def step1(x, h, mem):
x1 = x[:, 0:10] # seq_lenx10
x2 = x[:, 10:20]
x3 = x[:, 20:30]
x4 = x[:, 30:40]
h = T.dot(h.dimshuffle('x', 0), W)
h1 = h[:, 0:10] # 1x10
h2 = h[:, 10:20]
h3 = h[:, 20:30]
h4 = h[:, 30:40]
test = (T.dot(x1, T.transpose(h1)) +
T.dot(x2, T.transpose(h2)) +
T.dot(x3, T.transpose(h3)) +
T.dot(x4, T.transpose(h4)))
return test + mem
def step2(x1, x2, x3, x4, h, mem):
h = h.dimshuffle('x', 0) # 1x20
h1 = T.dot(h, W1) # 1x10
h2 = T.dot(h, W2)
h3 = T.dot(h, W3)
h4 = T.dot(h, W4)
test = (T.dot(x1, T.transpose(h1)) +
T.dot(x2, T.transpose(h2)) +
T.dot(x3, T.transpose(h3)) +
T.dot(x4, T.transpose(h4)))
return test + mem
def step3(x, h, mem):
x1 = x[:, 0:10] # seq_lenx10
x2 = x[:, 10:20]
x3 = x[:, 20:30]
x4 = x[:, 30:40]
h = h.dimshuffle('x', 0) # 1x20
h1 = T.dot(h, W1) # 1x10
h2 = T.dot(h, W2)
h3 = T.dot(h, W3)
h4 = T.dot(h, W4)
test = (T.dot(x1, T.transpose(h1)) +
T.dot(x2, T.transpose(h2)) +
T.dot(x3, T.transpose(h3)) +
T.dot(x4, T.transpose(h4)))
return test + mem
import numpy as np
import time
batch_size = 128
seq_len = 512
X = T.variable(np.random.rand(batch_size, seq_len, 20))
W1 = T.variable(np.random.rand(20, 10))
W2 = T.variable(np.random.rand(20, 10))
W3 = T.variable(np.random.rand(20, 10))
W4 = T.variable(np.random.rand(20, 10))
hidden = T.variable(np.random.rand(batch_size, 20))
# ====== First approach ====== #
W = T.concatenate((W1, W2, W3, W4), axis=1) # 20x40
inputs = T.dot(X, W) #batch_sizexseq_lenx40
inputs1 = T.dot(X, W1)
inputs2 = T.dot(X, W2)
inputs3 = T.dot(X, W3)
inputs4 = T.dot(X, W4)
def step1(x, h, mem):
x1 = x[:, 0:10] # seq_lenx10
x2 = x[:, 10:20]
x3 = x[:, 20:30]
x4 = x[:, 30:40]
h = T.dot(h.dimshuffle('x', 0), W)
h1 = h[:, 0:10] # 1x10
