def ff(self, x, phase):
self.dropmask = owl.randb(x.shape, self.keep_ratio)
if phase == "TRAIN":
return ele.mult(x, self.dropmask)*self.scale
else:
return x
'''
def ff(self, x, phase):
self.dropmask = owl.randb(x.shape, self.keep_ratio)
if phase == "TRAIN":
return ele.mult(x, self.dropmask) * self.scale
else:
#return x * (1 - self.params.dropout_param.dropout_ratio)
return x
'''
def ff(self, x, phase):
''' Foward function of dropout
The dropout mask will not be multiplied if under ``"TEST"`` mode.
'''
if phase == "TRAIN":
self.dropmask = owl.randb(x.shape, self.keep_ratio)
return ele.mult(x, self.dropmask) * self.scale
else:
return x
def ff(self, x, phase):
''' Foward function of dropout
The dropout mask will not be multiplied if under ``"TEST"`` mode.
'''
self.dropmask = owl.randb(x.shape, self.keep_ratio)
if phase == "TRAIN":
return ele.mult(x, self.dropmask)*self.scale
else:
return x
def train_network(model,
num_epochs=100,
minibatch_size=256,
dropout_rate=0.5,
eps_w=0.01,
eps_b=0.01,
mom=0.9,
wd=0.0005):
gpu = owl.create_gpu_device(1)
owl.set_device(gpu)
num_layers = 20
count = 0
last = time.time()
dp = ImageNetDataProvider(
mean_file='/home/minjie/data/imagenet/imagenet_mean.binaryproto',
train_db='/home/minjie/data/imagenet/ilsvrc12_train_lmdb',
val_db='/home/minjie/data/imagenet/ilsvrc12_val_lmdb',
test_db='/home/minjie/data/imagenet/ilsvrc12_test_lmdb')
acts = [None] * num_layers
sens = [None] * num_layers
for i in xrange(num_epochs):
print "---------------------Epoch #", i
sys.stdout.flush()
for (samples, labels) in dp.get_train_mb(minibatch_size):
num_samples = samples.shape[0]
acts = [None] * num_layers
sens = [None] * num_layers
# FF
acts[0] = owl.from_nparray(samples).reshape(
[227, 227, 3, num_samples])
target = owl.from_nparray(labels)
acts1 = conv_forward(acts[0], model.weights[0], model.bias[0],
model.conv_infos[0])
acts[1] = ele.relu(
acts1
) #(conv_forward(acts[0], model.weights[0], model.bias[0], model.conv_infos[0])) # conv1
acts[2] = pooling_forward(acts[1], model.pooling_infos[0]) # pool1
acts3 = conv_forward(acts[2], model.weights[1], model.bias[1],
model.conv_infos[1]) # conv2
acts[3] = ele.relu(
acts3
) #(conv_forward(acts[2], model.weights[1], model.bias[1], model.conv_infos[1])) # conv2
acts[4] = pooling_forward(acts[3], model.pooling_infos[1]) # pool2
acts5 = conv_forward(acts[4], model.weights[2], model.bias[2],
model.conv_infos[2]) # conv3
acts[5] = ele.relu(
acts5
) #(conv_forward(acts[4], model.weights[2], model.bias[2], model.conv_infos[2])) # conv3
acts6 = conv_forward(acts[5], model.weights[3], model.bias[3],
model.conv_infos[3]) # conv4
acts[6] = ele.relu(
acts6
) #(conv_forward(acts[5], model.weights[3], model.bias[3], model.conv_infos[3])) # conv4
acts7 = conv_forward(acts[6], model.weights[4], model.bias[4],
model.conv_infos[4]) # conv5
acts[7] = ele.relu(
acts7
) #(conv_forward(acts[6], model.weights[4], model.bias[4], model.conv_infos[4])) # conv5
acts[8] = pooling_forward(acts[7], model.pooling_infos[2]) # pool5
re_acts8 = acts[8].reshape(
[np.prod(acts[8].shape[0:3]), num_samples])
acts9 = model.weights[5] * re_acts8 + model.bias[5] # fc6
acts[9] = ele.relu(
acts9) #(model.weights[5] * re_acts8 + model.bias[5]) # fc6
mask6 = owl.randb(acts[9].shape, dropout_rate)
acts[9] = ele.mult(acts[9], mask6) # drop6
acts10 = model.weights[6] * acts[9] + model.bias[6] # fc7
acts[10] = ele.relu(
acts10) #(model.weights[6] * acts[9] + model.bias[6]) # fc7
mask7 = owl.randb(acts[10].shape, dropout_rate)
acts[10] = ele.mult(acts[10], mask7) # drop7
acts[11] = model.weights[7] * acts[10] + model.bias[7] # fc8
acts[12] = softmax_forward(
acts[11].reshape([1000, 1, 1, num_samples]),
soft_op.instance).reshape([1000, num_samples]) # prob
# error
sens[11] = acts[12] - target
# BP
sens[10] = model.weights[7].trans() * sens[11] # fc8
sens[10] = ele.mult(sens[10], mask7) # drop7
sens[10] = ele.relu_back(sens[10], acts[10], acts10) # relu7
sens[9] = model.weights[6].trans() * sens[10]
sens[9] = ele.mult(sens[9], mask6) # drop6
sens[9] = ele.relu_back(sens[9], acts[9], acts9) # relu6
sens[8] = (model.weights[5].trans() * sens[9]).reshape(
acts[8].shape) # fc6
sens[7] = pooling_backward(sens[8], acts[8], acts[7],
model.pooling_infos[2]) # pool5
sens[7] = ele.relu_back(sens[7], acts[7], acts7) # relu5
sens[6] = conv_backward_data(sens[7], model.weights[4],
model.conv_infos[4]) # conv5
sens[6] = ele.relu_back(sens[6], acts[6], acts6) # relu4
sens[5] = conv_backward_data(sens[6], model.weights[3],
model.conv_infos[3]) # conv4
sens[5] = ele.relu_back(sens[5], acts[5], acts5) # relu3
sens[4] = conv_backward_data(sens[5], model.weights[2],
model.conv_infos[2]) # conv3
sens[3] = pooling_backward(sens[4], acts[4], acts[3],
model.pooling_infos[1]) # pool2
sens[3] = ele.relu_back(sens[3], acts[3], acts3) # relu2
sens[2] = conv_backward_data(sens[3], model.weights[1],
model.conv_infos[1]) # conv2
sens[1] = pooling_backward(sens[2], acts[2], acts[1],
model.pooling_infos[0]) # pool1
sens[1] = ele.relu_back(sens[1], acts[1], acts1) # relu1
model.weightsdelta[
7] = mom * model.weightsdelta[7] - eps_w / num_samples * (
sens[11] * acts[10].trans() + wd * model.weights[7])
model.biasdelta[7] = mom * model.biasdelta[
7] - eps_b / num_samples * sens[11].sum(1)
model.weightsdelta[
6] = mom * model.weightsdelta[6] - eps_w / num_samples * (
sens[10] * acts[9].trans() + wd * model.weights[6])
model.biasdelta[6] = mom * model.biasdelta[
6] - eps_b / num_samples * sens[10].sum(1)
model.weightsdelta[
5] = mom * model.weightsdelta[5] - eps_w / num_samples * (
sens[9] * re_acts8.trans() + wd * model.weights[5])
model.biasdelta[5] = mom * model.biasdelta[
5] - eps_b / num_samples * sens[9].sum(1)
model.weightsdelta[
4] = mom * model.weightsdelta[4] - eps_w / num_samples * (
conv_backward_filter(sens[7], acts[6], model.conv_infos[4])
+ wd * model.weights[4])
model.biasdelta[4] = mom * model.biasdelta[
4] - eps_b / num_samples * conv_backward_bias(sens[7])
model.weightsdelta[
3] = mom * model.weightsdelta[3] - eps_w / num_samples * (
conv_backward_filter(sens[6], acts[5], model.conv_infos[3])
+ wd * model.weights[3])
model.biasdelta[3] = mom * model.biasdelta[
3] - eps_b / num_samples * conv_backward_bias(sens[6])
model.weightsdelta[
2] = mom * model.weightsdelta[2] - eps_w / num_samples * (
conv_backward_filter(sens[5], acts[4], model.conv_infos[2])
+ wd * model.weights[2])
model.biasdelta[2] = mom * model.biasdelta[
2] - eps_b / num_samples * conv_backward_bias(sens[5])
model.weightsdelta[
1] = mom * model.weightsdelta[1] - eps_w / num_samples * (
conv_backward_filter(sens[3], acts[2], model.conv_infos[1])
+ wd * model.weights[1])
model.biasdelta[1] = mom * model.biasdelta[
1] - eps_b / num_samples * conv_backward_bias(sens[3])
model.weightsdelta[
0] = mom * model.weightsdelta[0] - eps_w / num_samples * (
conv_backward_filter(sens[1], acts[0], model.conv_infos[0])
+ wd * model.weights[0])
model.biasdelta[0] = mom * model.biasdelta[
0] - eps_b / num_samples * conv_backward_bias(sens[1])
for k in range(8):
model.weights[k] += model.weightsdelta[k]
model.bias[k] += model.biasdelta[k]
count = count + 1
if count % 10 == 0:
print_training_accuracy(acts[12], target, num_samples)
print "time: %s" % (time.time() - last)
last = time.time()
def train_one_mb(model, data, label, weightsgrad, biasgrad, dropout_rate):
num_samples = data.shape[-1]
num_layers = 20
acts = [None] * num_layers
sens = [None] * num_layers
# FF
acts[0] = data
acts1 = conv_forward(acts[0], model.weights[0], model.bias[0], model.conv_infos[0])
acts[1] = ele.relu(acts1)#(conv_forward(acts[0], model.weights[0], model.bias[0], model.conv_infos[0])) # conv1
acts[2] = pooling_forward(acts[1], model.pooling_infos[0]) # pool1
acts3 = conv_forward(acts[2], model.weights[1], model.bias[1], model.conv_infos[1]) # conv2
acts[3] = ele.relu(acts3)#(conv_forward(acts[2], model.weights[1], model.bias[1], model.conv_infos[1])) # conv2
acts[4] = pooling_forward(acts[3], model.pooling_infos[1]) # pool2
acts5 = conv_forward(acts[4], model.weights[2], model.bias[2], model.conv_infos[2]) # conv3
acts[5] = ele.relu(acts5)#(conv_forward(acts[4], model.weights[2], model.bias[2], model.conv_infos[2])) # conv3
acts6 = conv_forward(acts[5], model.weights[3], model.bias[3], model.conv_infos[3]) # conv4
acts[6] = ele.relu(acts6)#(conv_forward(acts[5], model.weights[3], model.bias[3], model.conv_infos[3])) # conv4
acts7 = conv_forward(acts[6], model.weights[4], model.bias[4], model.conv_infos[4]) # conv5
acts[7] = ele.relu(acts7)#(conv_forward(acts[6], model.weights[4], model.bias[4], model.conv_infos[4])) # conv5
acts[8] = pooling_forward(acts[7], model.pooling_infos[2]) # pool5
re_acts8 = acts[8].reshape([np.prod(acts[8].shape[0:3]), num_samples])
acts9 = model.weights[5] * re_acts8 + model.bias[5] # fc6
acts[9] = ele.relu(acts9)#(model.weights[5] * re_acts8 + model.bias[5]) # fc6
mask6 = owl.randb(acts[9].shape, dropout_rate)
acts[9] = ele.mult(acts[9], mask6) # drop6
acts10 = model.weights[6] * acts[9] + model.bias[6] # fc7
acts[10] = ele.relu(acts10)#(model.weights[6] * acts[9] + model.bias[6]) # fc7
mask7 = owl.randb(acts[10].shape, dropout_rate)
acts[10] = ele.mult(acts[10], mask7) # drop7
acts[11] = model.weights[7] * acts[10] + model.bias[7] # fc8
acts[12] = softmax_forward(acts[11].reshape([1000, 1, 1, num_samples]), soft_op.instance).reshape([1000, num_samples]) # prob
# error
sens[11] = acts[12] - label
# BP
sens[10] = model.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] = model.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] = (model.weights[5].trans() * sens[9]).reshape(acts[8].shape) # fc6
sens[7] = pooling_backward(sens[8], acts[8], acts[7], model.pooling_infos[2]) # pool5
sens[7] = ele.relu_back(sens[7], acts[7]) # relu5
sens[6] = conv_backward_data(sens[7], model.weights[4], model.conv_infos[4]) # conv5
sens[6] = ele.relu_back(sens[6], acts[6]) # relu4
sens[5] = conv_backward_data(sens[6], model.weights[3], model.conv_infos[3]) # conv4
sens[5] = ele.relu_back(sens[5], acts[5]) # relu3
sens[4] = conv_backward_data(sens[5], model.weights[2], model.conv_infos[2]) # conv3
sens[3] = pooling_backward(sens[4], acts[4], acts[3], model.pooling_infos[1]) # pool2
sens[3] = ele.relu_back(sens[3], acts[3]) # relu2
sens[2] = conv_backward_data(sens[3], model.weights[1], model.conv_infos[1]) # conv2
sens[1] = pooling_backward(sens[2], acts[2], acts[1], model.pooling_infos[0]) # 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] = conv_backward_filter(sens[7], acts[6], model.conv_infos[4])
weightsgrad[3] = conv_backward_filter(sens[6], acts[5], model.conv_infos[3])
weightsgrad[2] = conv_backward_filter(sens[5], acts[4], model.conv_infos[2])
weightsgrad[1] = conv_backward_filter(sens[3], acts[2], model.conv_infos[1])
weightsgrad[0] = conv_backward_filter(sens[1], acts[0], model.conv_infos[0])
biasgrad[7] = sens[11].sum(1)
biasgrad[6] = sens[10].sum(1)
biasgrad[5] = sens[9].sum(1)
biasgrad[4] = conv_backward_bias(sens[7])
biasgrad[3] = conv_backward_bias(sens[6])
biasgrad[2] = conv_backward_bias(sens[5])
biasgrad[1] = conv_backward_bias(sens[3])
biasgrad[0] = conv_backward_bias(sens[1])
return acts[12]
def train_one_mb(model, data, label, weightsgrad, biasgrad, dropout_rate):
num_samples = data.shape[-1]
num_layers = 20
acts = [None] * num_layers
sens = [None] * num_layers
# FF
acts[0] = data
acts1 = conv_forward(acts[0], model.weights[0], model.bias[0],
model.conv_infos[0])
acts[1] = ele.relu(
acts1
) #(conv_forward(acts[0], model.weights[0], model.bias[0], model.conv_infos[0])) # conv1
acts[2] = pooling_forward(acts[1], model.pooling_infos[0]) # pool1
acts3 = conv_forward(acts[2], model.weights[1], model.bias[1],
model.conv_infos[1]) # conv2
acts[3] = ele.relu(
acts3
) #(conv_forward(acts[2], model.weights[1], model.bias[1], model.conv_infos[1])) # conv2
acts[4] = pooling_forward(acts[3], model.pooling_infos[1]) # pool2
acts5 = conv_forward(acts[4], model.weights[2], model.bias[2],
model.conv_infos[2]) # conv3
acts[5] = ele.relu(
acts5
) #(conv_forward(acts[4], model.weights[2], model.bias[2], model.conv_infos[2])) # conv3
acts6 = conv_forward(acts[5], model.weights[3], model.bias[3],
model.conv_infos[3]) # conv4
acts[6] = ele.relu(
acts6
) #(conv_forward(acts[5], model.weights[3], model.bias[3], model.conv_infos[3])) # conv4
acts7 = conv_forward(acts[6], model.weights[4], model.bias[4],
model.conv_infos[4]) # conv5
acts[7] = ele.relu(
acts7
) #(conv_forward(acts[6], model.weights[4], model.bias[4], model.conv_infos[4])) # conv5
acts[8] = pooling_forward(acts[7], model.pooling_infos[2]) # pool5
re_acts8 = acts[8].reshape([np.prod(acts[8].shape[0:3]), num_samples])
acts9 = model.weights[5] * re_acts8 + model.bias[5] # fc6
acts[9] = ele.relu(
acts9) #(model.weights[5] * re_acts8 + model.bias[5]) # fc6
mask6 = owl.randb(acts[9].shape, dropout_rate)
acts[9] = ele.mult(acts[9], mask6) # drop6
acts10 = model.weights[6] * acts[9] + model.bias[6] # fc7
acts[10] = ele.relu(
acts10) #(model.weights[6] * acts[9] + model.bias[6]) # fc7
mask7 = owl.randb(acts[10].shape, dropout_rate)
acts[10] = ele.mult(acts[10], mask7) # drop7
acts[11] = model.weights[7] * acts[10] + model.bias[7] # fc8
acts[12] = softmax_forward(acts[11].reshape([1000, 1, 1, num_samples]),
soft_op.instance).reshape([1000,
num_samples]) # prob
# error
sens[11] = acts[12] - label
# BP
sens[10] = model.weights[7].trans() * sens[11] # fc8
sens[10] = ele.mult(sens[10], mask7) # drop7
sens[10] = ele.relu_back(sens[10], acts[10], acts10) # relu7
sens[9] = model.weights[6].trans() * sens[10]
sens[9] = ele.mult(sens[9], mask6) # drop6
sens[9] = ele.relu_back(sens[9], acts[9], acts9) # relu6
sens[8] = (model.weights[5].trans() * sens[9]).reshape(
acts[8].shape) # fc6
sens[7] = pooling_backward(sens[8], acts[8], acts[7],
model.pooling_infos[2]) # pool5
sens[7] = ele.relu_back(sens[7], acts[7], acts7) # relu5
sens[6] = conv_backward_data(sens[7], model.weights[4],
model.conv_infos[4]) # conv5
sens[6] = ele.relu_back(sens[6], acts[6], acts6) # relu4
sens[5] = conv_backward_data(sens[6], model.weights[3],
model.conv_infos[3]) # conv4
sens[5] = ele.relu_back(sens[5], acts[5], acts5) # relu3
sens[4] = conv_backward_data(sens[5], model.weights[2],
model.conv_infos[2]) # conv3
sens[3] = pooling_backward(sens[4], acts[4], acts[3],
model.pooling_infos[1]) # pool2
sens[3] = ele.relu_back(sens[3], acts[3], acts3) # relu2
sens[2] = conv_backward_data(sens[3], model.weights[1],
model.conv_infos[1]) # conv2
sens[1] = pooling_backward(sens[2], acts[2], acts[1],
model.pooling_infos[0]) # pool1
sens[1] = ele.relu_back(sens[1], acts[1], acts1) # relu1
weightsgrad[7] = sens[11] * acts[10].trans()
weightsgrad[6] = sens[10] * acts[9].trans()
weightsgrad[5] = sens[9] * re_acts8.trans()
weightsgrad[4] = conv_backward_filter(sens[7], acts[6],
model.conv_infos[4])
weightsgrad[3] = conv_backward_filter(sens[6], acts[5],
model.conv_infos[3])
weightsgrad[2] = conv_backward_filter(sens[5], acts[4],
model.conv_infos[2])
weightsgrad[1] = conv_backward_filter(sens[3], acts[2],
model.conv_infos[1])
weightsgrad[0] = conv_backward_filter(sens[1], acts[0],
model.conv_infos[0])
biasgrad[7] = sens[11].sum(1)
biasgrad[6] = sens[10].sum(1)
biasgrad[5] = sens[9].sum(1)
biasgrad[4] = conv_backward_bias(sens[7])
biasgrad[3] = conv_backward_bias(sens[6])
biasgrad[2] = conv_backward_bias(sens[5])
biasgrad[1] = conv_backward_bias(sens[3])
biasgrad[0] = conv_backward_bias(sens[1])
return acts[12]
def train_network(model, num_epochs = 100, minibatch_size=256,
dropout_rate = 0.5, eps_w = 0.01, eps_b = 0.01, mom = 0.9, wd = 0.0005):
gpu = owl.create_gpu_device(1)
owl.set_device(gpu)
num_layers = 20
count = 0
last = time.time()
dp = ImageNetDataProvider(mean_file='/home/minjie/data/imagenet/imagenet_mean.binaryproto',
train_db='/home/minjie/data/imagenet/ilsvrc12_train_lmdb',
val_db='/home/minjie/data/imagenet/ilsvrc12_val_lmdb',
test_db='/home/minjie/data/imagenet/ilsvrc12_test_lmdb')
acts = [None] * num_layers
sens = [None] * num_layers
for i in xrange(num_epochs):
print "---------------------Epoch #", i
sys.stdout.flush()
for (samples, labels) in dp.get_train_mb(minibatch_size):
num_samples = samples.shape[0]
acts = [None] * num_layers
sens = [None] * num_layers
'''
thisimg = samples[0, :]
print thisimg
imgdata = np.transpose(thisimg.reshape([3, 227*227])).reshape([227, 227, 3])
print imgdata
img = Image.fromarray(imgdata.astype(np.uint8))
img.save('testimg.jpg', format='JPEG')
exit(0)
'''
# FF
acts[0] = owl.from_nparray(samples).reshape([227, 227, 3, num_samples])
#print np.array(acts[0].tolist())[0:227*227*3]
target = owl.from_nparray(labels)
#np.set_printoptions(linewidth=200)
#print acts[0].shape, model.weights[0].shape, model.bias[0].shape
#im = np.array(acts[0].tolist()).reshape([num_samples, 227, 227, 3])
#print im[0,:,:,0]
#print im[0,:,:,1]
#print im[0,:,:,2]
#print target.max_index(0).tolist()[0:20]
#sys.exit()
acts1 = conv_forward(acts[0], model.weights[0], model.bias[0], model.conv_infos[0])
acts[1] = ele.relu(acts1)#(conv_forward(acts[0], model.weights[0], model.bias[0], model.conv_infos[0])) # conv1
acts[2] = pooling_forward(acts[1], model.pooling_infos[0]) # pool1
acts3 = conv_forward(acts[2], model.weights[1], model.bias[1], model.conv_infos[1]) # conv2
acts[3] = ele.relu(acts3)#(conv_forward(acts[2], model.weights[1], model.bias[1], model.conv_infos[1])) # conv2
acts[4] = pooling_forward(acts[3], model.pooling_infos[1]) # pool2
acts5 = conv_forward(acts[4], model.weights[2], model.bias[2], model.conv_infos[2]) # conv3
acts[5] = ele.relu(acts5)#(conv_forward(acts[4], model.weights[2], model.bias[2], model.conv_infos[2])) # conv3
acts6 = conv_forward(acts[5], model.weights[3], model.bias[3], model.conv_infos[3]) # conv4
acts[6] = ele.relu(acts6)#(conv_forward(acts[5], model.weights[3], model.bias[3], model.conv_infos[3])) # conv4
acts7 = conv_forward(acts[6], model.weights[4], model.bias[4], model.conv_infos[4]) # conv5
acts[7] = ele.relu(acts7)#(conv_forward(acts[6], model.weights[4], model.bias[4], model.conv_infos[4])) # conv5
acts[8] = pooling_forward(acts[7], model.pooling_infos[2]) # pool5
re_acts8 = acts[8].reshape([np.prod(acts[8].shape[0:3]), num_samples])
acts9 = model.weights[5] * re_acts8 + model.bias[5] # fc6
acts[9] = ele.relu(acts9)#(model.weights[5] * re_acts8 + model.bias[5]) # fc6
mask6 = owl.randb(acts[9].shape, dropout_rate)
acts[9] = ele.mult(acts[9], mask6) # drop6
acts10 = model.weights[6] * acts[9] + model.bias[6] # fc7
acts[10] = ele.relu(acts10)#(model.weights[6] * acts[9] + model.bias[6]) # fc7
mask7 = owl.randb(acts[10].shape, dropout_rate)
acts[10] = ele.mult(acts[10], mask7) # drop7
acts[11] = model.weights[7] * acts[10] + model.bias[7] # fc8
acts[12] = softmax_forward(acts[11].reshape([1000, 1, 1, num_samples]), soft_op.instance).reshape([1000, num_samples]) # prob
# error
sens[11] = acts[12] - target
# BP
sens[10] = model.weights[7].trans() * sens[11] # fc8
sens[10] = ele.mult(sens[10], mask7) # drop7
sens[10] = ele.relu_back(sens[10], acts[10], acts10) # relu7
sens[9] = model.weights[6].trans() * sens[10]
sens[9] = ele.mult(sens[9], mask6) # drop6
sens[9] = ele.relu_back(sens[9], acts[9], acts9) # relu6
sens[8] = (model.weights[5].trans() * sens[9]).reshape(acts[8].shape) # fc6
sens[7] = pooling_backward(sens[8], acts[8], acts[7], model.pooling_infos[2]) # pool5
sens[7] = ele.relu_back(sens[7], acts[7], acts7) # relu5
sens[6] = conv_backward_data(sens[7], model.weights[4], model.conv_infos[4]) # conv5
sens[6] = ele.relu_back(sens[6], acts[6], acts6) # relu4
sens[5] = conv_backward_data(sens[6], model.weights[3], model.conv_infos[3]) # conv4
sens[5] = ele.relu_back(sens[5], acts[5], acts5) # relu3
sens[4] = conv_backward_data(sens[5], model.weights[2], model.conv_infos[2]) # conv3
sens[3] = pooling_backward(sens[4], acts[4], acts[3], model.pooling_infos[1]) # pool2
sens[3] = ele.relu_back(sens[3], acts[3], acts3) # relu2
sens[2] = conv_backward_data(sens[3], model.weights[1], model.conv_infos[1]) # conv2
sens[1] = pooling_backward(sens[2], acts[2], acts[1], model.pooling_infos[0]) # pool1
sens[1] = ele.relu_back(sens[1], acts[1], acts1) # relu1
model.weightsdelta[7] = mom * model.weightsdelta[7] - eps_w / num_samples * (sens[11] * acts[10].trans() + wd * model.weights[7])
model.biasdelta[7] = mom * model.biasdelta[7] - eps_b / num_samples * (sens[11].sum(1) + wd * model.bias[7])
model.weightsdelta[6] = mom * model.weightsdelta[6] - eps_w / num_samples * (sens[10] * acts[9].trans() + wd * model.weights[6])
model.biasdelta[6] = mom * model.biasdelta[6] - eps_b / num_samples * (sens[10].sum(1) + wd * model.bias[6])
model.weightsdelta[5] = mom * model.weightsdelta[5] - eps_w / num_samples * (sens[9] * re_acts8.trans() + wd * model.weights[5])
model.biasdelta[5] = mom * model.biasdelta[5] - eps_b / num_samples * (sens[9].sum(1) + wd * model.bias[5])
model.weightsdelta[4] = mom * model.weightsdelta[4] - eps_w / num_samples * (conv_backward_filter(sens[7], acts[6], model.conv_infos[4]) + wd * model.weights[4])
model.biasdelta[4] = mom * model.biasdelta[4] - eps_b / num_samples * (conv_backward_bias(sens[7]) + wd * model.bias[4])
model.weightsdelta[3] = mom * model.weightsdelta[3] - eps_w / num_samples * (conv_backward_filter(sens[6], acts[5], model.conv_infos[3]) + wd * model.weights[3])
model.biasdelta[3] = mom * model.biasdelta[3] - eps_b / num_samples * (conv_backward_bias(sens[6]) + wd * model.bias[3])
model.weightsdelta[2] = mom * model.weightsdelta[2] - eps_w / num_samples * (conv_backward_filter(sens[5], acts[4], model.conv_infos[2]) + wd * model.weights[2])
model.biasdelta[2] = mom * model.biasdelta[2] - eps_b / num_samples * (conv_backward_bias(sens[5]) + wd * model.bias[2])
model.weightsdelta[1] = mom * model.weightsdelta[1] - eps_w / num_samples * (conv_backward_filter(sens[3], acts[2], model.conv_infos[1]) + wd * model.weights[1])
model.biasdelta[1] = mom * model.biasdelta[1] - eps_b / num_samples * (conv_backward_bias(sens[3]) + wd * model.bias[1])
model.weightsdelta[0] = mom * model.weightsdelta[0] - eps_w / num_samples * (conv_backward_filter(sens[1], acts[0], model.conv_infos[0]) + wd * model.weights[0])
model.biasdelta[0] = mom * model.biasdelta[0] - eps_b / num_samples * (conv_backward_bias(sens[1]) + wd * model.bias[0])
for k in range(8):
model.weights[k] += model.weightsdelta[k]
model.bias[k] += model.biasdelta[k]
count = count + 1
#if count % 2 == 0:
#acts[18].start_eval()
if count % 10 == 0:
print_training_accuracy(acts[12], target, num_samples)
print "time: %s" % (time.time() - last)
last = time.time()
def ff(self, x):
self.dropmask = owl.randb(x, self.params.dropout_param.dropout_ratio)
return ele.mult(x, self.dropmask)
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 train_one_mb(model, data, label, weightsgrad, biasgrad):
#Be careful, python list is like pointer
acts = [None] * model.num_layers
sens = [None] * model.num_layers
beforeacts = [None] * model.num_layers
beforedropout = [None] * model.num_layers
dropoutmask = [None] * model.num_layers
before2fullyact = []
conv2fullylayer = model.num_layers
acts[0] = data
num_samples = data.shape[-1]
num_class = label.shape[0]
#find the reshape layer
for i in range(0, model.num_layers - 1):
#if from conv 2 fully
if (i < model.num_layers - 2) and (
model.ff_infos[i]['ff_type'] == 'conv'
or model.ff_infos[i]['ff_type'] == 'pooling') and (
model.ff_infos[i + 1]['ff_type'] == 'fully'):
conv2fullylayer = i + 1
break
for i in range(0, model.num_layers - 1):
if model.ff_infos[i]['ff_type'] == 'conv':
#print '%d conv ff' % (i)
beforeacts[i + 1] = conv_forward(acts[i], model.weights[i],
model.bias[i],
model.ff_infos[i]['conv_info'])
elif model.ff_infos[i]['ff_type'] == 'pooling':
#print '%d pooling ff' % (i)
beforeacts[i + 1] = pooling_forward(
acts[i], model.ff_infos[i]['pooling_info'])
else:
#print '%d fully ff' % (i)
beforeacts[i + 1] = model.weights[i] * acts[i] + model.bias[i]
#activation function
if model.ff_infos[i]['neuron_type'] == 'RELU':
#print '%d relu ff' % (i)
acts[i + 1] = ele.relu(beforeacts[i + 1])
elif model.ff_infos[i]['neuron_type'] == 'SOFTMAX':
#print '%d softmax ff' % (i)
acts[i + 1] = softmax_forward(
beforeacts[i + 1].reshape([num_class, 1, 1, num_samples]),
soft_op.instance).reshape([num_class, num_samples]) # prob
else:
#print '%d linear ff' % (i)
acts[i + 1] = beforeacts[i + 1]
#dropout
beforedropout[i + 1] = acts[i + 1]
if model.ff_infos[i]['dropout_rate'] > 0:
#print '%d dropout ff' % (i)
dropoutmask[i + 1] = owl.randb(acts[i + 1].shape,
model.ff_infos[i]['dropout_rate'])
acts[i + 1] = ele.mult(beforedropout[i + 1], dropoutmask[i + 1])
if i + 1 == conv2fullylayer:
before2fullyact = acts[i + 1]
acts[i + 1] = before2fullyact.reshape(
[np.prod(before2fullyact.shape[0:3]), num_samples])
# error
sens[model.num_layers - 1] = acts[model.num_layers - 1] - label
#bp
for i in range(model.num_layers - 1, 0, -1):
if model.ff_infos[i - 1]['ff_type'] == 'conv':
sens[i - 1] = conv_backward_data(
sens[i], model.weights[i - 1],
model.ff_infos[i - 1]['conv_info'])
elif model.ff_infos[i - 1]['ff_type'] == 'pooling':
if i == conv2fullylayer:
sens[i - 1] = pooling_backward(
sens[i].reshape(before2fullyact.shape), before2fullyact,
acts[i - 1], model.ff_infos[i - 1]['pooling_info'])
else:
sens[i - 1] = pooling_backward(
sens[i], acts[i], acts[i - 1],
model.ff_infos[i - 1]['pooling_info'])
else:
sens[i - 1] = model.weights[i - 1].trans() * sens[i]
if i - 2 >= 0:
#dropout
if model.ff_infos[i - 2]['dropout_rate'] > 0:
sens[i - 1] = ele.mult(sens[i - 1], dropoutmask[i - 1])
#backact
if model.ff_infos[i - 2]['neuron_type'] == 'RELU':
sens[i - 1] = ele.relu_back(sens[i - 1], beforedropout[i - 1],
beforeacts[i - 1])
else:
sens[i - 1] = sens[i - 1]
#gradient
for i in range(0, model.num_layers - 1):
if model.ff_infos[i]['ff_type'] == 'conv':
weightsgrad[i] = conv_backward_filter(
sens[i + 1], acts[i], model.ff_infos[i]['conv_info'])
biasgrad[i] = conv_backward_bias(sens[i + 1])
elif model.ff_infos[i]['ff_type'] == 'fully':
weightsgrad[i] = sens[i + 1] * acts[i].trans()
biasgrad[i] = sens[i + 1].sum(1)
else:
continue
return acts[model.num_layers - 1]