def train(model, samples, label):
num_layers = 9
num_samples = samples.shape[-1]
fc_shape = [512, num_samples]
acts = [None] * num_layers
sens = [None] * num_layers
weightgrad = [None] * len(model.weights)
biasgrad = [None] * len(model.bias)
acts[0] = samples
acts[1] = ele.relu(model.convs[0].ff(acts[0], model.weights[0], model.bias[0]))
acts[2] = model.poolings[0].ff(acts[1])
acts[3] = ele.relu(model.convs[1].ff(acts[2], model.weights[1], model.bias[1]))
acts[4] = model.poolings[1].ff(acts[3])
acts[5] = model.weights[2] * acts[4].reshape(fc_shape) + model.bias[2]
out = conv.softmax(acts[5], conv.soft_op.instance)
sens[5] = out - label
sens[4] = (model.weights[2].trans() * sens[5]).reshape(acts[4].shape)
sens[3] = ele.relu_back(model.poolings[1].bp(sens[4], acts[4], acts[3]), acts[3])
sens[2] = model.convs[1].bp(sens[3], model.weights[1])
sens[1] = ele.relu_back(model.poolings[0].bp(sens[2], acts[2], acts[1]), acts[1])
weightgrad[2] = sens[5] * acts[4].reshape(fc_shape).trans()
biasgrad[2] = sens[5].sum(1)
weightgrad[1] = model.convs[1].weight_grad(sens[3], acts[2])
biasgrad[1] = model.convs[1].bias_grad(sens[3])
weightgrad[0] = model.convs[0].weight_grad(sens[1], acts[0])
biasgrad[0] = model.convs[0].bias_grad(sens[1])
return (out, weightgrad, biasgrad)
def bpprop(model, samples, label):
num_layers = 6
num_samples = samples.shape[-1]
fc_shape = [512, num_samples]
acts = [None] * num_layers
errs = [None] * num_layers
weightgrad = [None] * len(model.weights)
biasgrad = [None] * len(model.bias)
acts[0] = samples
acts[1] = ele.relu(model.convs[0].ff(acts[0], model.weights[0], model.bias[0]))
acts[2] = model.poolings[0].ff(acts[1])
acts[3] = ele.relu(model.convs[1].ff(acts[2], model.weights[1], model.bias[1]))
acts[4] = model.poolings[1].ff(acts[3])
acts[5] = model.weights[2] * acts[4].reshape(fc_shape) + model.bias[2]
out = conv.softmax(acts[5], conv.soft_op.instance)
errs[5] = out - label
errs[4] = (model.weights[2].trans() * errs[5]).reshape(acts[4].shape)
errs[3] = ele.relu_back(model.poolings[1].bp(errs[4], acts[4], acts[3]), acts[3])
errs[2] = model.convs[1].bp(errs[3], acts[2], model.weights[1])
errs[1] = ele.relu_back(model.poolings[0].bp(errs[2], acts[2], acts[1]), acts[1])
weightgrad[2] = errs[5] * acts[4].reshape(fc_shape).trans()
biasgrad[2] = errs[5].sum(1)
weightgrad[1] = model.convs[1].weight_grad(errs[3], acts[2], model.weights[1])
biasgrad[1] = model.convs[1].bias_grad(errs[3])
weightgrad[0] = model.convs[0].weight_grad(errs[1], acts[0], model.weights[0])
biasgrad[0] = model.convs[0].bias_grad(errs[1])
return (out, weightgrad, biasgrad)
def forward(self, from_btm, to_top, phase):
to_top[self.top_names[0]] = co.softmax(from_btm[self.btm_names[0]], co.soft_op.instance)
self.ff_y = to_top[self.top_names[0]]
#turn label into matrix form
nplabel = np.zeros([self.ff_y.shape[1], self.ff_y.shape[0]], dtype=np.float32)
self.strlabel = from_btm[self.btm_names[1]]
for i in range(len(self.strlabel)):
nplabel[i, self.strlabel[i]] = 1
self.y = owl.from_numpy(nplabel)
def run(self):
(train_data,
test_data) = mnist_io.load_mb_from_mat(self.data_file, self.mb_size)
np.set_printoptions(linewidth=200)
num_test_samples = test_data[0].shape[0]
(test_samples,
test_labels) = map(lambda npdata: owl.from_numpy(npdata), test_data)
count = 1
owl.set_device(self.gpu)
for epoch in range(self.num_epochs):
print '---Start epoch #%d' % epoch
# train
for (mb_samples, mb_labels) in train_data:
num_samples = mb_samples.shape[0]
a1 = owl.from_numpy(mb_samples)
target = owl.from_numpy(mb_labels)
# ff
a2 = ele.relu(self.w1 * a1 + self.b1)
a3 = self.w2 * a2 + self.b2
# softmax & error
out = co.softmax(a3)
s3 = out - target
# bp
s2 = self.w2.trans() * s3
s2 = ele.relu_back(s2, a2)
# grad
gw1 = s2 * a1.trans() / num_samples
gb1 = s2.sum(1) / num_samples
gw2 = s3 * a2.trans() / num_samples
gb2 = s3.sum(1) / num_samples
# update
self.w1 -= self.eps_w * gw1
self.w2 -= self.eps_w * gw2
self.b1 -= self.eps_b * gb1
self.b2 -= self.eps_b * gb2
if (count % 40 == 0):
correct = out.argmax(0) - target.argmax(0)
val = correct.to_numpy()
print 'Training error:', float(
np.count_nonzero(val)) / num_samples
count = count + 1
# test
a1 = test_samples
a2 = ele.relu(self.w1 * a1 + self.b1)
a3 = self.w2 * a2 + self.b2
correct = a3.argmax(0) - test_labels.argmax(0)
val = correct.to_numpy()
#print val
print 'Testing error:', float(
np.count_nonzero(val)) / num_test_samples
print '---Finish epoch #%d' % epoch
def test(self):
base = np.asarray([40.0,20.0,30.0,10.0])
max = np.max(base)
base = np.reshape(base, [1,1,1,4])
owlarray = owl.from_numpy(base)
expected = np.exp(base - max)
expected = expected / np.sum(expected)
test = conv.softmax(owlarray)
#print 'Expected\n',expected
#print "Actual\n",test.to_numpy()
self.assertTrue(np.allclose(expected, test.to_numpy()))
def forward(self, from_btm, to_top, phase):
to_top[self.top_names[0]] = co.softmax(from_btm[self.btm_names[0]],
co.soft_op.instance)
self.ff_y = to_top[self.top_names[0]]
#turn label into matrix form
nplabel = np.zeros([self.ff_y.shape[1], self.ff_y.shape[0]],
dtype=np.float32)
self.strlabel = from_btm[self.btm_names[1]]
for i in range(len(self.strlabel)):
nplabel[i, self.strlabel[i]] = 1
self.y = owl.from_numpy(nplabel)
def run(self):
(train_data, test_data) = mnist_io.load_mb_from_mat(self.data_file, self.mb_size)
np.set_printoptions(linewidth=200)
num_test_samples = test_data[0].shape[0]
(test_samples, test_labels) = map(lambda npdata : owl.from_numpy(npdata), test_data)
count = 1
owl.set_device(self.gpu)
for epoch in range(self.num_epochs):
print '---Start epoch #%d' % epoch
# train
for (mb_samples, mb_labels) in train_data:
num_samples = mb_samples.shape[0]
a1 = owl.from_numpy(mb_samples)
target = owl.from_numpy(mb_labels)
# ff
a2 = ele.relu(self.w1 * a1 + self.b1)
a3 = self.w2 * a2 + self.b2
# softmax & error
out = co.softmax(a3)
s3 = out - target
# bp
s2 = self.w2.trans() * s3
s2 = ele.relu_back(s2, a2)
# grad
gw1 = s2 * a1.trans() / num_samples
gb1 = s2.sum(1) / num_samples
gw2 = s3 * a2.trans() / num_samples
gb2 = s3.sum(1) / num_samples
# update
self.w1 -= self.eps_w * gw1
self.w2 -= self.eps_w * gw2
self.b1 -= self.eps_b * gb1
self.b2 -= self.eps_b * gb2
if (count % 40 == 0):
correct = out.max_index(0) - target.max_index(0)
val = correct.to_numpy()
print 'Training error:', float(np.count_nonzero(val)) / num_samples
count = count + 1
# test
a1 = test_samples
a2 = ele.relu(self.w1 * a1 + self.b1)
a3 = self.w2 * a2 + self.b2
correct = a3.max_index(0) - test_labels.max_index(0)
val = correct.to_numpy()
#print val
print 'Testing error:', float(np.count_nonzero(val)) / num_test_samples
print '---Finish epoch #%d' % epoch
def bpprop(model, samples, label):
num_layers = model.layers
num_samples = samples.shape[-1]
fc_shape = [model.convolution_output_size, num_samples]
acts = [None] * num_layers
errs = [None] * num_layers
weightgrad = [None] * len(model.weights)
biasgrad = [None] * len(model.bias)
acts[0] = samples
acts[1] = ele.relu(model.convs[0].ff(acts[0], model.weights[0], model.bias[0]))
acts[2] = model.poolings[0].ff(acts[1])
acts[3] = ele.relu(model.convs[1].ff(acts[2], model.weights[1], model.bias[1]))
acts[4] = model.poolings[1].ff(acts[3])
acts[5] = model.weights[2] * acts[4].reshape(fc_shape) + model.bias[2]
acts[6] = model.weights[3] * acts[5] + model.bias[3]
out = conv.softmax(acts[6], conv.soft_op.instance)
errs[6] = out - label
errs[5] = (model.weights[3].trans() * errs[6]).reshape(acts[5].shape)
errs[4] = (model.weights[2].trans() * errs[5]).reshape(acts[4].shape)
errs[3] = ele.relu_back(model.poolings[1].bp(errs[4], acts[4], acts[3]), acts[3])
errs[2] = model.convs[1].bp(errs[3], acts[2], model.weights[1])
errs[1] = ele.relu_back(model.poolings[0].bp(errs[2], acts[2], acts[1]), acts[1])
weightgrad[3] = errs[6] * acts[5].trans()
biasgrad[3] = errs[6].sum(1)
weightgrad[2] = errs[5] * acts[4].reshape(fc_shape).trans()
biasgrad[2] = errs[5].sum(1)
weightgrad[1] = model.convs[1].weight_grad(errs[3], acts[2], model.weights[1])
biasgrad[1] = model.convs[1].bias_grad(errs[3])
weightgrad[0] = model.convs[0].weight_grad(errs[1], acts[0], model.weights[0])
biasgrad[0] = model.convs[0].bias_grad(errs[1])
return (out, weightgrad, biasgrad)
def ff(self, x):
self.ff_y = co.softmax(x, co.soft_op.instance)
return self.ff_y
def train_one_mb(self, data, label, dropout_rate):
num_samples = data.shape[-1]
num_layers = 12
acts = [None] * num_layers
sens = [None] * num_layers
weightsgrad = [None] * self.num_weights
biasgrad = [None] * self.num_weights
# FF
acts[0] = data
acts[1] = ele.relu(self.convs[0].ff(acts[0], self.weights[0],
self.bias[0])) # conv1
acts[2] = self.poolings[0].ff(acts[1]) # pool1
acts[3] = ele.relu(self.convs[1].ff(acts[2], self.weights[1],
self.bias[1])) # conv2
acts[4] = self.poolings[1].ff(acts[3]) # pool2
acts[5] = ele.relu(self.convs[2].ff(acts[4], self.weights[2],
self.bias[2])) # conv3
acts[6] = ele.relu(self.convs[3].ff(acts[5], self.weights[3],
self.bias[3])) # conv4
acts[7] = ele.relu(self.convs[4].ff(acts[6], self.weights[4],
self.bias[4])) # conv5
acts[8] = self.poolings[2].ff(acts[7]) # pool5
re_acts8 = acts[8].reshape([np.prod(acts[8].shape[0:3]), num_samples])
acts[9] = ele.relu(self.weights[5] * re_acts8 + self.bias[5]) # fc6
mask6 = owl.randb(acts[9].shape, dropout_rate)
acts[9] = ele.mult(acts[9], mask6) # drop6
acts[10] = ele.relu(self.weights[6] * acts[9] + self.bias[6]) # fc7
mask7 = owl.randb(acts[10].shape, dropout_rate)
acts[10] = ele.mult(acts[10], mask7) # drop7
acts[11] = self.weights[7] * acts[10] + self.bias[7] # fc8
out = co.softmax(acts[11], co.soft_op.instance) # prob
sens[11] = out - label
sens[10] = self.weights[7].trans() * sens[11] # fc8
sens[10] = ele.mult(sens[10], mask7) # drop7
sens[10] = ele.relu_back(sens[10], acts[10]) # relu7
sens[9] = self.weights[6].trans() * sens[10]
sens[9] = ele.mult(sens[9], mask6) # drop6
sens[9] = ele.relu_back(sens[9], acts[9]) # relu6
sens[8] = (self.weights[5].trans() * sens[9]).reshape(
acts[8].shape) # fc6
sens[7] = ele.relu_back(self.poolings[2].bp(sens[8], acts[8], acts[7]),
acts[7]) # pool5, relu5
sens[6] = ele.relu_back(self.convs[4].bp(sens[7], self.weights[4]),
acts[6]) # conv5, relu4
sens[5] = ele.relu_back(self.convs[3].bp(sens[6], self.weights[3]),
acts[5]) # conv4, relu3
sens[4] = self.convs[2].bp(sens[5], self.weights[2]) # conv3
sens[3] = ele.relu_back(self.poolings[1].bp(sens[4], acts[4], acts[3]),
acts[3]) # pool2, relu2
sens[2] = self.convs[1].bp(sens[3], self.weights[1]) # conv2
sens[1] = self.poolings[0].bp(sens[2], acts[2], acts[1]) # pool1
sens[1] = ele.relu_back(sens[1], acts[1]) # relu1
weightsgrad[7] = sens[11] * acts[10].trans()
weightsgrad[6] = sens[10] * acts[9].trans()
weightsgrad[5] = sens[9] * re_acts8.trans()
weightsgrad[4] = self.convs[4].weight_grad(sens[7], acts[6])
weightsgrad[3] = self.convs[3].weight_grad(sens[6], acts[5])
weightsgrad[2] = self.convs[2].weight_grad(sens[5], acts[4])
weightsgrad[1] = self.convs[1].weight_grad(sens[3], acts[2])
weightsgrad[0] = self.convs[0].weight_grad(sens[1], acts[0])
biasgrad[7] = sens[11].sum(1)
biasgrad[6] = sens[10].sum(1)
biasgrad[5] = sens[9].sum(1)
biasgrad[4] = self.convs[4].bias_grad(sens[7])
biasgrad[3] = self.convs[3].bias_grad(sens[6])
biasgrad[2] = self.convs[2].bias_grad(sens[5])
biasgrad[1] = self.convs[1].bias_grad(sens[3])
biasgrad[0] = self.convs[0].bias_grad(sens[1])
return (out, weightsgrad, biasgrad)
def train_one_mb(self, data, label, dropout_rate):
num_samples = data.shape[-1]
num_layers = 12
acts = [None] * num_layers
sens = [None] * num_layers
weightsgrad = [None] * self.num_weights
biasgrad = [None] * self.num_weights
# FF
acts[0] = data
acts[1] = ele.relu(self.convs[0].ff(acts[0], self.weights[0], self.bias[0])) # conv1
acts[2] = self.poolings[0].ff(acts[1]) # pool1
acts[3] = ele.relu(self.convs[1].ff(acts[2], self.weights[1], self.bias[1])) # conv2
acts[4] = self.poolings[1].ff(acts[3]) # pool2
acts[5] = ele.relu(self.convs[2].ff(acts[4], self.weights[2], self.bias[2])) # conv3
acts[6] = ele.relu(self.convs[3].ff(acts[5], self.weights[3], self.bias[3])) # conv4
acts[7] = ele.relu(self.convs[4].ff(acts[6], self.weights[4], self.bias[4])) # conv5
acts[8] = self.poolings[2].ff(acts[7]) # pool5
re_acts8 = acts[8].reshape([np.prod(acts[8].shape[0:3]), num_samples])
acts[9] = ele.relu(self.weights[5] * re_acts8 + self.bias[5]) # fc6
mask6 = owl.randb(acts[9].shape, dropout_rate)
acts[9] = ele.mult(acts[9], mask6) # drop6
acts[10] = ele.relu(self.weights[6] * acts[9] + self.bias[6]) # fc7
mask7 = owl.randb(acts[10].shape, dropout_rate)
acts[10] = ele.mult(acts[10], mask7) # drop7
acts[11] = self.weights[7] * acts[10] + self.bias[7] # fc8
out = co.softmax(acts[11], co.soft_op.instance) # prob
sens[11] = out - label
sens[10] = self.weights[7].trans() * sens[11] # fc8
sens[10] = ele.mult(sens[10], mask7) # drop7
sens[10] = ele.relu_back(sens[10], acts[10]) # relu7
sens[9] = self.weights[6].trans() * sens[10]
sens[9] = ele.mult(sens[9], mask6) # drop6
sens[9] = ele.relu_back(sens[9], acts[9]) # relu6
sens[8] = (self.weights[5].trans() * sens[9]).reshape(acts[8].shape) # fc6
sens[7] = ele.relu_back(self.poolings[2].bp(sens[8], acts[8], acts[7]), acts[7]) # pool5, relu5
sens[6] = ele.relu_back(self.convs[4].bp(sens[7], acts[6], self.weights[4]), acts[6]) # conv5, relu4
sens[5] = ele.relu_back(self.convs[3].bp(sens[6], acts[5], self.weights[3]), acts[5]) # conv4, relu3
sens[4] = self.convs[2].bp(sens[5], acts[4], self.weights[2]) # conv3
sens[3] = ele.relu_back(self.poolings[1].bp(sens[4], acts[4], acts[3]), acts[3]) # pool2, relu2
sens[2] = self.convs[1].bp(sens[3], acts[2], self.weights[1]) # conv2
sens[1] = self.poolings[0].bp(sens[2], acts[2], acts[1]) # pool1
sens[1] = ele.relu_back(sens[1], acts[1]) # relu1
weightsgrad[7] = sens[11] * acts[10].trans()
weightsgrad[6] = sens[10] * acts[9].trans()
weightsgrad[5] = sens[9] * re_acts8.trans()
weightsgrad[4] = self.convs[4].weight_grad(sens[7], acts[6], self.weights[4])
weightsgrad[3] = self.convs[3].weight_grad(sens[6], acts[5], self.weights[3])
weightsgrad[2] = self.convs[2].weight_grad(sens[5], acts[4], self.weights[2])
weightsgrad[1] = self.convs[1].weight_grad(sens[3], acts[2], self.weights[1])
weightsgrad[0] = self.convs[0].weight_grad(sens[1], acts[0], self.weights[0])
biasgrad[7] = sens[11].sum(1)
biasgrad[6] = sens[10].sum(1)
biasgrad[5] = sens[9].sum(1)
biasgrad[4] = self.convs[4].bias_grad(sens[7])
biasgrad[3] = self.convs[3].bias_grad(sens[6])
biasgrad[2] = self.convs[2].bias_grad(sens[5])
biasgrad[1] = self.convs[1].bias_grad(sens[3])
biasgrad[0] = self.convs[0].bias_grad(sens[1])
return (out, weightsgrad, biasgrad)
def forward(self, from_btm, to_top, phase):
to_top[self.top_names[0]] = co.softmax(from_btm[self.btm_names[0]],
co.soft_op.instance)
self.ff_y = to_top[self.top_names[0]]
self.y = from_btm[self.btm_names[1]]
