continuous = False
train_idx, valid_idx, test_idx, other_data = data.apnews()
[docs, dic, w2i, i2w] = other_data
dim_x = len(dic)
dim_y = dim_x
print "#features = ", dim_x, "#labels = ", dim_y
print "compiling..."
model = VAE(dim_x, dim_x, hidden_size, latent_size, continuous, optimizer)
print "training..."
start = time.time()
for i in xrange(100):
train_xy = data.batched_idx(train_idx, batch_size)
error = 0.0
in_start = time.time()
for batch_id, x_idx in train_xy.items():
X = data.batched_news(x_idx, other_data)
cost, z = model.train(X, lr)
error += cost
#print i, batch_id, "/", len(train_xy), cost
in_time = time.time() - in_start
error /= len(train_xy)
print "Iter = " + str(i) + ", Loss = " + str(error) + ", Time = " + str(
in_time)
print "training finished. Time = " + str(time.time() - start)
continuous = False
train_idx, valid_idx, test_idx, other_data = data.apnews()
[docs, dic, w2i, i2w, targets] = other_data
dim_x = len(dic)
dim_y = dim_x
print("#features = ", dim_x, "#labels = ", dim_y)
print("compiling...")
model = VAE(dim_x, dim_x, hidden_size, latent_size, continuous, optimizer)
print("training...")
start = time.time()
for i in range(20):
train_xy = data.batched_idx(train_idx, batch_size)
error = 0.0
in_start = time.time()
for batch_id, x_idx in train_xy.items():
X = data.batched_news(x_idx, other_data)
cost, z = model.train(X, lr)
error += cost
#print i, batch_id, "/", len(train_xy), cost
in_time = time.time() - in_start
error /= len(train_xy);
print("Iter = " + str(i) + ", Loss = " + str(error) + ", Time = " + str(in_time))
print("training finished. Time = " + str(time.time() - start))
print("save model...")
optimizer = "rmsprop"
train_idx, valid_idx, test_idx, other_data = data.apnews()
[docs, dic, w2i, i2w] = other_data
dim_x = len(dic)
dim_y = dim_x
print "#features = ", dim_x, "#labels = ", dim_y
print "compiling..."
model = GAN(dim_x, hidden_size, latent_size, optimizer)
print "training..."
start = time.time()
for i in xrange(100):
train_xy = data.batched_idx(train_idx, batch_size)
error_d = 0.0
error_g = 0.0
in_start = time.time()
for batch_id, x_idx in train_xy.items():
local_bath_size = len(x_idx)
X = data.batched_news(x_idx, other_data)
Z = model.noiser(local_bath_size)
loss_d = 0
for di in xrange(iter_d):
loss_d += model.train_d(X, Z, lr)
loss_d = loss_d / iter_d
loss_g = model.train_g(X, Z, lr)
error_d += loss_d