def test_learn_with_depth_and_multiple_filter(self):
print("al")
net = fcnetwork.FCLayer(arch=[784, 100, 10], load_path=None)
enhancedModel = model.Model(learning_rate=0.1,
dataset=None,
layerContainer=[net])
net = enhancedModel.getLayerContainer()[0]
before_w, before_b = net.getWeightsAndBiases()
enhancedModel.test_learn_mnist(epoch=1, batch_size=30)
net = enhancedModel.getLayerContainer()[0]
after_w, after_b = net.getWeightsAndBiases()
for b, a in zip(before_w, after_w):
print("la")
print(b)
print(a)
self.assertEqual((b == a).all(), False)
for b, a in zip(before_b, after_b):
print("la")
self.assertEqual((b == a).all(), False)
def test_online_learning1(self):
pathdir = "./tensorfiles"
filename = "online_learning1"
fullypath = pathdir + "/" + filename
if not os.path.exists(os.path.join(pathdir, filename + ".bs1.npy")):
# print("la2")
ws1 = numpy.array([[0.2, 0.3], [0.4, 0.5], [1.1, 0.1]])
# print(f"w = {ws1.shape}")
ws2 = numpy.array([[0.9, 0.3, 0.1], [0.3, 0.4, 0.1]])
ws = [ws1, ws2]
bs1 = [0, 0, 0]
bs2 = [0, 0]
# return
TensorFileManager("./tensorfiles").save(filename + ".bs1", bs1)
TensorFileManager("./tensorfiles").save(filename + ".bs2", bs2)
TensorFileManager("./tensorfiles").save(filename + ".ws1", ws1)
TensorFileManager("./tensorfiles").save(filename + ".ws2", ws2)
net = fcn.FCLayer(arch=[2, 3, 2], load_path=filename)
input = numpy.array([0.1, 0.2])
expected_res = numpy.array([1, 0])
previous_delta = numpy.array([-0.00601069, 0.00878729, 0.00173201])
ws1 = numpy.array([[0.2, 0.3], [0.4, 0.5], [1.1, 0.1]])
expected_delta = numpy.dot(previous_delta, ws1)
net.compute(input, learn=True)
net.learn(expected_res)
returnedDelta = net.getLastDelta()
res = numpy.isclose(returnedDelta, expected_delta, atol=1e-5)
self.assertEqual(res.all(), True)
def model_for_mnist():
layerContainer = [fcnetwork.FCLayer(arch=[784, 100, 10])]
learning_rate = 0.01
modelMnist = model.Model(learning_rate=learning_rate,
dataset=None,
layerContainer=layerContainer)
modelMnist.test_learn_mnist(epoch=50, batch_size=10)
def test_perceptron1(self):
pathdir = "./tensorfiles"
filename = "perceptest1"
fullypath = pathdir + "/" + filename
if not os.path.exists(os.path.join(pathdir, filename + ".bs1.npy")):
TensorFileManager("./tensorfiles").save(filename + ".bs1", numpy.array([[0.4]]))
TensorFileManager("./tensorfiles").save(filename + ".ws1", numpy.array([[1.1]]))
net = fcn.FCLayer(arch=[1, 1], load_path=filename)
expected_res = fcn.sigmoid(0.4 + 1.1)
res = net.compute(numpy.array([1]))
self.assertEqual(res, expected_res)
def test_learn():
layerContainer = [
#3, 150, 150
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c1",
filtershape=(32, 3, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(3, 150, 150)),
#32, 74, 74
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c2",
filtershape=(64, 32, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(32, 74, 74)),
#64, 36, 36
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c3",
filtershape=(128, 64, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(64, 36, 36)),
#128, 17, 17
fcnetwork.FCLayer(optimizer=adam.AdamFC(),
load_path="ff2fcn1",
arch=[36992, 512, 128],
activation_func="relu",
is_classifier=False),
softmax.SoftmaxLayer(optimizer=adam.AdamFC(),
load_path="ff2softm",
arch=[128, 5])
]
# signal.signal(signal.SIGINT, signal_handler)
## here learning rate is useless
model_FAndF = model.Model(learning_rate=0.001,
dataset=None,
layerContainer=layerContainer)
pic = iml.ImageLoader.getOutputNpArray(example1,
crop=True,
crop_size=(0, 0, 150, 150))
y = model_FAndF.compute(pic)
print(y)
def flowerAndFun(path=example1):
input = iml.ImageLoader.getOutputNpArray(path,
crop=True,
crop_size=(0, 0, 150, 150))
layerContainer = [
#3, 150, 150
conv.ConvLayer(optimizer=adam.AdamConv(),
filtershape=(32, 3, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(3, 150, 150)),
#32, 74, 74
conv.ConvLayer(optimizer=adam.AdamConv(),
filtershape=(64, 32, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(32, 74, 74)),
#64, 36, 36
conv.ConvLayer(optimizer=adam.AdamConv(),
filtershape=(128, 64, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(64, 36, 36)),
#128, 17, 17
fcnetwork.FCLayer(optimizer=adam.AdamFC(), arch=[36992, 512, 128, 5])
]
learning_rate = 0.0001
model_FAndF = model.Model(learning_rate=learning_rate,
dataset=None,
layerContainer=layerContainer)
# output = model_FAndF.compute(input, learn=True)
# model_FAndF.soft_learn()
model_FAndF.test_learn(epoch=50)
def test_shallow_network1(self):
pathdir = "./tensorfiles"
filename = "shallow_network1"
fullypath = pathdir + "/" + filename
if not os.path.exists(os.path.join(pathdir, filename + ".bs1.npy")):
# ws = [[numpy.array([1, 0.7]), numpy.array([0.1, 0.8]), numpy.array([0.4, 0.9])], [numpy.array([0.9, 0.7, 0.1])]]
ws1 = numpy.array([[1, 0.7], [0.1, 0.8], [0.4, 0.9]])
ws2 = numpy.array([[0.9, 0.7, 0.1]])
ws = [ws1, ws2]
bs1 = [[0, 0, 0]]
bs2 = [[0]]
# return
TensorFileManager("./tensorfiles").save(filename + ".bs1", bs1)
TensorFileManager("./tensorfiles").save(filename + ".bs2", bs2)
TensorFileManager("./tensorfiles").save(filename + ".ws1", ws1)
TensorFileManager("./tensorfiles").save(filename + ".ws2", ws2)
net = fcn.FCLayer(arch=[2, 3, 1], load_path=filename)
input = numpy.array([0.3, 0.4])
expected_res = 0.7406534729647368
res = net.compute(input)
self.assertEqual(res, expected_res)
def test_learn_with_depth_and_multiple_filter(self):
# input 3, 5, 5
# filter 3, 3, 2, 2 => output 3, 4, 4
input = numpy.array([[[0.1, 2, 0.11, 0.3, 1], [0, 0.4, 0.4, 0.36, 1],
[0, 0.12, 0.27, 0.34, -3],
[0.62, 0.12, 0.11, 10, 1], [0, 56, 11, 23, 44]],
[[0.11, 0.58, -1, 2, 0.35],
[0.1, 0.36, 0.12, 0.8, 0.27],
[0.27, 0, 0.64, 1, 0.12], [1, -1, 0.4, 3, 11],
[0, 0.56, 0.11, 0.23, 0.44]],
[[1, 3, 0.24, 5, -1], [0.12, 2, 0, 1, 11],
[-0.1, 0.2, 0.3, 0.11, 0.22],
[12, 0.27, 0.18, 0.2, 0.34],
[0, 0.56, 0.11, 0.23, 0.44]]])
pathdir = "./tensorfiles"
filename = "conv_test_depth_multiple_filter"
fullypath = pathdir + "/" + filename
if not os.path.exists(os.path.join(pathdir, filename + ".bs1.npy")):
tm = TensorFileManager("./tensorfiles")
containerfilter = numpy.ndarray((0, 3, 2, 2))
f3 = numpy.array([[[0.3, 0.3], [0.3, 0.3]], [[0.3, 0.3],
[0.3, 0.3]],
[[0.3, 0.3], [0.3, 0.3]]])
containerfilter = numpy.insert(containerfilter, 0, f3, 0)
f2 = numpy.array([[[0.2, 0.2], [0.2, 0.2]], [[0.2, 0.2],
[0.2, 0.2]],
[[0.2, 0.2], [0.2, 0.2]]])
containerfilter = numpy.insert(containerfilter, 0, f2, 0)
f1 = numpy.array([[[0.1, 0.1], [0.1, 0.1]], [[0.1, 0.1],
[0.1, 0.1]],
[[0.1, 0.1], [0.1, 0.1]]])
containerfilter = numpy.insert(containerfilter, 0, f1, 0)
biases = numpy.zeros((3, ))
tm.save("conv_test_depth_multiple_filter.bs1", biases)
tm.save("conv_test_depth_multiple_filter.ws1", containerfilter)
l1 = conv.ConvLayer(load_path=filename,
filtershape=(3, 3, 2, 2),
pool=pool.PoolLayer(),
activation_function="relu")
output = l1.compute(input)
filename = "fcn_test_depth_multiple_filter"
if not os.path.exists(os.path.join(pathdir, filename + ".bs1.npy")):
tm = TensorFileManager("./tensorfiles")
ws = numpy.array([[
0.1, 0.3, 0.5, 0.12, 0.9, 0.12, 0.9, 0.10, 0.1, 0.11, 0.12,
0.13
],
[
0.34, 0.3, 0.64, 0.12, 1, 0.12, 0.1, 0.1,
0.12, 0.13, 0.15, 0.11
]])
biases = numpy.zeros((2, ))
tm.save("fcn_test_depth_multiple_filter.bs1", biases)
tm.save("fcn_test_depth_multiple_filter.ws1", ws)
l2 = fcnetwork.FCLayer(arch=[12, 2],
load_path="fcn_test_depth_multiple_filter")
expected_res = numpy.array([1, 0])
l2.compute(input=output, learn=True)
l2.learn(expected_res)
delta = l2.getLastDelta()
l1.learn(delta)
nabla_w = l1.getNablaW()
self.assertEqual(numpy.isclose(nabla_w[0][0][0][0], 4.1609570961e-09),
True)
self.assertEqual(numpy.isclose(nabla_w[1][1][0][0], 1.8135273233e-09),
True)
def flowerAndFun2(path=example1):
def signal_handler(sig, frame):
# saveModel.saveLayers(["ff2c1", "ff2c2", "ff2c3", "ff2fcn1", "ff2softm"])
pic = iml.ImageLoader.getOutputNpArray(example1,
crop=True,
crop_size=(0, 0, 150, 150))
y = saveModel.compute(pic)
saveModel.saveLayers([
"ff2c1", "d1", "ff2c2", "d2", "ff2c3", "d3", "ff2fcn1", "d4",
"ff2softm"
])
print(y)
sys.exit(0)
input = iml.ImageLoader.getOutputNpArray(path,
crop=True,
crop_size=(0, 0, 150, 150))
# layerContainer = [
# #3, 150, 150
# conv.ConvLayer(optimizer=adam.AdamConv(), filtershape=(32, 3, 3, 3), stride_length=1, pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2), ishape=(3, 150, 150)),
# dropout.DropoutLayer(p=0.2, ishape=(32, 74, 74)),
# #32, 74, 74
# conv.ConvLayer(optimizer=adam.AdamConv(), filtershape=(64, 32, 3, 3), stride_length=1, pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2), ishape=(32, 74, 74)),
# dropout.DropoutLayer(p=0.2, ishape=(64, 36, 36)),
# #64, 36, 36
# conv.ConvLayer(optimizer=adam.AdamConv(), filtershape=(128, 64, 3, 3), stride_length=1, pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2), ishape=(64, 36, 36)),
# dropout.DropoutLayer(p=0.2, ishape=(128, 17, 17)),
# #128, 17, 17
# fcnetwork.FCLayer(optimizer=adam.AdamFC(), arch=[36992, 512, 128], activation_func="relu", is_classifier=False),
# dropout.DropoutLayer(p=0.2, ishape=(128,)),
# softmax.SoftmaxLayer(optimizer=adam.AdamFC(), arch=[128, 5])
# ]
# # load net
layerContainer = [
#3, 150, 150
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c1",
filtershape=(32, 3, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(3, 150, 150)),
dropout.DropoutLayer(p=0.2, ishape=(32, 74, 74)),
#32, 74, 74
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c2",
filtershape=(64, 32, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(32, 74, 74)),
dropout.DropoutLayer(p=0.2, ishape=(64, 36, 36)),
#64, 36, 36
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c3",
filtershape=(128, 64, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(64, 36, 36)),
dropout.DropoutLayer(p=0.2, ishape=(128, 17, 17)),
#128, 17, 17
fcnetwork.FCLayer(optimizer=adam.AdamFC(),
load_path="ff2fcn1",
arch=[36992, 512, 128],
activation_func="relu",
is_classifier=False),
dropout.DropoutLayer(p=0.2, ishape=(128, )),
softmax.SoftmaxLayer(optimizer=adam.AdamFC(),
load_path="ff2softm",
arch=[128, 5])
]
signal.signal(signal.SIGINT, signal_handler)
## here learning rate is useless
model_FAndF = model.Model(learning_rate=0.001,
dataset=None,
layerContainer=layerContainer)
saveModel = model_FAndF
# saveModel.saveLayers(["ff2c1", "ff2c2", "ff2c3", "ff2fcn1", "ff2softm"])
model_FAndF.test_learn(epoch=50)
def flowerAndFunModel(path=example1):
#it crops by 224x224 by default
print(f"Model load this picture as input: {path}")
input = iml.ImageLoader.getOutputNpArray(path,
crop=True,
crop_size=(0, 0, 150, 150))
dir = "./tensorfiles"
if not os.path.exists(dir + "/" + "ff2c1" + ".ws1.npy"):
layerContainer = [
#3, 150, 150
conv.ConvLayer(optimizer=adam.AdamConv(),
filtershape=(32, 3, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2),
stride_length=2),
ishape=(3, 150, 150)),
#32, 74, 74
conv.ConvLayer(optimizer=adam.AdamConv(),
filtershape=(64, 32, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2),
stride_length=2),
ishape=(32, 74, 74)),
#64, 36, 36
conv.ConvLayer(optimizer=adam.AdamConv(),
filtershape=(128, 64, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2),
stride_length=2),
ishape=(64, 36, 36)),
#128, 17, 17
fcnetwork.FCLayer(optimizer=adam.AdamFC(),
arch=[36992, 512, 128],
activation_func="relu",
is_classifier=False),
softmax.SoftmaxLayer(optimizer=adam.AdamFC(), arch=[128, 5])
]
ffM = model.Model(learning_rate=None,
dataset=None,
layerContainer=layerContainer)
ffM.saveLayers(["ff2c1", "ff2c2", "ff2c3", "ff2fcn1", "ff2softm"])
layerContainer = [
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c1",
filtershape=(32, 3, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(3, 150, 150)),
#32, 74, 74
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c2",
filtershape=(64, 32, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(32, 74, 74)),
#64, 36, 36
conv.ConvLayer(optimizer=adam.AdamConv(),
load_path="ff2c3",
filtershape=(128, 64, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(2, 2), stride_length=2),
ishape=(64, 36, 36)),
#128, 17, 17
fcnetwork.FCLayer(optimizer=adam.AdamFC(),
load_path="ff2fcn1",
arch=[36992, 512, 128],
activation_func="relu",
is_classifier=False),
softmax.SoftmaxLayer(optimizer=adam.AdamFC(),
load_path="ff2softm",
arch=[128, 5])
]
ffM = model.Model(learning_rate=None,
dataset=None,
layerContainer=layerContainer)
try:
output = ffM.compute(input)
except:
print("error occured in zf5 model")
return "error"
return return_response(output)
# print(zf5model())
# overfeat()
# a = numpy.array(
# [[1, 2, 3, 4, 5, 6],
# [1, 2, 3, 4, 5, 6],
# [1, 2, 3, 4, 5, 6],
# [1, 2, 3, 4, 5, 6],
# [1, 2, 3, 4, 5, 6],
# [1, 2, 3, 4, 5, 6]])
def zf5model(path=example1):
# from this architecture:
# https://www.researchgate.net/figure/Architecture-of-ZF-model-An-3-channels-image-with-224224-is-as-the-input-It-is_fig5_318577329
# image = iml.ImageLoader.getOutputNpArray(rp_dataset + "daisy/" + "5547758_eea9edfd54_n.jpg", gray=True)
# image = iml.ImageLoader.getCropedImage(rp_dataset + "daisy/" + "5547758_eea9edfd54_n.jpg")#.getOutputNpArray(rp_dataset + "daisy/" + "5547758_eea9edfd54_n.jpg")
#it crops by 224x224 by default
print(f"Model load this picture as input: {path}")
input = iml.ImageLoader.getOutputNpArray(path,
crop=True,
crop_size=(0, 0, 224, 224))
dir = "./tensorfiles"
if not os.path.exists(dir + "/" + "conv1" + ".ws1.npy"):
layerContainer = [
#3, 224, 224
conv.ConvLayer(padding=1,
filtershape=(96, 3, 7, 7),
stride_length=2,
pool=pool.PoolLayer(pad=1,
pool_size=(3, 3),
stride_length=2),
ishape=(3, 224, 224)),
#96, 55, 55
conv.ConvLayer(filtershape=(256, 96, 5, 5),
stride_length=2,
pool=pool.PoolLayer(pad=1,
pool_size=(3, 3),
stride_length=2),
ishape=(96, 55, 55)),
#256, 26, 26
conv.ConvLayer(padding=1,
filtershape=(384, 256, 3, 3),
stride_length=1,
ishape=(256, 26, 26)),
#384, 13, 13
conv.ConvLayer(padding=1,
filtershape=(384, 384, 3, 3),
stride_length=1,
ishape=(384, 13, 13)),
#384, 13, 13
conv.ConvLayer(padding=1,
filtershape=(256, 384, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(3, 3),
stride_length=2),
ishape=(384, 13, 13)),
#do use he initialization here
fcnetwork.FCLayer(arch=[9216, 4096, 4096, 5])
]
zf5 = model.Model(learning_rate=None,
dataset=None,
layerContainer=layerContainer)
# output = zf5.compute(input)
# print(f"first output = {output}")
zf5.saveLayers(
["conv1", "conv2", "conv3", "conv4", "conv5", "classifier"])
layerContainer = [
#3, 224, 224
conv.ConvLayer(load_path="conv1"),
#96, 55, 55
conv.ConvLayer(load_path="conv2"),
#256, 26, 26
conv.ConvLayer(load_path="conv3"),
#384, 13, 13
conv.ConvLayer(load_path="conv4"),
#384, 13, 13
conv.ConvLayer(load_path="conv5"),
#do use he initialization here
fcnetwork.FCLayer(arch=[9216, 4096, 4096, 5], load_path="classifier")
]
zf5 = model.Model(learning_rate=None,
dataset=None,
layerContainer=layerContainer)
# output = zf5.compute(input)
# res = zf5.learn()
# print(f"snd output = {res}")
try:
output = zf5.compute(input)
except:
print("error occured in zf5 model")
return "error"
# print(return_response(output))
return return_response(output)
# from this architecture:
# https://www.researchgate.net/figure/Architecture-of-ZF-model-An-3-channels-image-with-224224-is-as-the-input-It-is_fig5_318577329
layerContainer = [
conv.ConvLayer(padding=1,
filtershape=(96, 3, 7, 7),
stride_length=2,
pool=pool.PoolLayer(pad=1,
pool_size=(3, 3),
stride_length=2)),
conv.ConvLayer(filtershape=(256, 96, 5, 5),
stride_length=2,
pool=pool.PoolLayer(pad=1,
pool_size=(3, 3),
stride_length=2)),
conv.ConvLayer(padding=1, filtershape=(384, 256, 3, 3), stride_length=1),
conv.ConvLayer(padding=1, filtershape=(384, 384, 3, 3), stride_length=1),
conv.ConvLayer(padding=1,
filtershape=(256, 384, 3, 3),
stride_length=1,
pool=pool.PoolLayer(pool_size=(3, 3), stride_length=2)),
fcnetwork.FCLayer(arch=[9216, 4096, 5])
]
zf5 = model.Model(learning_rate=None,
dataset=None,
layerContainer=layerContainer)
output = zf5.compute(input)
print(output.shape)