print ("Model loading started")
model= model_provider.get_model_pretrained(params,rng)
print("Prediction started")
batch_loss = 0.
batch_loss3d = 0.
batch_bb_loss = 0.
loss_list=[]
last_index=0
first_index=0
sq_loss_lst=[]
for minibatch_index in range(n_test_batches):
x_lst=F_list_test[minibatch_index * batch_size: (minibatch_index + 1) * batch_size] #60*20*1024
y_lst=G_list_test[minibatch_index * batch_size: (minibatch_index + 1) * batch_size] #60*20*1024
x,y=du.load_batch(params,x_lst,y_lst)
last_index=first_index+sum([len(i) for i in y])
first_index=last_index
if(params["model"]=="blstmnp"):
x_b=np.asarray(map(np.flipud,x))
pred = model.predictions(x,x_b,is_train)
else:
pred = model.predictions(x,is_train)
# print("Prediction done....")
if residual>0:
if(minibatch_index==n_test_batches-1):
pred = pred[0:(len(pred)-residual)]
y=y[0:(len(y)-residual)]
# n_list=n_list[0:(len(n_list)-residual)]
def train_rnn(params):
rng = RandomStreams(seed=1234)
(X_train,Y_train,S_Train_list,F_list_train,G_list_train,X_test,Y_test,S_Test_list,F_list_test,G_list_test)=du.load_pose(params)
params["len_train"]=len(F_list_train)
params["len_test"]=len(F_list_test)
u.start_log(params)
batch_size=params['batch_size']
n_train_batches = len(F_list_train)
n_train_batches /= batch_size
n_test_batches = len(F_list_test)
n_test_batches /= batch_size
nb_epochs=params['n_epochs']
print("Batch size: %i, train batch size: %i, test batch size: %i"%(batch_size,n_train_batches,n_test_batches))
u.log_write("Model build started",params)
if params['run_mode']==1:
model= model_provider.get_model_pretrained(params,rng)
u.log_write("Pretrained loaded: %s"%(params['mfile']),params)
else:
model= model_provider.get_model(params,rng)
u.log_write("Number of parameters: %s"%(model.n_param),params)
train_errors = np.ndarray(nb_epochs)
u.log_write("Training started",params)
val_counter=0
best_loss=1000
for epoch_counter in range(nb_epochs):
batch_loss = 0.
# H=C=np.zeros(shape=(batch_size,params['n_hidden']), dtype=dtype) # initial hidden state
sid=0
for minibatch_index in range(n_train_batches):
x_lst=F_list_train[minibatch_index * batch_size: (minibatch_index + 1) * batch_size] #60*20*1024
y_lst=G_list_train[minibatch_index * batch_size: (minibatch_index + 1) * batch_size] #60*20*1024
x,y=du.load_batch(params,x_lst,y_lst)
# x=X_train[id_lst] #60*20*1024
# y=Y_train[id_lst]#60*20*54
is_train=1
if(params["model"]=="blstmnp"):
x_b=np.asarray(map(np.flipud,x))
loss = model.train(x,x_b,y)
else:
loss= model.train(x, y,is_train)
batch_loss += loss
if params['shufle_data']==1:
X_train,Y_train=du.shuffle_in_unison_inplace(X_train,Y_train)
train_errors[epoch_counter] = batch_loss
batch_loss/=n_train_batches
s='TRAIN--> epoch %i | error %f'%(epoch_counter, batch_loss)
u.log_write(s,params)
if(epoch_counter%3==0):
print("Model testing")
batch_loss3d = []
H=C=np.zeros(shape=(batch_size,params['n_hidden']), dtype=dtype) # resetting initial state, since seq change
sid=0
for minibatch_index in range(n_test_batches):
x_lst=F_list_test[minibatch_index * batch_size: (minibatch_index + 1) * batch_size] #60*20*1024
y_lst=G_list_test[minibatch_index * batch_size: (minibatch_index + 1) * batch_size] #60*20*1024
x,y=du.load_batch(params,x_lst,y_lst)
# tmp_sid=S_Test_list[(minibatch_index + 1) * batch_size-1]
# if(sid==0):
# sid=tmp_sid
# if(tmp_sid!=sid):
# sid=tmp_sid
# H=C=np.zeros(shape=(batch_size,params['n_hidden']), dtype=dtype) # resetting initial state, since seq change
# x=X_test[id_lst] #60*20*1024
# y=Y_test[id_lst]#60*20*54
is_train=0
if(params["model"]=="blstmnp"):
x_b=np.asarray(map(np.flipud,x))
pred = model.predictions(x,x_b)
else:
pred= model.predictions(x,is_train)
loss3d =u.get_loss(params,y,pred)
batch_loss3d.append(loss3d)
batch_loss3d=np.nanmean(batch_loss3d)
if(batch_loss3d<best_loss):
best_loss=batch_loss3d
ext=str(epoch_counter)+"_"+str(batch_loss3d)+"_best.p"
u.write_params(model.params,params,ext)
else:
ext=str(val_counter%2)+".p"
u.write_params(model.params,params,ext)
val_counter+=1#0.08
s ='VAL--> epoch %i | error %f, %f'%(val_counter,batch_loss3d,n_test_batches)
u.log_write(s,params)
print("Model loading started")
model = model_provider.get_model_pretrained(params, rng)
print("Prediction started")
batch_loss = 0.
batch_loss3d = 0.
batch_bb_loss = 0.
loss_list = []
last_index = 0
first_index = 0
sq_loss_lst = []
for minibatch_index in range(n_test_batches):
x_lst = F_list_test[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size] #60*20*1024
y_lst = G_list_test[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size] #60*20*1024
x, y = du.load_batch(params, x_lst, y_lst)
last_index = first_index + sum([len(i) for i in y])
first_index = last_index
if (params["model"] == "blstmnp"):
x_b = np.asarray(map(np.flipud, x))
pred = model.predictions(x, x_b, is_train)
else:
pred = model.predictions(x, is_train)
# print("Prediction done....")
if residual > 0:
if (minibatch_index == n_test_batches - 1):
pred = pred[0:(len(pred) - residual)]
y = y[0:(len(y) - residual)]
# n_list=n_list[0:(len(n_list)-residual)]
