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):
gpu0 = owl.create_gpu_device(0)
owl.set_device(gpu0)
num_weights = 8
count = 0
last = time.time()
dp = ImageNetDataProvider(mean_file='/home/yutian/data/config_file/google_model/imagenet_mean.binaryproto',
train_db='/home/yutian/data/imagenet/ilsvrc12_train_lmdb',
val_db='/home/yutian/data/imagenet/ilsvrc12_val_lmdb',
test_db='/home/yutian/data/imagenet/ilsvrc12_test_lmdb')
for i in xrange(num_epochs):
print "---------------------Epoch #", i
for (samples, labels) in dp.get_train_mb(minibatch_size):
count = count + 1
num_samples = samples.shape[0]
data = owl.from_numpy(samples).reshape([227, 227, 3, num_samples])
target = owl.from_numpy(labels)
out, weightsgrad, biasgrad = model.train_one_mb(data, target, dropout_rate)
model.update(weightsgrad, biasgrad, num_samples, mom, eps_w, wd)
if count % 4 == 0:
print_training_accuracy(out, target, data.shape[-1])
print "time: %s" % (time.time() - last)
last = time.time()
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):
gpu0 = owl.create_gpu_device(0)
owl.set_device(gpu0)
num_weights = 8
count = 0
last = time.time()
dp = ImageNetDataProvider(mean_file='/home/yutian/data/config_file/google_model/imagenet_mean.binaryproto',
train_db='/home/yutian/data/imagenet/ilsvrc12_train_lmdb',
val_db='/home/yutian/data/imagenet/ilsvrc12_val_lmdb',
test_db='/home/yutian/data/imagenet/ilsvrc12_test_lmdb')
for i in xrange(num_epochs):
print "---------------------Epoch #", i
for (samples, labels) in dp.get_train_mb(minibatch_size):
count = count + 1
num_samples = samples.shape[0]
data = owl.from_numpy(samples).reshape([227, 227, 3, num_samples])
target = owl.from_numpy(labels)
out, weightsgrad, biasgrad = model.train_one_mb(data, target, dropout_rate)
model.update(weightsgrad, biasgrad, num_samples, mom, eps_w, wd)
if count % 4 == 0:
print_training_accuracy(out, target, data.shape[-1])
print "time: %s" % (time.time() - last)
last = time.time()
def train_network(model, num_epochs = 100, minibatch_size=10,
dropout_rate = 0.5, eps_w = 0.01, mom = 0.9, wd = 0.0005):
gpu0 = owl.create_gpu_device(0)
owl.set_device(gpu0)
num_weights = 8
count = 0
last = time.time()
cropped_size = 224
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')
#mark the output layer
output_layer = 'prob'
for i in xrange(num_epochs):
print "---------------------Epoch #", i
for (samples, labels) in dp.get_train_mb(minibatch_size, cropped_size):
count = count + 1
num_samples = samples.shape[0]
data = owl.from_numpy(samples).reshape([cropped_size, cropped_size, 3, num_samples])
target = owl.from_numpy(labels)
model.ff(data, target)
print_training_accuracy(model.layers[output_layer].get_act(), target, minibatch_size)
model.bp(data, target)
exit(0)
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 = [None] * 2
gpu[0] = owl.create_gpu_device(0)
gpu[1] = owl.create_gpu_device(1)
num_layers = 20
num_weights = 8
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')
minibatch_size = minibatch_size / 2
wgrad = [None] * 2
bgrad = [None] * 2
num_samples = 0
for i in xrange(num_epochs):
print "---------------------Epoch #", i
for (samples, labels) in dp.get_train_mb(minibatch_size):
#for j in range(300):
count = count + 1
gpuid = count % 2
owl.set_device(gpu[gpuid])
data = owl.from_numpy(samples).reshape([227, 227, 3, samples.shape[0]])
label = owl.from_numpy(labels)
#data = owl.randn([227, 227, 3, 128], 0.0, 0.01)
#label = owl.randn([1000, 128], 0.0, 0.01)
num_samples += data.shape[-1]
(out, wgrad[gpuid], bgrad[gpuid]) = model.train_one_mb(data, label, dropout_rate)
out.start_eval()
if count % 2 != 0:
continue
for k in range(num_weights):
wgrad[0][k] += wgrad[1][k]
bgrad[0][k] += bgrad[1][k]
model.update(wgrad[0], bgrad[0], num_samples, mom, eps_w, wd)
if count % 8 == 0:
print_training_accuracy(out, label, data.shape[-1])
print "time: %s" % (time.time() - last)
last = time.time()
num_samples = 0
wgrad = [None] * 2
bgrad = [None] * 2
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 = [None] * 2
gpu[0] = owl.create_gpu_device(0)
gpu[1] = owl.create_gpu_device(1)
num_layers = 20
num_weights = 8
count = 0
last = time.time()
dp = ImageNetDataProvider(mean_file='/home/yutian/data/config_file/google_model/imagenet_mean.binaryproto',
train_db='/home/yutian/data/imagenet/ilsvrc12_train_lmdb',
val_db='/home/yutian/data/imagenet/ilsvrc12_val_lmdb',
test_db='/home/yutian/data/imagenet/ilsvrc12_test_lmdb')
minibatch_size = minibatch_size / 2
wgrad = [None] * 2
bgrad = [None] * 2
num_samples = 0
for i in xrange(num_epochs):
print "---------------------Epoch #", i
for (samples, labels) in dp.get_train_mb(minibatch_size):
#for j in range(300):
count = count + 1
gpuid = count % 2
owl.set_device(gpu[gpuid])
data = owl.from_numpy(samples).reshape([227, 227, 3, samples.shape[0]])
label = owl.from_numpy(labels)
#data = owl.randn([227, 227, 3, 128], 0.0, 0.01)
#label = owl.randn([1000, 128], 0.0, 0.01)
num_samples += data.shape[-1]
(out, wgrad[gpuid], bgrad[gpuid]) = model.train_one_mb(data, label, dropout_rate)
if count % 2 != 0:
continue
for k in range(num_weights):
wgrad[0][k] += wgrad[1][k]
bgrad[0][k] += bgrad[1][k]
model.update(wgrad[0], bgrad[0], num_samples, mom, eps_w, wd)
if count % 8 == 0:
print_training_accuracy(out, label, data.shape[-1])
print "time: %s" % (time.time() - last)
last = time.time()
num_samples = 0
wgrad = [None] * 2
bgrad = [None] * 2
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):
num_layers = model.num_layers
num_weights = model.num_weights
last = time.time()
num_samples = minibatch_size
minibatch_size = minibatch_size / num_gpu
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')
for i in xrange(num_epochs):
print "---------------------Epoch #", i
weightsgrad = [[None] * num_weights for z in range(num_gpu)]
biasgrad = [[None] * num_weights for z in range(num_gpu)]
j = 0
for (samples, labels) in dp.get_train_mb(minibatch_size):
j += 1
count = j % num_gpu
owl.set_device(gpu_array[count])
data = owl.from_nparray(samples).reshape([227, 227, 3, minibatch_size])
label = owl.from_nparray(labels)
out = train_one_mb(model, data, label, weightsgrad[count], biasgrad[count], dropout_rate)
# out.start_eval()
if count == 0:
# Update
for k in range(num_weights):
for l in range(1, num_gpu):
weightsgrad[0][k] = weightsgrad[0][k] + weightsgrad[l][k]
biasgrad[0][k] = biasgrad[0][k] + biasgrad[l][k]
model.weightsdelta[k] = mom * model.weightsdelta[k] - eps_w / num_samples * (weightsgrad[0][k] + wd * model.weights[k])
model.biasdelta[k] = mom * model.biasdelta[k] - eps_b / num_samples * (biasgrad[0][k] + wd * model.bias[k])
model.weights[k] += model.weightsdelta[k]
model.weights[k].start_eval()
model.bias[k] += model.biasdelta[k]
model.bias[k].start_eval()
if j % (lazy * num_gpu) == 0:
print_training_accuracy(out, label, minibatch_size)
print "time: %s" % (time.time() - last)
last = time.time()
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_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()