[PYTHON] Introduction to Deep Learning ~ Function Approximation ~
Target person
The previous article is here
In this article, we will try to approximate the sin function using the code introduced in the [Introduction to Deep Learning Series](#Deep Learning Series).
By the way, I found a lot of bugs and fixes in this process, so I reflect them in each article.
environment
The code is running on a jupyter notebook. The introduction of jupyter notebook is introduced at here. Required packages
numpymatplotlibtqdm
It has become.
tqdm only shows the progress in an easy-to-understand manner, so if it's a hassle, just remove the relevant part from the test code and it will work.
The experimental code (test.ipynb) of this article is uploaded to github as it is.
table of contents
-[Code used](# Code used)
-[Test code](#test code)
-[Learning target setting](#Learning target setting)
-[Initial value setting](#Initial value setting)
-[Network construction](#Network construction)
-[Create animation base](#Create animation base)
-Learning
-[Animation and error transition display](#Animation and error transition display)
-[Experimental results](#Experimental results)
-[Port functionality to LayerManager](#Port functionality to layermanager)
Codes used
Please refer to [Past Articles](#Deep Learning Series) for a detailed production process.
`_layererror.py`
_layererror.py
class LayerManagerError(Exception):
"""Base class for user-defined errors in layer modules"""
pass
class AssignError(LayerManagerError):
def __init__(self, value=None):
if not value is None:
self.value = value
self.message = (str(value)
+ ": Assigning that value is prohibited.")
else:
self.value = None
self.message = "Assigning that value is prohibited."
def __str__(self):
return self.message
class UnmatchUnitError(LayerManagerError):
def __init__(self, prev, n):
self.prev = prev
self.n = n
self.message = "Unmatch units: {} and {}.".format(prev, n)
def __str__(self):
return self.message
class UndefinedLayerError(LayerManagerError):
def __init__(self, type_name):
self.type = type_name
self.message = str(type_name) + ": Undefined layer type."
def __str__(self):
return self.message
`baselayer.py`
baselayer.py
import numpy as np
class BaseLayer():
"""
All underlying layer classes
Describe the processing common to the intermediate layer and the output layer.
"""
def __init__(self, *, prev=1, n=1,
name="", wb_width=5e-2,
act="ReLU", opt="Adam",
act_dic={}, opt_dic={}, **kwds):
self.prev = prev #Number of outputs of the previous layer=Number of inputs to this layer
self.n = n #Number of outputs in this layer=Number of inputs to the next layer
self.name = name #The name of this layer
#Set weight and bias
self.w = wb_width*np.random.randn(prev, n)
self.b = wb_width*np.random.randn(n)
#Activation function(class)Get
self.act = get_act(act, **act_dic)
#Optimizer(class)Get
self.opt = get_opt(opt, **opt_dic)
def forward(self, x):
"""
Implementation of forward propagation
"""
#Remember your input
self.x = x.copy()
#Forward propagation
self.u = [email protected] + self.b
self.y = self.act.forward(self.u)
return self.y
def backward(self, grad):
"""
Implementation of backpropagation
"""
dact = grad*self.act.backward(self.u, self.y)
self.grad_w = self.x.T@dact
self.grad_b = np.sum(dact, axis=0)
self.grad_x = [email protected]
return self.grad_x
def update(self, **kwds):
"""
Implementation of parameter learning
"""
dw, db = self.opt.update(self.grad_w, self.grad_b, **kwds)
self.w += dw
self.b += db
`middlelayer.py`
middlelayer.py
import numpy as np
class MiddleLayer(BaseLayer):
"""
Middle class
The input layer is also treated as one of the intermediate layers in mounting.
"""
pass
`outputlayer.py`
outputlayer.py
import numpy as np
class OutputLayer(BaseLayer):
"""
Output layer class
"""
def __init__(self, *, err_func="Square", **kwds):
#Loss function(class)Get
self.errfunc = get_err(err_func)
super().__init__(**kwds)
def backward(self, t):
"""
Implementation of backpropagation
"""
#When the activation function of the output layer is the softmax function and the loss function is the cross entropy error
#Separate cases of error propagation
if isinstance(self.act, type(get_act("softmax"))) \
and isinstance(self.errfunc, type(get_err("Cross"))):
dact = self.y - t
self.grad_w = self.x.T@dact
self.grad_b = np.sum(dact, axis=0)
self.grad_x = [email protected]
return self.grad_x
elif isinstance(self.act, type(get_act("sigmoid"))) \
and isinstance(self.errfunc, type(get_err("Binary"))):
dact = self.y - t
self.grad_w = self.x.T@dact
self.grad_b = np.sum(dact, axis=0)
self.grad_x = [email protected]
return self.grad_x
else:
grad = self.errfunc.backward(self.y, t)
return super().backward(grad)
def get_error(self, t):
self.error = self.errfunc.forward(self.y, t)
return self.errfunc.total_error()
`layermanager.py`
layermanager.py
import numpy as np
class _TypeManager():
"""
Manager class for layer types
"""
N_TYPE = 2 #Number of layer types
MIDDLE = 0 #Middle layer numbering
OUTPUT = 1 #Output layer numbering
class LayerManager(_TypeManager):
"""
Manager class for managing layers
"""
def __init__(self):
self.__layer_list = [] #List of layers
self.__name_list = [] #Name list for each layer
self.__ntype = np.zeros(self.N_TYPE, dtype=int) #Number of layers by type
def __repr__(self):
layerRepr= "layer_list: " + repr(self.__layer_list)
nameRepr = "name_list: " + repr(self.__name_list)
ntypeRepr = "ntype: " + repr(self.__ntype)
return (layerRepr + "\n"
+ nameRepr + "\n"
+ ntypeRepr)
def __str__(self):
layerStr = "layer_list: " + str(self.__layer_list)
nameStr = "name_list: " + str(self.__name_list)
ntypeStr = "ntype: " + str(self.__ntype)
return (layerStr + "\n"
+ nameStr + "\n"
+ ntypeStr)
def __len__(self):
"""
Python built-in functions`len`Describes the operation when called from.
Returns the sum of the number of layers by type.
"""
return int(np.sum(self.__ntype))
def __getitem__(self, key):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
x = lm[3].~~
Because it is called when an element of a list or array is accessed, like
Describe the operation at that time.
slice and str,Only allow access via int.
"""
if isinstance(key, slice):
#If the key is a slice, refer to the list of layers with slice.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
return self.__layer_list[key]
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Returns the elements of the list of applicable layers.
if key in self.__name_list:
index = self.__name_list.index(key)
return self.__layer_list[index]
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, returns the corresponding element in the list of layers.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
return self.__layer_list[key]
else:
raise KeyError(key, ": Undefined such key type.")
def __setitem__(self, key, value):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
lm[1] = x
Because it is called when an element of a list or array is accessed, like
Describe the operation at that time.
Only overwriting elements is allowed, and adding new elements is prohibited.
"""
value_type = ""
if isinstance(value, list):
#Specified on the right side'value'But'list'If
#All elements'BaseLayer'Error if class or not inheriting it.
if not np.all(
np.where(isinstance(value, BaseLayer), True, False)):
self.AssignError()
value_type = "list"
elif isinstance(value, BaseLayer):
#Specified on the right side'value'But'BaseLayer'Is it a class?
#Error if it is not inherited.
self.AssignError(type(value))
if value_type == "":
value_type = "BaseLayer"
if isinstance(key, slice):
#If key is a slice, overwrite the element in the list of layers.
#However'value_type'But'list'Otherwise an error.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
if value_type != "list":
self.AssignError(value_type)
self.__layer_list[key] = value
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Overwrite the element in the list of applicable layers.
#However'value_type'But'BaseLayer'Otherwise an error.
if value_type != "BaseLayer":
raise AssignError(value_type)
if key in self.__name_list:
index = self.__name_list.index(key)
self.__layer_list[index] = value
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, overwrite the corresponding element in the layer list.
#However'value_type'But'BaseLayer'Otherwise an error.
#Also, an abnormal value(Index out of range etc.)When is entered
#Python gives me an error.
if value_type != "BaseLayer":
raise AssignError(value_type)
self.__layer_list[key] = value
else:
raise KeyError(key, ": Undefined such key type.")
def __delitem__(self, key):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
del lm[2]
Because it is called when the element of the list or array is accessed by the del statement like
Describe the operation at that time.
If the specified element exists, it will be deleted and renamed.
"""
if isinstance(key, slice):
#If the key is a slice, delete the specified element as it is
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
del self.__layer_list[slice]
del self.__name_list[slice]
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Delete the relevant element.
if key in self.__name_list:
del self.__layer_list[index]
del self.__name_list[index]
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, delete the corresponding element in the layer list.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
del self.__layer_list[key]
else:
raise KeyError(key, ": Undefined such key type.")
#Rename
self._rename()
def _rename(self):
"""
When the name list naming violates the rules due to list operations
Rename the naming list and each layer to meet the rules again.
The naming rule is[Layer type][What number]will do.
If the layer type is Middle Layer, Middle
Output for Output Layer
It is abbreviated as.
The number is counted by type.
Also, here again__Counts ntypes.
"""
#Initialize the number of layers by type
self.__ntype = np.zeros(self.N_TYPE)
#Recount and rename each layer
for i in range(len(self)):
if "Middle" in self.__name_list[i]:
self.__ntype[self.MIDDLE] += 1
self.__name_list[i] = "Middle{}".format(
self.__ntype[self.MIDDLE])
self.__layer_list[i].name = "Middle{}".format(
self.__ntype[self.MIDDLE])
elif "Output" in self.__name_list[i]:
self.__ntype[self.OUTPUT] += 1
self.__name_list[i] = "Output{}".format(
self.__ntype[self.OUTPUT])
self.__layer_list[i].name = "Output{}".format(
self.__ntype[self.OUTPUT])
else:
raise UndefinedLayerType(self.__name_list[i])
def append(self, *, name="Middle", **kwds):
"""
Implementation of the familiar append method, which is a method for adding elements to a list.
"""
if "prev" in kwds:
# 'prev'Is included in the keyword
#This means that the number of elements in the previous layer is specified.
#Basically it is supposed to be the time to insert the first layer, so
#Other than that, it is basically determined automatically and is not specified.
if len(self) != 0:
if kwds["prev"] != self.__layer_list[-1].n:
#Error if it does not match the number of units at the end.
raise UnmatchUnitError(self.__layer_list[-1].n,
kwds["prev"])
else:
if len(self) == 0:
#The first layer must always specify the number of input units.
raise UnmatchUnitError("Input units", "Unspecified")
else:
#The number of units in the last layer'kwds'Add to
kwds["prev"] = self.__layer_list[-1].n
#Read the layer type and change the name according to the naming rule
if name == "Middle" or name == "mid" or name == "m":
name = "Middle"
elif name == "Output" or name == "out" or name == "o":
name = "Output"
else:
raise UndefinedLayerError(name)
#Add a layer.
if name == "Middle":
#Increment the layer by type
self.__ntype[self.MIDDLE] += 1
#Add to name
name += str(self.__ntype[self.MIDDLE])
#Add to name list
self.__name_list.append(name)
#Finally, create a layer and add it to the list.
self.__layer_list.append(
MiddleLayer(name=name, **kwds))
elif name == "Output":
#This is also the same.
self.__ntype[self.OUTPUT] += 1
name += str(self.__ntype[self.OUTPUT])
self.__name_list.append(name)
self.__layer_list.append(
OutputLayer(name=name, **kwds))
#If you do not draw an else statement here, change the name according to the naming rule
#Already abnormal at the stage'name'Is omitted.
def extend(self, lm):
"""
Another layer manager already in the extend method'lm'Elements of
Add all.
"""
if not isinstance(lm, LayerManager):
# 'lm'Error if the instance of is not LayerManager.
raise TypeError(type(lm), ": Unexpected type.")
if len(self) != 0:
if self.__layer_list[-1].n != lm[0].prev:
#With the number of units in your last layer
# 'lm'Error if the number of inputs in the first layer of is not the same.
raise UnmatchUnitError(self.__layer_list[-1].n,
lm[0].prev)
#Each'extend'Add by method
self.__layer_list.extend(lm.layer_list)
self.__name_list.extend(lm.name_list)
#Rename
self._rename()
def insert(self, prev_name, name="Middle", **kwds):
"""
In the insert method, specify the name of the previous layer and combine it with that layer.
Add an element.
"""
# 'prev_name'Error if does not exist.
if not prev_name in self.__name_list:
raise KeyError(prev_name, ": No such key.")
# 'prev'Is included in the keyword
# 'prev_name'Error if it does not match the number of units in the layer specified in.
if "prev" in kwds:
if kwds["prev"] \
!= self.__layer_list[self.index(prev_name)].n:
raise UnmatchUnitError(
kwds["prev"],
self.__layer_list[self.index(prev_name)].n)
# 'n'Is included in the keyword
if "n" in kwds:
# 'prev_name'If is not the last
if prev_name != self.__name_list[-1]:
#Error if it does not match the number of units in the next layer.
if kwds["n"] != self.__layer_list[
self.index(prev_name)+1].prev:
raise UnmatchUnitError(
kwds["n"],
self.__layer_list[self.index(prev_name)].prev)
#If there are no elements yet'append'Give an error to use the method.
if len(self) == 0:
raise RuntimeError(
"You have to use 'append' method instead.")
#Get index of insertion location
index = self.index(prev_name) + 1
#Read the layer type and change the name according to the naming rule
if name == "Middle" or name == "mid" or name == "m":
name = "Middle"
elif name == "Output" or name == "out" or name == "o":
name = "Output"
else:
raise UndefinedLayerError(name)
#Insert element
#At this time,'name'Does not yet follow the naming rules,
#I'll rename it later so don't worry about it.
if "Middle" in name:
self.__layer_list.insert(index,
MiddleLayer(name=name, **kwds))
self.__name_list.insert(index, name)
elif "Output" in name:
self.__layer_list.insert(index,
OutputLayer(name=name, **kwds))
self.__name_list.insert(index, name)
#Rename
self._rename()
def extend_insert(self, prev_name, lm):
"""
This is the original function.
It behaves like a combination of extend and insert methods.
Simply put, it's like inserting another layer manager.
"""
if not isinstance(lm, LayerManager):
# 'lm'Error if the instance of is not LayerManager.
raise TypeError(type(lm), ": Unexpected type.")
# 'prev_name'Error if does not exist.
if not prev_name in self.__name_list:
raise KeyError(prev_name, ": No such key.")
#The number of units of the layers before and after the specified location and the first and last layers of lm
#If they do not match, an error occurs.
if len(self) != 0:
if self.__layer_list[self.index(prev_name)].n \
!= lm.layer_list[0].prev:
#With the number of units in your designated location'lm'The first number of units in
#If they do not match, an error occurs.
raise UnmatchUnitError(
self.__layer_list[self.index(prev_name)].n,
lm.layer_list[0].prev)
if prev_name != self.__name_list[-1]:
# 'prev_name'Is not my last layer
if lm.layer_list[-1].n \
!= self.__layer_list[self.index(prev_name)+1].prev:
# 'lm'The number of units at the end of and the layer next to your designated location
# 'prev'Error if it does not match the number of units.
raise UnmatchUnitError(
lm.layer_list[-1].n,
self.__layer_list[self.index(prev_name)+1].prev)
else:
#If you don't have any elements'extend'I get an error to use the method.
raise RuntimeError(
"You have to use 'extend' method instead.")
#Get index of insertion location
index = self.index(prev_name) + 1
#Elements after the insertion location'buf'After evacuating to, remove it once and
#Add an element using the extend method
layer_buf = self.__layer_list[index:]
name_buf = self.__name_list[index:]
del self.__layer_list[index:]
del self.__name_list[index:]
self.extend(lm)
#Add the element that was evacuated
self.__layer_list.extend(layer_buf)
self.__name_list.extend(name_buf)
#Rename
self._rename()
def remove(self, key):
"""
The remove method removes the element with the specified name.
It is also allowed to be specified by index.
"""
#Already implemented'del'The sentence is OK.
del self[key]
def index(self, target):
return self.__name_list.index(target)
def name(self, indices):
return self.__name_list[indices]
@property
def layer_list(self):
return self.__layer_list
@property
def name_list(self):
return self.__name_list
@property
def ntype(self):
return self.__ntype
`errors.py`
errors.py
import numpy as np
class Error():
def __init__(self, *args,**kwds):
self.error = 0
def forward(self, *args,**kwds):
pass
def backward(self, *args,**kwds):
pass
def total_error(self, *args,**kwds):
return np.sum(self.error)/self.error.size
class SquareError(Error):
def forward(self, y, t, *args,**kwds):
self.error = 0.5 * (y - t)**2
return self.error
def backward(self, y, t, *args,**kwds):
return y - t
class BinaryCrossEntropy(Error):
def forward(self, y, t, *args,**kwds):
self.error = - t*np.log(y) - (1 - t)*np.log(1 - y)
return self.error
def backward(self, y, t, *args,**kwds):
return (y - t) / (y*(1 - y))
class CrossEntropy(Error):
def forward(self, y, t, *args,**kwds):
self.error = - t*np.log(y)
return self.error
def backward(self, y, t, *args,**kwds):
return - t/y
`get_err.py`
get_err.py
_err_dic = {"Square": SquareError,
"Binary": BinaryCrossEntropy,
"Cross": CrossEntropy,
}
def get_err(name, *args,**kwds):
if name in _err_dic.keys():
errfunc = _err_dic[name](*args,**kwds)
else:
raise ValueError(name + ": Unknown error function")
return errfunc
`activations.py`
activations.py
import numpy as np
class Activator():
def __init__(self, *args,**kwds):
pass
def forward(self, *args,**kwds):
raise Exception("Not Implemented")
def backward(self, *args,**kwds):
raise Exception("Not Implemented")
def update(self, *args,**kwds):
pass
class step(Activator):
def forward(self, x, *args,**kwds):
return np.where(x > 0, 1, 0)
def backward(self, x, *args,**kwds):
return np.zeros_like(x)
class identity(Activator):
def forward(self, x, *args,**kwds):
return x
def backward(self, x, *args,**kwds):
return np.ones_like(x)
class bentIdentity(Activator):
def forward(self, x, *args,**kwds):
return 0.5*(np.sqrt(x**2 + 1) - 1) + x
def backward(self, x, *args,**kwds):
return 0.5*x/np.sqrt(x**2 + 1) + 1
class hardShrink(Activator):
def __init__(self, lambda_=0.5, *args,**kwds):
self.lambda_ = lambda_
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.where((-self.lambda_ <= x) & (x <= self.lambda_),
0, x)
def backward(self, x, *args,**kwds):
return np.where((-self.lambda_ <= x) & (x <= self.lambda_),
0, 1)
class softShrink(Activator):
def __init__(self, lambda_=0.5, *args,**kwds):
self.lambda_ = lambda_
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.where(x < -self.lambda_, x + self.lambda_,
np.where(x > self.lambda_, x - self.lambda_, 0))
def backward(self, x, *args,**kwds):
return np.where((-self.lambda_ <= x) & (x <= self.lambda_),
0, 1)
class threshold(Activator):
def __init__(self, threshold, value, *args,**kwds):
self.threshold = threshold
self.value = value
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.where(x > self.threshold, x, self.value)
def backward(self, x, *args,**kwds):
return np.where(x > self.threshold, 1, 0)
class sigmoid(Activator):
def forward(self, x, *args,**kwds):
return 1/(1 + np.exp(-x))
def backward(self, x, y, *args,**kwds):
return y*(1 - y)
class hardSigmoid(Activator):
def forward(self, x, *args,**kwds):
return np.clip(0.2*x + 0.5, 0, 1)
def backward(self, x, *args,**kwds):
return np.where((x > 2.5) | (x < -2.5), 0, 0.2)
class logSigmoid(Activator):
def forward(self, x, *args,**kwds):
return -np.log(1 + np.exp(-x))
def backward(self, x, *args,**kwds):
return 1/(1 + np.exp(x))
class act_tanh(Activator):
def forward(self, x, *args,**kwds):
return np.tanh(x)
def backward(self, x, *args,**kwds):
return 1 - np.tanh(x)**2
class hardtanh(Activator):
def forward(self, x, *args,**kwds):
return np.clip(x, -1, 1)
def backward(self, x, *args,**kwds):
return np.where((-1 <= x) & (x <= 1), 1, 0)
class tanhShrink(Activator):
def forward(self, x, *args,**kwds):
return x - np.tanh(x)
def backward(self, x, *args,**kwds):
return np.tanh(x)**2
class ReLU(Activator):
def forward(self, x, *args,**kwds):
return np.maximum(0, x)
def backward(self, x, *args,**kwds):
return np.where(x > 0, 1, 0)
class ReLU6(Activator):
def forward(self, x, *args,**kwds):
return np.clip(x, 0, 6)
def backward(self, x, *args,**kwds):
return np.where((0 < x) & (x < 6), 1, 0)
class leakyReLU(Activator):
def __init__(self, alpha=1e-2, *args,**kwds):
self.alpha = alpha
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.maximum(self.alpha * x, x)
def backward(self, x, *args,**kwds):
return np.where(x < 0, self.alpha, 1)
class ELU(Activator):
def __init__(self, alpha=1., *args,**kwds):
self.alpha = alpha
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.where(x >= 0, x, self.alpha*(np.exp(x) - 1))
def backward(self, x, *args,**kwds):
return np.where(x >= 0, 1, self.alpha*np.exp(x))
class SELU(Activator):
def __init__(self, lambda_=1.0507, alpha=1.67326, *args,**kwds):
self.lambda_ = lambda_
self.alpha = alpha
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.where(x >= 0,
self.lambda_*x,
self.lambda_*self.alpha*(np.exp(x) - 1))
def backward(self, x, *args,**kwds):
return np.where(x >= 0,
self.lambda_,
self.lambda_*self.alpha*np.exp(x))
class CELU(Activator):
def __init__(self, alpha=1., *args,**kwds):
self.alpha = alpha
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return np.where(x >= 0,
x,
self.alpha*(np.exp(x/self.alpha) - 1))
def backward(self, x, *args,**kwds):
return np.where(x >= 0, 1, np.exp(x/self.alpha))
class softmax(Activator):
def forward(self, x, *args,**kwds):
return np.exp(x)/np.sum(np.exp(x))
def backward(self, x, *args,**kwds):
return np.exp(x)*(np.sum(np.exp(x))
- np.exp(x))/np.sum(np.exp(x))**2
class softmin(Activator):
def forward(self, x, *args,**kwds):
return np.exp(-x)/np.sum(np.exp(-x))
def backward(self, x, *args,**kwds):
return -(np.exp(x)*(np.sum(np.exp(-x)) - np.exp(x))
/np.sum(np.exp(-x))**2)
class logSoftmax(Activator):
def forward(self, x, *args,**kwds):
return np.log(np.exp(x)/np.sum(np.exp(x)))
def backward(self, x, *args,**kwds):
y = np.sum(np.exp(x))
return (y - np.exp(x))/y
class softplus(Activator):
def forward(self, x, *args,**kwds):
return np.logaddexp(x, 0)
def backward(self, x, *args,**kwds):
return 1/(1 + np.exp(-x))
class softsign(Activator):
def forward(self, x, *args,**kwds):
return x/(1 + np.abs(x))
def backward(self, x, *args,**kwds):
return 1/(1 + np.abs(x)) ** 2
class Swish(Activator):
def __init__(self, beta=1, *args,**kwds):
self.beta = beta
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
return x/(1 + np.exp(-self.beta*x))
def backward(self, x, y, *args,**kwds):
return self.beta*y + (1 - self.beta*y)/(1 + np.exp(-self.beta*x))
def d2y(self, x, *args,**kwds):
return (-0.25*self.beta*(self.beta*x*np.tanh(0.5*self.beta*x) - 2)
*(1 - np.tanh(0.5*self.beta*x)**2))
class Mish(Activator):
def forward(self, x, *args,**kwds):
return x*np.tanh(np.logaddexp(x, 0))
def backward(self, x, *args,**kwds):
omega = (4*(x + 1) + 4*np.exp(2*x)
+ np.exp(3*x) + (4*x + 6)*np.exp(x))
delta = 2*np.exp(x) + np.exp(2*x) + 2
return np.exp(x)*omega/delta**2
def d2y(self, x, *args,**kwds):
omega = (2*(x + 2)
+ np.exp(x)*(np.exp(x)*(-2*np.exp(x)*(x - 1) - 3*x + 6)
+ 2*(x + 4)))
delta = np.exp(x)*(np.exp(x) + 2) + 2
return 4*np.exp(x)*omega/delta**3
class tanhExp(Activator):
def forward(self, x, *args,**kwds):
return x*np.tanh(np.exp(x))
def backward(self, x, *args,**kwds):
tanh_exp = np.tanh(np.exp(x))
return tanh_exp - x*np.exp(x)*(tanh_exp**2 - 1)
def d2y(self, x, *args,**kwds):
tanh_exp = np.tanh(np.exp(x))
return (np.exp(x)*(-x + 2*np.exp(x)*x*tanh_exp - 2)
*(tanh_exp**2 - 1))
class maxout(Activator):
def __init__(self, n_prev, n, k, wb_width=5e-2, *args,**kwds):
self.n_prev = n_prev
self.n = n
self.k = k
self.w = wb_width*np.random.rand((n_prev, n*k))
self.b = wb_width*np.random.rand(n*k)
super().__init__(*args,**kwds)
def forward(self, x, *args,**kwds):
self.x = x.copy()
self.z = np.dot(self.w.T, x) + self.b
self.z = self.z.reshape(self.n, self.k)
self.y = np.max(self.z, axis=1)
return self.y
def backward(self, g, *args,**kwds):
self.dw = np.sum(np.dot(self.w, self.x))
`get_act.py`
get_act.py
_act_dic = {"step": step,
"identity": identity,
"bent-identity": bentIdentity,
"hard-shrink": hardShrink,
"soft-shrink": softShrink,
"threshold": threshold,
"sigmoid": sigmoid,
"hard-sigmoid": hardSigmoid,
"log-sigmoid": logSigmoid,
"tanh": act_tanh,
"tanh-shrink": tanhShrink,
"hard-tanh":hardtanh,
"ReLU": ReLU,
"ReLU6": ReLU6,
"leaky-ReLU": leakyReLU,
"ELU": ELU,
"SELU": SELU,
"CELU": CELU,
"softmax": softmax,
"softmin": softmin,
"log-softmax": logSoftmax,
"softplus": softplus,
"softsign": softsign,
"Swish": Swish,
"Mish": Mish,
"tanhExp": tanhExp,
}
def get_act(name, *args,**kwds):
if name in _act_dic.keys():
activator = _act_dic[name](*args,**kwds)
else:
raise ValueError(name + ": Unknown activator")
return activator
`optimizers.py`
optimizers.py
import numpy as np
class Optimizer():
"""
A superclass inherited by the optimization method.
"""
def __init__(self, *args,**kwds):
pass
def update(self, *args,**kwds):
pass
class SGD(Optimizer):
def __init__(self, eta=1e-2, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
def update(self, grad_w, grad_b, *args,**kwds):
dw = -self.eta*grad_w
db = -self.eta*grad_b
return dw, db
class MSGD(Optimizer):
def __init__(self, eta=1e-2, mu=0.9, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
self.mu = mu
#Hold the value of the previous step
self.dw = 0
self.db = 0
def update(self, grad_w, grad_b, *args,**kwds):
dw = self.mu*self.dw - (1-self.mu)*self.eta*grad_w
db = self.mu*self.db - (1-self.mu)*self.eta*grad_b
#Assigning in the view instead of copying is because these values may be used
#This is because it will not be changed.
self.dw = dw
self.db = db
return dw, db
class NAG(Optimizer):
def __init__(self, eta=1e-2, mu=0.9, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
self.mu = mu
#Holds the value of the previous step
self.dw = 0
self.db = 0
def update(self, grad_w, grad_b, w=0, b=0, dfw=None, dfb=None,
nargs=2, *args,**kwds):
if nargs == 1:
grad_w = dfw(w + self.mu*self.dw)
grad_b = 0
elif nargs == 2:
grad_w = dfw(w + self.mu*self.dw, b + self.mu*self.db)
grad_b = dfb(w + self.mu*self.dw, b + self.mu*self.db)
dw = self.mu*self.dw - (1-self.mu)*self.eta*grad_w
db = self.mu*self.db - (1-self.mu)*self.eta*grad_b
#Assigning in the view instead of copying is because these values may be used
#This is because it will not be changed.
self.dw = dw
self.db = db
return dw, db
class AdaGrad(Optimizer):
def __init__(self, eta=1e-3, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
#Hold the value of the previous step
self.gw = 0
self.gb = 0
def update(self, grad_w, grad_b, *args,**kwds):
self.gw += grad_w*grad_w
self.gb += grad_b*grad_b
dw = -self.eta*grad_w/np.sqrt(self.gw)
db = -self.eta*grad_b/np.sqrt(self.gb)
return dw, db
class RMSprop(Optimizer):
def __init__(self, eta=1e-2, rho=0.99, eps=1e-8, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
self.rho = rho
self.eps = eps
#Hold the value of the previous step
self.vw = 0
self.vb = 0
def update(self, grad_w, grad_b, *args,**kwds):
self.vw += (1-self.rho)*(grad_w**2 - self.vw)
self.vb += (1-self.rho)*(grad_b**2 - self.vb)
dw = -self.eta*grad_w/np.sqrt(self.vw+self.eps)
db = -self.eta*grad_b/np.sqrt(self.vb+self.eps)
return dw, db
class AdaDelta(Optimizer):
def __init__(self, rho=0.95, eps=1e-6, *args,**kwds):
super().__init__(*args,**kwds)
self.rho = rho
self.eps = eps
#Hold the value of the previous step
self.vw = 0
self.vb = 0
self.uw = 0
self.ub = 0
def update(self, grad_w, grad_b, *args,**kwds):
self.vw += (1-self.rho)*(grad_w**2 - self.vw)
self.vb += (1-self.rho)*(grad_b**2 - self.vb)
dw = -grad_w*np.sqrt(self.uw+self.eps)/np.sqrt(self.vw+self.eps)
db = -grad_b*np.sqrt(self.ub+self.eps)/np.sqrt(self.vb+self.eps)
self.uw += (1-self.rho)*(dw**2 - self.uw)
self.ub += (1-self.rho)*(db**2 - self.ub)
return dw, db
class Adam(Optimizer):
def __init__(self, alpha=1e-3, beta1=0.9, beta2=0.999, eps=1e-8,
*args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.beta1 = beta1
self.beta2 = beta2
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
self.mw += (1-self.beta1)*(grad_w - self.mw)
self.mb += (1-self.beta1)*(grad_b - self.mb)
self.vw += (1-self.beta2)*(grad_w**2 - self.vw)
self.vb += (1-self.beta2)*(grad_b**2 - self.vb)
alpha_t = self.alpha*np.sqrt(1-self.beta2**t)/(1-self.beta1**t)
dw = -alpha_t*self.mw/(np.sqrt(self.vw+self.eps))
db = -alpha_t*self.mb/(np.sqrt(self.vb+self.eps))
return dw, db
class RMSpropGraves(Optimizer):
def __init__(self, eta=1e-4, rho=0.95, eps=1e-4, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
self.rho = rho
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
def update(self,grad_w, grad_b, *args,**kwds):
self.mw += (1-self.rho)*(grad_w - self.mw)
self.mb += (1-self.rho)*(grad_b - self.mb)
self.vw += (1-self.rho)*(grad_w**2 - self.vw)
self.vb += (1-self.rho)*(grad_b**2 - self.vb)
dw = -self.eta*grad_w/np.sqrt(self.vw - self.mw**2 + self.eps)
db = -self.eta*grad_b/np.sqrt(self.vb - self.mb**2 + self.eps)
return dw, db
class SMORMS3(Optimizer):
def __init__(self, eta=1e-3, eps=1e-8, *args,**kwds):
super().__init__(*args,**kwds)
self.eta = eta
self.eps = eps
#Hold the value of the previous step
self.zetaw = 0
self.zetab = 0
self.sw = 1
self.sb = 1
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
def update(self, grad_w, grad_b, *args,**kwds):
rhow = 1/(1+self.sw)
rhob = 1/(1+self.sb)
self.mw += (1-rhow)*(grad_w - self.mw)
self.mb += (1-rhob)*(grad_b - self.mb)
self.vw += (1-rhow)*(grad_w**2 - self.vw)
self.vb += (1-rhob)*(grad_b**2 - self.vb)
self.zetaw = self.mw**2 / (self.vw + self.eps)
self.zetaw = self.mb**2 / (self.vb + self.eps)
dw = -grad_w*(np.minimum(self.eta, self.zetaw)
/np.sqrt(self.vw + self.eps))
db = -grad_b*(np.minimum(self.eta, self.zetab)
/np.sqrt(self.vb + self.eps))
self.sw = 1 + (1 - self.zetaw)*self.sw
self.sb = 1 + (1 - self.zetab)*self.sb
return dw, db
class AdaMax(Optimizer):
def __init__(self, alpha=2e-3, beta1=0.9, beta2=0.999,
*args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.beta1 = beta1
self.beta2 = beta2
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.uw = 0
self.ub = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
self.mw += (1-self.beta1)*(grad_w - self.mw)
self.mb += (1-self.beta1)*(grad_b - self.mb)
self.uw = np.maximum(self.beta2*self.uw, np.abs(grad_w))
self.ub = np.maximum(self.beta2*self.ub, np.abs(grad_b))
alpha_t = self.alpha/(1 - self.beta1**t)
dw = -alpha_t*self.mw/self.uw
db = -alpha_t*self.mb/self.ub
return dw, db
class Nadam(Optimizer):
def __init__(self, alpha=2e-3, mu=0.975, nu=0.999, eps=1e-8,
*args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.mu = mu
self.nu = nu
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
self.mw += (1-self.mu)*(grad_w - self.mw)
self.mb += (1-self.mu)*(grad_b - self.mb)
self.vw += (1-self.nu)*(grad_w**2 - self.vw)
self.vb += (1-self.nu)*(grad_b**2 - self.vb)
mhatw = (self.mu*self.mw/(1-self.mu**(t+1))
+ (1-self.mu)*grad_w/(1-self.mu**t))
mhatb = (self.mu*self.mb/(1-self.mu**(t+1))
+ (1-self.mu)*grad_b/(1-self.mu**t))
vhatw = self.nu*self.vw/(1-self.nu**t)
vhatb = self.nu*self.vb/(1-self.nu**t)
dw = -self.alpha*mhatw/np.sqrt(vhatw + self.eps)
db = -self.alpha*mhatb/np.sqrt(vhatb + self.eps)
return dw, db
class Eve(Optimizer):
def __init__(self, alpha=1e-3, beta1=0.9, beta2=0.999, beta3=0.999,
c=10, eps=1e-8, fstar=0, *args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.beta1 = beta1
self.beta2 = beta2
self.beta3 = beta3
self.c = c
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
self.f = 0
self.fstar = fstar
self.dtilde_w = 0
self.dtilde_b = 0
def update(self, grad_w, grad_b, t=1, f=1, *args,**kwds):
self.mw += (1-self.beta1)*(grad_w - self.mw)
self.mb += (1-self.beta1)*(grad_b - self.mb)
self.vw += (1-self.beta2)*(grad_w**2 - self.vw)
self.vb += (1-self.beta2)*(grad_b**2 - self.vb)
mhatw = self.mw/(1 - self.beta1**t)
mhatb = self.mb/(1 - self.beta1**t)
vhatw = self.vw/(1 - self.beta2**t)
vhatb = self.vb/(1 - self.beta2**t)
if t > 1:
d_w = (np.abs(f-self.fstar)
/(np.minimum(f, self.f) - self.fstar))
d_b = (np.abs(f-self.fstar)
/(np.minimum(f, self.f) - self.fstar))
dhat_w = np.clip(d_w, 1/self.c, self.c)
dhat_b = np.clip(d_b, 1/self.c, self.c)
self.dtilde_w += (1 - self.beta3)*(dhat_w - self.dtilde_w)
self.dtilde_b += (1 - self.beta3)*(dhat_b - self.dtilde_b)
else:
self.dtilde_w = 1
self.dtilde_b = 1
self.f = f
dw = -(self.alpha*mhatw
/(self.dtilde_w*(np.sqrt(vhatw) + self.eps)))
db = -(self.alpha*mhatb
/(self.dtilde_b*(np.sqrt(vhatb) + self.eps)))
return dw, db
class SantaE(Optimizer):
def __init__(self, eta=1e-2, sigma=0.95, lambda_=1e-8,
anne_func=lambda t, n: t**n, anne_rate=0.5,
burnin=100, C=5, N=16,
*args,**kwds):
"""
Args:
eta: Learning rate
sigma: Maybe in other cases;
'rho' in RMSprop, AdaDelta, RMSpropGraves.
'rhow' or 'rhob' in SMORMS3.
'beta2' in Adam, Eve.
'nu' in Nadam.
To use calculation 'v'.
lambda_: Named 'eps'(ε) in other cases.
anne_func: Annealing function.
To use calculation 'beta' at each timestep.
Default is 'timestep'**'annealing rate'.
The calculated value should be towards infinity
as 't' increases.
anne_rate: Annealing rate.
To use calculation 'beta' at each timestep.
The second Argument of 'anne_func'.
burnin: Swith exploration and refinement.
This should be specified by users.
C: To calculate first 'alpha'.
N: Number of minibatch.
"""
super().__init__(*args,**kwds)
self.eta = eta
self.sigma = sigma
self.lambda_ = lambda_
self.anne_func = anne_func
self.anne_rate = anne_rate
self.burnin = burnin
self.N = N
# Keep one step before and Initialize.
self.alpha_w = np.sqrt(eta)*C
self.alpha_b = np.sqrt(eta)*C
self.vw = 0
self.vb = 0
self.gw = 0
self.gb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
try:
shape_w = grad_w.shape
except:
shape_w = (1, )
try:
shape_b = grad_b.shape
except:
shape_b = (1, )
if t == 1:
# Initialize uw, ub.
self.uw = np.sqrt(self.eta)*np.random.randn(*shape_w)
self.ub = np.sqrt(self.eta)*np.random.randn(*shape_b)
self.vw = (self.sigma*self.vw
+ grad_w*grad_w * (1 - self.sigma) / self.N**2)
self.vb = (self.sigma*self.vb
+ grad_b*grad_b * (1 - self.sigma) / self.N**2)
gw = 1/np.sqrt(self.lambda_ + np.sqrt(self.vw))
gb = 1/np.sqrt(self.lambda_ + np.sqrt(self.vb))
beta = self.anne_func(t, self.anne_rate)
if t < self.burnin:
# Exploration.
self.alpha_w += self.uw*self.uw - self.eta/beta
self.alpha_b += self.ub*self.ub - self.eta/beta
uw = (self.eta/beta * (1 - self.gw/gw)/self.uw
+ np.sqrt(2*self.eta/beta * self.gw)
* np.random.randn(*shape_w))
ub = (self.eta/beta * (1 - self.gb/gb)/self.ub
+ np.sqrt(2*self.eta/beta * self.gb)
* np.random.randn(*shape_b))
else:
# Refinement.
uw = 0
ub = 0
uw += (1 - self.alpha_w)*self.uw - self.eta*gw*grad_w
ub += (1 - self.alpha_b)*self.ub - self.eta*gb*grad_b
# Update values.
self.uw = uw
self.ub = ub
self.gw = gw
self.gb = gb
dw = gw*uw
db = gb*ub
return dw, db
class SantaSSS(Optimizer):
def __init__(self, eta=1e-2, sigma=0.95, lambda_=1e-8,
anne_func=lambda t, n: t**n, anne_rate=0.5,
burnin=100, C=5, N=16,
*args,**kwds):
"""
Args:
eta: Learning rate
sigma: Maybe in other cases;
'rho' in RMSprop, AdaDelta, RMSpropGraves.
'rhow' or 'rhob' in SMORMS3.
'beta2' in Adam, Eve.
'nu' in Nadam.
To use calculation 'v'.
lambda_: Named 'eps'(ε) in other cases.
anne_func: Annealing function.
To use calculation 'beta' at each timestep.
Default is 'timestep'**'annealing rate'.
The calculated value should be towards infinity
as 't' increases.
anne_rate: Annealing rate.
To use calculation 'beta' at each timestep.
The second Argument of 'anne_func'.
burnin: Swith exploration and refinement.
This should be specified by users.
C: To calculate first 'alpha'.
N: Number of minibatch.
"""
super().__init__(*args,**kwds)
self.eta = eta
self.sigma = sigma
self.lambda_ = lambda_
self.anne_func = anne_func
self.anne_rate = anne_rate
self.burnin = burnin
self.N = N
# Keep one step before and Initialize.
self.alpha_w = np.sqrt(eta)*C
self.alpha_b = np.sqrt(eta)*C
self.vw = 0
self.vb = 0
self.gw = 0
self.gb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
try:
shape_w = grad_w.shape
except:
shape_w = (1, )
try:
shape_b = grad_b.shape
except:
shape_b = (1, )
if t == 1:
# Initialize uw, ub.
self.uw = np.sqrt(self.eta)*np.random.randn(*shape_w)
self.ub = np.sqrt(self.eta)*np.random.randn(*shape_b)
self.vw = (self.sigma*self.vw
+ grad_w*grad_w * (1 - self.sigma) / self.N**2)
self.vb = (self.sigma*self.vb
+ grad_b*grad_b * (1 - self.sigma) / self.N**2)
gw = 1/np.sqrt(self.lambda_ + np.sqrt(self.vw))
gb = 1/np.sqrt(self.lambda_ + np.sqrt(self.vb))
dw = 0.5*gw*self.uw
db = 0.5*gb*self.ub
beta = self.anne_func(t, self.anne_rate)
if t < self.burnin:
# Exploration.
self.alpha_w += (self.uw*self.uw - self.eta/beta)*0.5
self.alpha_b += (self.ub*self.ub - self.eta/beta)*0.5
uw = np.exp(-0.5*self.alpha_w)*self.uw
ub = np.exp(-0.5*self.alpha_b)*self.ub
uw += (-gw*grad_w*self.eta
+ np.sqrt(2*self.gw*self.eta/beta)
* np.random.randn(*shape_w)
+ self.eta/beta*(1-self.gw/gw)/self.uw)
ub += (-gb*grad_b*self.eta
+ np.sqrt(2*self.gb*self.eta/beta)
* np.random.randn(*shape_b)
+ self.eta/beta*(1-self.gb/gb)/self.ub)
uw *= np.exp(-0.5*self.alpha_w)
ub *= np.exp(-0.5*self.alpha_b)
self.alpha_w += (uw*uw - self.eta/beta)*0.5
self.alpha_b += (ub*ub - self.eta/beta)*0.5
else:
# Refinement.
uw = np.exp(-0.5*self.alpha_w)*self.uw
ub = np.exp(-0.5*self.alpha_b)*self.ub
uw -= gw*grad_w*self.eta
ub -= gb*grad_b*self.eta
uw *= np.exp(-0.5*self.alpha_w)
ub *= np.exp(-0.5*self.alpha_b)
# Update values.
self.uw = uw
self.ub = ub
self.gw = gw
self.gb = gb
dw = gw*uw*0.5
db = gb*ub*0.5
return dw, db
class AMSGrad(Optimizer):
def __init__(self, alpha=1e-3, beta1=0.9, beta2=0.999, eps=1e-8,
*args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.beta1 = beta1
self.beta2 = beta2
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
self.vhatw = 0
self.vhatb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
self.mw += (1-self.beta1)*(grad_w - self.mw)
self.mb += (1-self.beta1)*(grad_b - self.mb)
self.vw += (1-self.beta2)*(grad_w**2 - self.vw)
self.vb += (1-self.beta2)*(grad_b**2 - self.vb)
self.vhatw = np.maximum(self.vhatw, self.vw)
self.vhatb = np.maximum(self.vhatb, self.vb)
alpha_t = self.alpha / np.sqrt(t)
dw = - alpha_t * self.mw/np.sqrt(self.vhatw + self.eps)
db = - alpha_t * self.mb/np.sqrt(self.vhatb + self.eps)
return dw, db
class AdaBound(Optimizer):
def __init__(self, alpha=1e-3, eta=1e-1, beta1=0.9, beta2=0.999,
eps=1e-8, *args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.eta = eta
self.beta1 = beta1
self.beta2 = beta2
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
self.mw += (1-self.beta1)*(grad_w - self.mw)
self.mb += (1-self.beta1)*(grad_b - self.mb)
self.vw += (1-self.beta2)*(grad_w**2 - self.vw)
self.vb += (1-self.beta2)*(grad_b**2 - self.vb)
etal = self.eta*(1 - 1/((1-self.beta2)*t + 1))
etau = self.eta*(1 + 1/((1-self.beta2)*t + self.eps))
etahatw_t = np.clip(self.alpha/np.sqrt(self.vw), etal, etau)
etahatb_t = np.clip(self.alpha/np.sqrt(self.vb), etal, etau)
etaw_t = etahatw_t/np.sqrt(t)
etab_t = etahatb_t/np.sqrt(t)
dw = - etaw_t*self.mw
db = - etab_t*self.mb
return dw, db
class AMSBound(Optimizer):
def __init__(self, alpha=1e-3, eta=1e-1, beta1=0.9, beta2=0.999,
eps=1e-8, *args,**kwds):
super().__init__(*args,**kwds)
self.alpha = alpha
self.eta = eta
self.beta1 = beta1
self.beta2 = beta2
self.eps = eps
#Hold the value of the previous step
self.mw = 0
self.mb = 0
self.vw = 0
self.vb = 0
self.vhatw = 0
self.vhatb = 0
def update(self, grad_w, grad_b, t=1, *args,**kwds):
self.mw += (1-self.beta1)*(grad_w - self.mw)
self.mb += (1-self.beta1)*(grad_b - self.mb)
self.vw += (1-self.beta2)*(grad_w**2 - self.vw)
self.vb += (1-self.beta2)*(grad_b**2 - self.vb)
self.vhatw = np.maximum(self.vhatw, self.vw)
self.vhatb = np.maximum(self.vhatb, self.vb)
etal = self.eta*(1 - 1/((1-self.beta2)*t + 1))
etau = self.eta*(1 + 1/((1-self.beta2)*t + self.eps))
etahatw_t = np.clip(self.alpha/np.sqrt(self.vhatw), etal, etau)
etahatb_t = np.clip(self.alpha/np.sqrt(self.vhatb), etal, etau)
etaw_t = etahatw_t/np.sqrt(t)
etab_t = etahatb_t/np.sqrt(t)
dw = - etaw_t*self.mw
db = - etab_t*self.mb
return dw, db
`get_opt.py`
get_opt.py
_opt_dic = {
"SGD": SGD,
"MSGD": MSGD,
"NAG": NAG,
"AdaGrad": AdaGrad,
"RMSprop": RMSprop,
"AdaDelta": AdaDelta,
"Adam": Adam,
"RMSpropGraves": RMSpropGraves,
"SMORMS3": SMORMS3,
"AdaMax": AdaMax,
"Nadam": Nadam,
"Eve": Eve,
"SantaE": SantaE,
"SantaSSS": SantaSSS,
"AMSGrad": AMSGrad,
"AdaBound": AdaBound,
"AMSBound": AMSBound,
}
def get_opt(name, *args,**kwds):
if name in _opt_dic.keys():
optimizer = _opt_dic[name](*args,**kwds)
else:
raise ValueError(name + ": Unknown optimizer")
return optimizer
Test code
Below is the experimental code.
`test.py`
test.py
%matplotlib nbagg
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tqdm
#Learning target setting
def split_test(target, train_indices):
return target[train_indices], target[~ train_indices]
x = np.arange(0, 4, 5e-2)
y = np.sin(x)
x_left = 1
x_right = 3
y_top = np.max(y) + 1
y_bottom = np.min(y) - 1
indices = (x_left <= x) & (x <= x_right)
x_train, x_test = split_test(x, indices)
y_train, y_test = split_test(y, indices)
#Initial setting
epoch = 10000
error_prev = 0
error = 0
error_list = []
threshold = 1e-8
n_batch = 4
n_train = x_train.size//n_batch
n_test = x_test.size
#Network construction
n_in = 1
n_out = 1
lm = LayerManager()
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
#Creating a foundation for animation plots
n_image = 100
interval = 50
images = []
fig, ax = plt.subplots(1)
fig.suptitle("fitting animation")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_xlim(np.min(x), np.max(x))
ax.set_ylim(y_bottom, y_top)
ax.grid()
ax.plot(x, y, color="r")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
#Start learning
rand_index = np.arange(x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if t % (epoch/n_image) == 1:
x_in = x.reshape(-1, 1)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
im, = ax.plot(x, ll.y, color="b")
images.append([im])
#Error calculation
x_in = x_test.reshape(n_test, n_in)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(y_test.reshape(n_test, n_out))
error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
#print("t", t)
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = x_train[rand].reshape(-1, n_in)
#print("x_in", x_in)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
y_in = y_train[rand].reshape(-1, n_out)
#print("y_in", y_in)
for ll in lm.layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in lm.layer_list:
ll.update()
#Creating fitting animation
anim = animation.ArtistAnimation(fig, images, interval=interval, repeat_delay=3000)
#Error transition display
fig2, ax2 = plt.subplots(1)
fig2.suptitle("error transition")
ax2.set_yscale("log")
ax2.set_xlabel("epoch")
ax2.set_ylabel("error")
ax2.grid()
ax2.plot(error_list)
fig2.show()
fig2.savefig("error_transition.png ")
I will explain each of them.
Learning target setting
Learning target settings
test.py
#Learning target setting
def split_test(target, train_indices):
return target[train_indices], target[~ train_indices]
x = np.arange(0, 4, 5e-2)
y = np.sin(x)
x_left = 1
x_right = 3
y_top = np.max(y) + 1
y_bottom = np.min(y) - 1
indices = (x_left <= x) & (x <= x_right)
x_train, x_test = split_test(x, indices)
y_train, y_test = split_test(y, indices)
test.py
#Learning target setting
def split_test(target, train_indices):
return target[train_indices], target[~ train_indices]
x = np.arange(0, 4, 5e-2)
y = np.sin(x)
x_left = 1
x_right = 3
y_top = np.max(y) + 1
y_bottom = np.min(y) - 1
indices = (x_left <= x) & (x <= x_right)
x_train, x_test = split_test(x, indices)
y_train, y_test = split_test(y, indices)
Initial value setting
Initial value setting
test.py
#Initial setting
epoch = 10000
error_prev = 0
error = 0
error_list = []
threshold = 1e-8
n_batch = 4
n_train = x_train.size//n_batch
n_test = x_test.size
test.py
#Initial setting
epoch = 10000
error_prev = 0
error = 0
error_list = []
threshold = 1e-8
n_batch = 4
n_train = x_train.size//n_batch
n_test = x_test.size
Network construction
Network construction
test.py
#Network construction
n_in = 1
n_out = 1
lm = LayerManager()
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
test.py
#Network construction
n_in = 1
n_out = 1
lm = LayerManager()
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
Creating a foundation for animation
Creating a foundation for animation
test.py
#Creating a foundation for animation plots
n_image = 100
interval = 50
images = []
fig, ax = plt.subplots(1)
fig.suptitle("fitting animation")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_xlim(np.min(x), np.max(x))
ax.set_ylim(y_bottom, y_top)
ax.grid()
ax.plot(x, y, color="r")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
test.py
#Creating a foundation for animation plots
n_image = 100
interval = 50
images = []
fig, ax = plt.subplots(1)
fig.suptitle("fitting animation")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_xlim(np.min(x), np.max(x))
ax.set_ylim(y_bottom, y_top)
ax.grid()
ax.plot(x, y, color="r")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
Learning
Learning
test.py
#Start learning
rand_index = np.arange(x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if t % (epoch/n_image) == 1:
x_in = x.reshape(-1, 1)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
im, = ax.plot(x, ll.y, color="b")
images.append([im])
#Error calculation
x_in = x_test.reshape(n_test, n_in)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(y_test.reshape(n_test, n_out))
error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
#print("t", t)
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = x_train[rand].reshape(-1, n_in)
#print("x_in", x_in)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
y_in = y_train[rand].reshape(-1, n_out)
#print("y_in", y_in)
for ll in lm.layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in lm.layer_list:
ll.update()
test.py
#Start learning
rand_index = np.arange(x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if t % (epoch/n_image) == 1:
x_in = x.reshape(-1, 1)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
im, = ax.plot(x, ll.y, color="b")
images.append([im])
#Error calculation
x_in = x_test.reshape(n_test, n_in)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(y_test.reshape(n_test, n_out))
error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
#print("t", t)
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = x_train[rand].reshape(-1, n_in)
#print("x_in", x_in)
for ll in lm.layer_list:
x_in = ll.forward(x_in)
y_in = y_train[rand].reshape(-1, n_out)
#print("y_in", y_in)
for ll in lm.layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in lm.layer_list:
ll.update()
Animation and error transition display
Animation and error transition display
test.py
#Creating fitting animation
anim = animation.ArtistAnimation(fig, images, interval=interval, repeat_delay=3000)
#Error transition display
fig2, ax2 = plt.subplots(1)
fig2.suptitle("error transition")
ax2.set_yscale("log")
ax2.set_xlabel("epoch")
ax2.set_ylabel("error")
ax2.grid()
ax2.plot(error_list)
fig2.show()
fig2.savefig("error_transition.png ")
test.py
#Creating fitting animation
anim = animation.ArtistAnimation(fig, images, interval=interval, repeat_delay=3000)
#Error transition display
fig2, ax2 = plt.subplots(1)
fig2.suptitle("error transition")
ax2.set_yscale("log")
ax2.set_xlabel("epoch")
ax2.set_ylabel("error")
ax2.grid()
ax2.plot(error_list)
fig2.show()
fig2.savefig("error_transition.png ")
Experimental result
An example of the created animation and error transition is as follows.
This is just an example because the initial parameters are generated by random numbers and different results are given each time they are executed.
As for the training data, the fitting is completed as soon as possible, and the test data is also fitted well so that it can be followed. Of course, since the test data is not trained, it is ** gaining versatility for unknown data **.
Ported functionality to LayerManager
Now, let's port some of the features written directly in the test code to LayerManager.
First, let the layer manager hold various things.
Change the initial settings of `test.py`
test.py
#Initial setting
epoch = 10000
#error_prev = 0
#error = 0
#error_list = []
threshold = 1e-8
n_batch = 4
#n_train = x_train.size//n_batch
#n_test = x_test.size
#Network construction
n_in = 1
n_out = 1
lm = LayerManager((x_train, x_test), (y_train, y_test))
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
test.py
#Initial setting
epoch = 10000
#error_prev = 0
#error = 0
#error_list = []
threshold = 1e-8
n_batch = 4
#n_train = x_train.size//n_batch
#n_test = x_test.size
#Network construction
n_in = 1
n_out = 1
lm = LayerManager((x_train, x_test), (y_train, y_test))
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
Port default settings to `__init__` in` layermanager.py`
layermanager.py
def __init__(self, x, y):
self.x_train, self.x_test = x
self.y_train, self.y_test = y
self.__layer_list = [] #List of layers
self.__name_list = [] #Name list for each layer
self.__ntype = np.zeros(self.N_TYPE, dtype=int) #Number of layers by type
layermanager.py
def __init__(self, x, y):
self.x_train, self.x_test = x
self.y_train, self.y_test = y
self.__layer_list = [] #List of layers
self.__name_list = [] #Name list for each layer
self.__ntype = np.zeros(self.N_TYPE, dtype=int) #Number of layers by type
Next, the learning body and the error transition display are ported.
Change the learning code of `test.py`
test.py
#Start learning
lm.training(epoch, threshold=threshold, n_batch=n_batch)
test.py
#Start learning
lm.training(epoch, threshold=threshold, n_batch=n_batch)
Port learning code to `layermanager.py`
layermanager.py
def training(self, epoch, n_batch=16, threshold=1e-8, show_error=True):
if show_error:
self.error_list = []
n_in = self.__layer_list[0].prev
n_out = self.__layer_list[-1].n
n_train = self.x_train.size//n_batch
n_test = self.x_test.size
#Start learning
error = 0
error_prev = 0
rand_index = np.arange(self.x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Error calculation
x_in = self.x_test.reshape(n_test, n_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(self.y_test.reshape(n_test, n_out))
if show_error:
error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
#print("t", t)
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = self.x_train[rand].reshape(-1, n_in)
#print("x_in", x_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
y_in = self.y_train[rand].reshape(-1, n_out)
#print("y_in", y_in)
for ll in self.__layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in self.__layer_list:
ll.update()
if show_error:
#Error transition display
self.show_error(**kwds)
def show_errors(self, title="error transition",
xlabel="epoch", ylabel="error", fname="error_transition.png "):
fig, ax = plt.subplots(1)
fig.suptitle(title)
ax.set_yscale("log")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.grid()
ax.plot(error_list)
fig.show()
if len(fname) != 0:
fig.savefig(fname)
layermanager.py
def training(self, epoch, n_batch=16, threshold=1e-8, show_error=True):
if show_error:
self.error_list = []
n_in = self.__layer_list[0].prev
n_out = self.__layer_list[-1].n
n_train = self.x_train.size//n_batch
n_test = self.x_test.size
#Start learning
error = 0
error_prev = 0
rand_index = np.arange(self.x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Error calculation
x_in = self.x_test.reshape(n_test, n_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(self.y_test.reshape(n_test, n_out))
if show_error:
error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
#print("t", t)
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = self.x_train[rand].reshape(-1, n_in)
#print("x_in", x_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
y_in = self.y_train[rand].reshape(-1, n_out)
#print("y_in", y_in)
for ll in self.__layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in self.__layer_list:
ll.update()
if show_error:
#Error transition display
self.show_error(**kwds)
def show_errors(self, title="error transition",
xlabel="epoch", ylabel="error", fname="error_transition.png "):
fig, ax = plt.subplots(1)
fig.suptitle(title)
ax.set_yscale("log")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.grid()
ax.plot(error_list)
fig.show()
if len(fname) != 0:
fig.savefig(fname)
Finally, it's an animation. I thought about it, but I couldn't think of a way to insert animation with high versatility, so it's appropriate ... If you come up with something, change it.
Change animation code for `test.py`
test.py
#Creating a foundation for animation plots
n_image = 100
interval = 100
fig, ax = lm.ready_anim(n_image, x, y, title="fitting animation")
#images = []
#fig, ax = plt.subplots(1)
#fig.suptitle("fitting animation")
#ax.set_xlabel("x")
#ax.set_ylabel("y")
#ax.set_xlim(np.min(x), np.max(x))
#ax.set_ylim(y_bottom, y_top)
#ax.grid()
#ax.plot(x, y, color="r")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
#Start learning
lm.training(epoch, threshold=threshold, n_batch=n_batch)
#Creating fitting animation
anim = animation.ArtistAnimation(lm.anim_fig, lm.images,
interval=interval, repeat_delay=3000)
test.py
#Creating a foundation for animation plots
n_image = 100
interval = 100
fig, ax = lm.ready_anim(n_image, x, y, title="fitting animation")
#images = []
#fig, ax = plt.subplots(1)
#fig.suptitle("fitting animation")
#ax.set_xlabel("x")
#ax.set_ylabel("y")
#ax.set_xlim(np.min(x), np.max(x))
#ax.set_ylim(y_bottom, y_top)
#ax.grid()
#ax.plot(x, y, color="r")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
#Start learning
lm.training(epoch, threshold=threshold, n_batch=n_batch)
#Creating fitting animation
anim = animation.ArtistAnimation(lm.anim_fig, lm.images,
interval=interval, repeat_delay=3000)
Port animation code to `layermanager.py`
layermanager.py
def training(self, epoch, n_batch=16, threshold=1e-8,
show_error=True, **kwds):
if show_error:
self.error_list = []
if self.make_anim:
self.images = []
n_in = self.__layer_list[0].prev
n_out = self.__layer_list[-1].n
n_train = self.x_train.size//n_batch
n_test = self.x_test.size
#Start learning
error = 0
error_prev = 0
rand_index = np.arange(self.x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if self.make_anim:
self.make_scene(t, epoch)
#Error calculation
The following is omitted
def show_errors(self, title="error transition",
xlabel="epoch", ylabel="error", fname="error_transition.png ",
**kwds):
The following is omitted
def ready_anim(self, n_image, x, y, title="animation",
xlabel="x", ylabel="y", ex_color="r", color="b",
x_left=0, x_right=0, y_down = 1, y_up = 1):
self.n_image = n_image
self.x = x
self.color = color
self.make_anim = True
self.anim_fig, self.anim_ax = plt.subplots(1)
self.anim_fig.suptitle(title)
self.anim_ax.set_xlabel(xlabel)
self.anim_ax.set_ylabel(ylabel)
self.anim_ax.set_xlim(np.min(x) - x_left, np.max(x) + x_right)
self.anim_ax.set_ylim(np.min(y) - y_down, np.max(y) + y_up)
self.anim_ax.grid()
self.anim_ax.plot(x, y, color=ex_color)
return self.anim_fig, self.anim_ax
def make_scene(self, t, epoch):
#Scene creation
if t % (epoch/self.n_image) == 1:
x_in = self.x.reshape(-1, 1)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
im, = self.anim_ax.plot(self.x, ll.y, color=self.color)
self.images.append([im])
layermanager.py
def training(self, epoch, n_batch=16, threshold=1e-8,
show_error=True, **kwds):
if show_error:
self.error_list = []
if self.make_anim:
self.images = []
n_in = self.__layer_list[0].prev
n_out = self.__layer_list[-1].n
n_train = self.x_train.size//n_batch
n_test = self.x_test.size
#Start learning
error = 0
error_prev = 0
rand_index = np.arange(self.x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if self.make_anim:
self.make_scene(t, epoch)
#Error calculation
The following is omitted
def show_errors(self, title="error transition",
xlabel="epoch", ylabel="error", fname="error_transition.png ",
**kwds):
The following is omitted
def ready_anim(self, n_image, x, y, title="animation",
xlabel="x", ylabel="y", ex_color="r", color="b",
x_left=0, x_right=0, y_down = 1, y_up = 1):
self.n_image = n_image
self.x = x
self.color = color
self.make_anim = True
self.anim_fig, self.anim_ax = plt.subplots(1)
self.anim_fig.suptitle(title)
self.anim_ax.set_xlabel(xlabel)
self.anim_ax.set_ylabel(ylabel)
self.anim_ax.set_xlim(np.min(x) - x_left, np.max(x) + x_right)
self.anim_ax.set_ylim(np.min(y) - y_down, np.max(y) + y_up)
self.anim_ax.grid()
self.anim_ax.plot(x, y, color=ex_color)
return self.anim_fig, self.anim_ax
def make_scene(self, t, epoch):
#Scene creation
if t % (epoch/self.n_image) == 1:
x_in = self.x.reshape(-1, 1)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
im, = self.anim_ax.plot(self.x, ll.y, color=self.color)
self.images.append([im])
`test.py` whole
test.py
%matplotlib nbagg
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tqdm
#Learning target setting
def split_test(target, train_indices):
return (target[train_indices], target[~ train_indices])
x = np.arange(0, 4, 5e-2)
y = np.sin(x)
x_left = 1
x_right = 3
y_top = np.max(y) + 1
y_bottom = np.min(y) - 1
indices = (x_left <= x) & (x <= x_right)
x_train, x_test = split_test(x, indices)
y_train, y_test = split_test(y, indices)
#Initial setting
epoch = 10000
threshold = 1e-5
n_batch = 4
#Network construction
n_in = 1
n_out = 1
lm = LayerManager((x_train, x_test), (y_train, y_test))
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
#Creating a foundation for animation plots
n_image = 100
interval = 100
fig, ax = lm.ready_anim(n_image, x, y, title="fitting animation")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
#Start learning
lm.training(epoch, threshold=threshold, n_batch=n_batch)
#Creating fitting animation
anim = animation.ArtistAnimation(lm.anim_fig, lm.images,
interval=interval, repeat_delay=3000)
test.py
%matplotlib nbagg
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tqdm
#Learning target setting
def split_test(target, train_indices):
return (target[train_indices], target[~ train_indices])
x = np.arange(0, 4, 5e-2)
y = np.sin(x)
x_left = 1
x_right = 3
y_top = np.max(y) + 1
y_bottom = np.min(y) - 1
indices = (x_left <= x) & (x <= x_right)
x_train, x_test = split_test(x, indices)
y_train, y_test = split_test(y, indices)
#Initial setting
epoch = 10000
threshold = 1e-5
n_batch = 4
#Network construction
n_in = 1
n_out = 1
lm = LayerManager((x_train, x_test), (y_train, y_test))
lm.append(prev=n_in, n=30, act="sigmoid", wb_width=1)
lm.append(n=30, act="sigmoid", wb_width=1)
lm.append(n=n_out, name="o", act="identity", wb_width=1)
#Creating a foundation for animation plots
n_image = 100
interval = 100
fig, ax = lm.ready_anim(n_image, x, y, title="fitting animation")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_left),
np.arange(y_bottom, y_top+1),
color="g")
ax.plot(np.full_like(np.arange(y_bottom, y_top+1), x_right),
np.arange(y_bottom, y_top+1),
color="g")
#Start learning
lm.training(epoch, threshold=threshold, n_batch=n_batch)
#Creating fitting animation
anim = animation.ArtistAnimation(lm.anim_fig, lm.images,
interval=interval, repeat_delay=3000)
`layermanager.py` whole
layermanager.py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tqdm
class _TypeManager():
"""
Manager class for layer types
"""
N_TYPE = 2 #Number of layer types
MIDDLE = 0 #Middle layer numbering
OUTPUT = 1 #Output layer numbering
class LayerManager(_TypeManager):
"""
Manager class for managing layers
"""
def __init__(self, x, y):
self.x_train, self.x_test = x
self.y_train, self.y_test = y
self.__layer_list = [] #List of layers
self.__name_list = [] #Name list for each layer
self.__ntype = np.zeros(self.N_TYPE, dtype=int) #Number of layers by type
def __repr__(self):
layerRepr= "layer_list: " + repr(self.__layer_list)
nameRepr = "name_list: " + repr(self.__name_list)
ntypeRepr = "ntype: " + repr(self.__ntype)
return (layerRepr + "\n"
+ nameRepr + "\n"
+ ntypeRepr)
def __str__(self):
layerStr = "layer_list: " + str(self.__layer_list)
nameStr = "name_list: " + str(self.__name_list)
ntypeStr = "ntype: " + str(self.__ntype)
return (layerStr + "\n"
+ nameStr + "\n"
+ ntypeStr)
def __len__(self):
"""
Python built-in functions`len`Describes the operation when called from.
Returns the sum of the number of layers by type.
"""
return int(np.sum(self.__ntype))
def __getitem__(self, key):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
x = lm[3].~~
Because it is called when an element of a list or array is accessed, like
Describe the operation at that time.
slice and str,Only allow access via int.
"""
if isinstance(key, slice):
#If the key is a slice, refer to the list of layers with slice.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
return self.__layer_list[key]
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Returns the elements of the list of applicable layers.
if key in self.__name_list:
index = self.__name_list.index(key)
return self.__layer_list[index]
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, returns the corresponding element in the list of layers.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
return self.__layer_list[key]
else:
raise KeyError(key, ": Undefined such key type.")
def __setitem__(self, key, value):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
lm[1] = x
Because it is called when an element of a list or array is accessed, like
Describe the operation at that time.
Only overwriting elements is allowed, and adding new elements is prohibited.
"""
value_type = ""
if isinstance(value, list):
#Specified on the right side'value'But'list'If
#All elements'BaseLayer'Error if class or not inheriting it.
if not np.all(
np.where(isinstance(value, BaseLayer), True, False)):
self.AssignError()
value_type = "list"
elif isinstance(value, BaseLayer):
#Specified on the right side'value'But'BaseLayer'Is it a class?
#Error if it is not inherited.
self.AssignError(type(value))
if value_type == "":
value_type = "BaseLayer"
if isinstance(key, slice):
#If key is a slice, overwrite the element in the list of layers.
#However'value_type'But'list'Otherwise an error.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
if value_type != "list":
self.AssignError(value_type)
self.__layer_list[key] = value
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Overwrite the element in the list of applicable layers.
#However'value_type'But'BaseLayer'Otherwise an error.
if value_type != "BaseLayer":
raise AssignError(value_type)
if key in self.__name_list:
index = self.__name_list.index(key)
self.__layer_list[index] = value
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, overwrite the corresponding element in the layer list.
#However'value_type'But'BaseLayer'Otherwise an error.
#Also, an abnormal value(Index out of range etc.)When is entered
#Python gives me an error.
if value_type != "BaseLayer":
raise AssignError(value_type)
self.__layer_list[key] = value
else:
raise KeyError(key, ": Undefined such key type.")
def __delitem__(self, key):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
del lm[2]
Because it is called when the element of the list or array is accessed by the del statement like
Describe the operation at that time.
If the specified element exists, it will be deleted and renamed.
"""
if isinstance(key, slice):
#If the key is a slice, delete the specified element as it is
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
del self.__layer_list[slice]
del self.__name_list[slice]
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Delete the relevant element.
if key in self.__name_list:
del self.__layer_list[index]
del self.__name_list[index]
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, delete the corresponding element in the layer list.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
del self.__layer_list[key]
else:
raise KeyError(key, ": Undefined such key type.")
#Rename
self._rename()
def _rename(self):
"""
When the name list naming violates the rules due to list operations
Rename the naming list and each layer to meet the rules again.
The naming rule is[Layer type][What number]will do.
If the layer type is Middle Layer, Middle
Output for Output Layer
It is abbreviated as.
The number is counted by type.
Also, here again__Counts ntypes.
"""
#Initialize the number of layers by type
self.__ntype = np.zeros(self.N_TYPE)
#Recount and rename each layer
for i in range(len(self)):
if "Middle" in self.__name_list[i]:
self.__ntype[self.MIDDLE] += 1
self.__name_list[i] = "Middle{}".format(
self.__ntype[self.MIDDLE])
self.__layer_list[i].name = "Middle{}".format(
self.__ntype[self.MIDDLE])
elif "Output" in self.__name_list[i]:
self.__ntype[self.OUTPUT] += 1
self.__name_list[i] = "Output{}".format(
self.__ntype[self.OUTPUT])
self.__layer_list[i].name = "Output{}".format(
self.__ntype[self.OUTPUT])
else:
raise UndefinedLayerType(self.__name_list[i])
def append(self, *, name="Middle", **kwds):
"""
Implementation of the familiar append method, which is a method for adding elements to a list.
"""
if "prev" in kwds:
# 'prev'Is included in the keyword
#This means that the number of elements in the previous layer is specified.
#Basically it is supposed to be the time to insert the first layer, so
#Other than that, it is basically determined automatically and is not specified.
if len(self) != 0:
if kwds["prev"] != self.__layer_list[-1].n:
#Error if it does not match the number of units at the end.
raise UnmatchUnitError(self.__layer_list[-1].n,
kwds["prev"])
else:
if len(self) == 0:
#The first layer must always specify the number of input units.
raise UnmatchUnitError("Input units", "Unspecified")
else:
#The number of units in the last layer'kwds'Add to
kwds["prev"] = self.__layer_list[-1].n
#Read the layer type and change the name according to the naming rule
if name == "Middle" or name == "mid" or name == "m":
name = "Middle"
elif name == "Output" or name == "out" or name == "o":
name = "Output"
else:
raise UndefinedLayerError(name)
#Add a layer.
if name == "Middle":
#Increment the layer by type
self.__ntype[self.MIDDLE] += 1
#Add to name
name += str(self.__ntype[self.MIDDLE])
#Add to name list
self.__name_list.append(name)
#Finally, create a layer and add it to the list.
self.__layer_list.append(
MiddleLayer(name=name, **kwds))
elif name == "Output":
#This is also the same.
self.__ntype[self.OUTPUT] += 1
name += str(self.__ntype[self.OUTPUT])
self.__name_list.append(name)
self.__layer_list.append(
OutputLayer(name=name, **kwds))
#If you do not draw an else statement here, change the name according to the naming rule
#Already abnormal at the stage'name'Is omitted.
def extend(self, lm):
"""
Another layer manager already in the extend method'lm'Elements of
Add all.
"""
if not isinstance(lm, LayerManager):
# 'lm'Error if the instance of is not LayerManager.
raise TypeError(type(lm), ": Unexpected type.")
if len(self) != 0:
if self.__layer_list[-1].n != lm[0].prev:
#With the number of units in your last layer
# 'lm'Error if the number of inputs in the first layer of is not the same.
raise UnmatchUnitError(self.__layer_list[-1].n,
lm[0].prev)
#Each'extend'Add by method
self.__layer_list.extend(lm.layer_list)
self.__name_list.extend(lm.name_list)
#Rename
self._rename()
def insert(self, prev_name, name="Middle", **kwds):
"""
In the insert method, specify the name of the previous layer and combine it with that layer.
Add an element.
"""
# 'prev_name'Error if does not exist.
if not prev_name in self.__name_list:
raise KeyError(prev_name, ": No such key.")
# 'prev'Is included in the keyword
# 'prev_name'Error if it does not match the number of units in the layer specified in.
if "prev" in kwds:
if kwds["prev"] \
!= self.__layer_list[self.index(prev_name)].n:
raise UnmatchUnitError(
kwds["prev"],
self.__layer_list[self.index(prev_name)].n)
# 'n'Is included in the keyword
if "n" in kwds:
# 'prev_name'If is not the last
if prev_name != self.__name_list[-1]:
#Error if it does not match the number of units in the next layer.
if kwds["n"] != self.__layer_list[
self.index(prev_name)+1].prev:
raise UnmatchUnitError(
kwds["n"],
self.__layer_list[self.index(prev_name)].prev)
#If there are no elements yet'append'Give an error to use the method.
if len(self) == 0:
raise RuntimeError(
"You have to use 'append' method instead.")
#Get index of insertion location
index = self.index(prev_name) + 1
#Read the layer type and change the name according to the naming rule
if name == "Middle" or name == "mid" or name == "m":
name = "Middle"
elif name == "Output" or name == "out" or name == "o":
name = "Output"
else:
raise UndefinedLayerError(name)
#Insert element
#At this time,'name'Does not yet follow the naming rules,
#I'll rename it later so don't worry about it.
if "Middle" in name:
self.__layer_list.insert(index,
MiddleLayer(name=name, **kwds))
self.__name_list.insert(index, name)
elif "Output" in name:
self.__layer_list.insert(index,
OutputLayer(name=name, **kwds))
self.__name_list.insert(index, name)
#Rename
self._rename()
def extend_insert(self, prev_name, lm):
"""
This is the original function.
It behaves like a combination of extend and insert methods.
Simply put, it's like inserting another layer manager.
"""
if not isinstance(lm, LayerManager):
# 'lm'Error if the instance of is not LayerManager.
raise TypeError(type(lm), ": Unexpected type.")
# 'prev_name'Error if does not exist.
if not prev_name in self.__name_list:
raise KeyError(prev_name, ": No such key.")
#The number of units of the layers before and after the specified location and the first and last layers of lm
#If they do not match, an error occurs.
if len(self) != 0:
if self.__layer_list[self.index(prev_name)].n \
!= lm.layer_list[0].prev:
#With the number of units in your designated location'lm'The first number of units in
#If they do not match, an error occurs.
raise UnmatchUnitError(
self.__layer_list[self.index(prev_name)].n,
lm.layer_list[0].prev)
if prev_name != self.__name_list[-1]:
# 'prev_name'Is not my last layer
if lm.layer_list[-1].n \
!= self.__layer_list[self.index(prev_name)+1].prev:
# 'lm'The number of units at the end of and the layer next to your designated location
# 'prev'Error if it does not match the number of units.
raise UnmatchUnitError(
lm.layer_list[-1].n,
self.__layer_list[self.index(prev_name)+1].prev)
else:
#If you don't have any elements'extend'I get an error to use the method.
raise RuntimeError(
"You have to use 'extend' method instead.")
#Get index of insertion location
index = self.index(prev_name) + 1
#Elements after the insertion location'buf'After evacuating to, remove it once and
#Add an element using the extend method
layer_buf = self.__layer_list[index:]
name_buf = self.__name_list[index:]
del self.__layer_list[index:]
del self.__name_list[index:]
self.extend(lm)
#Add the element that was evacuated
self.__layer_list.extend(layer_buf)
self.__name_list.extend(name_buf)
#Rename
self._rename()
def remove(self, key):
"""
The remove method removes the element with the specified name.
It is also allowed to be specified by index.
"""
#Already implemented'del'The sentence is OK.
del self[key]
def index(self, target):
return self.__name_list.index(target)
def name(self, indices):
return self.__name_list[indices]
@property
def layer_list(self):
return self.__layer_list
@property
def name_list(self):
return self.__name_list
@property
def ntype(self):
return self.__ntype
def training(self, epoch, n_batch=16, threshold=1e-8,
show_error=True, **kwds):
if show_error:
self.error_list = []
if self.make_anim:
self.images = []
n_in = self.__layer_list[0].prev
n_out = self.__layer_list[-1].n
n_train = self.x_train.size//n_batch
n_test = self.x_test.size
#Start learning
error = 0
error_prev = 0
rand_index = np.arange(self.x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if self.make_anim:
self.make_scene(t, epoch)
#Error calculation
x_in = self.x_test.reshape(n_test, n_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(self.y_test.reshape(n_test, n_out))
if show_error:
self.error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = self.x_train[rand].reshape(-1, n_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
y_in = self.y_train[rand].reshape(-1, n_out)
for ll in self.__layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in self.__layer_list:
ll.update()
if show_error:
#Error transition display
self.show_errors(**kwds)
def show_errors(self, title="error transition",
xlabel="epoch", ylabel="error", fname="error_transition.png ",
**kwds):
fig, ax = plt.subplots(1)
fig.suptitle(title)
ax.set_yscale("log")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.grid()
ax.plot(self.error_list)
fig.show()
if len(fname) != 0:
fig.savefig(fname)
def ready_anim(self, n_image, x, y, title="animation",
xlabel="x", ylabel="y", ex_color="r", color="b",
x_left=0, x_right=0, y_down = 1, y_up = 1):
self.n_image = n_image
self.x = x
self.color = color
self.make_anim = True
self.anim_fig, self.anim_ax = plt.subplots(1)
self.anim_fig.suptitle(title)
self.anim_ax.set_xlabel(xlabel)
self.anim_ax.set_ylabel(ylabel)
self.anim_ax.set_xlim(np.min(x) - x_left, np.max(x) + x_right)
self.anim_ax.set_ylim(np.min(y) - y_down, np.max(y) + y_up)
self.anim_ax.grid()
self.anim_ax.plot(x, y, color=ex_color)
return self.anim_fig, self.anim_ax
def make_scene(self, t, epoch):
#Scene creation
if t % (epoch/self.n_image) == 1:
x_in = self.x.reshape(-1, 1)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
im, = self.anim_ax.plot(self.x, ll.y, color=self.color)
self.images.append([im])
layermanager.py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tqdm
class _TypeManager():
"""
Manager class for layer types
"""
N_TYPE = 2 #Number of layer types
MIDDLE = 0 #Middle layer numbering
OUTPUT = 1 #Output layer numbering
class LayerManager(_TypeManager):
"""
Manager class for managing layers
"""
def __init__(self, x, y):
self.x_train, self.x_test = x
self.y_train, self.y_test = y
self.__layer_list = [] #List of layers
self.__name_list = [] #Name list for each layer
self.__ntype = np.zeros(self.N_TYPE, dtype=int) #Number of layers by type
def __repr__(self):
layerRepr= "layer_list: " + repr(self.__layer_list)
nameRepr = "name_list: " + repr(self.__name_list)
ntypeRepr = "ntype: " + repr(self.__ntype)
return (layerRepr + "\n"
+ nameRepr + "\n"
+ ntypeRepr)
def __str__(self):
layerStr = "layer_list: " + str(self.__layer_list)
nameStr = "name_list: " + str(self.__name_list)
ntypeStr = "ntype: " + str(self.__ntype)
return (layerStr + "\n"
+ nameStr + "\n"
+ ntypeStr)
def __len__(self):
"""
Python built-in functions`len`Describes the operation when called from.
Returns the sum of the number of layers by type.
"""
return int(np.sum(self.__ntype))
def __getitem__(self, key):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
x = lm[3].~~
Because it is called when an element of a list or array is accessed, like
Describe the operation at that time.
slice and str,Only allow access via int.
"""
if isinstance(key, slice):
#If the key is a slice, refer to the list of layers with slice.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
return self.__layer_list[key]
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Returns the elements of the list of applicable layers.
if key in self.__name_list:
index = self.__name_list.index(key)
return self.__layer_list[index]
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, returns the corresponding element in the list of layers.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
return self.__layer_list[key]
else:
raise KeyError(key, ": Undefined such key type.")
def __setitem__(self, key, value):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
lm[1] = x
Because it is called when an element of a list or array is accessed, like
Describe the operation at that time.
Only overwriting elements is allowed, and adding new elements is prohibited.
"""
value_type = ""
if isinstance(value, list):
#Specified on the right side'value'But'list'If
#All elements'BaseLayer'Error if class or not inheriting it.
if not np.all(
np.where(isinstance(value, BaseLayer), True, False)):
self.AssignError()
value_type = "list"
elif isinstance(value, BaseLayer):
#Specified on the right side'value'But'BaseLayer'Is it a class?
#Error if it is not inherited.
self.AssignError(type(value))
if value_type == "":
value_type = "BaseLayer"
if isinstance(key, slice):
#If key is a slice, overwrite the element in the list of layers.
#However'value_type'But'list'Otherwise an error.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
if value_type != "list":
self.AssignError(value_type)
self.__layer_list[key] = value
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Overwrite the element in the list of applicable layers.
#However'value_type'But'BaseLayer'Otherwise an error.
if value_type != "BaseLayer":
raise AssignError(value_type)
if key in self.__name_list:
index = self.__name_list.index(key)
self.__layer_list[index] = value
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, overwrite the corresponding element in the layer list.
#However'value_type'But'BaseLayer'Otherwise an error.
#Also, an abnormal value(Index out of range etc.)When is entered
#Python gives me an error.
if value_type != "BaseLayer":
raise AssignError(value_type)
self.__layer_list[key] = value
else:
raise KeyError(key, ": Undefined such key type.")
def __delitem__(self, key):
"""
For example
lm = LayerManager()
+----------------+
| (Add element to lm) |
+----------------+
del lm[2]
Because it is called when the element of the list or array is accessed by the del statement like
Describe the operation at that time.
If the specified element exists, it will be deleted and renamed.
"""
if isinstance(key, slice):
#If the key is a slice, delete the specified element as it is
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
del self.__layer_list[slice]
del self.__name_list[slice]
elif isinstance(key, str):
#If key is a string, get the index from the name list of each layer and
#Delete the relevant element.
if key in self.__name_list:
del self.__layer_list[index]
del self.__name_list[index]
else:
#If the key does not exist, KeyError is issued.
raise KeyError("{}: No such item".format(key))
elif isinstance(key, int):
#If key is an integer, delete the corresponding element in the layer list.
#Unusual value(Index out of range etc.)When is entered
#Python gives me an error.
del self.__layer_list[key]
else:
raise KeyError(key, ": Undefined such key type.")
#Rename
self._rename()
def _rename(self):
"""
When the name list naming violates the rules due to list operations
Rename the naming list and each layer to meet the rules again.
The naming rule is[Layer type][What number]will do.
If the layer type is Middle Layer, Middle
Output for Output Layer
It is abbreviated as.
The number is counted by type.
Also, here again__Counts ntypes.
"""
#Initialize the number of layers by type
self.__ntype = np.zeros(self.N_TYPE)
#Recount and rename each layer
for i in range(len(self)):
if "Middle" in self.__name_list[i]:
self.__ntype[self.MIDDLE] += 1
self.__name_list[i] = "Middle{}".format(
self.__ntype[self.MIDDLE])
self.__layer_list[i].name = "Middle{}".format(
self.__ntype[self.MIDDLE])
elif "Output" in self.__name_list[i]:
self.__ntype[self.OUTPUT] += 1
self.__name_list[i] = "Output{}".format(
self.__ntype[self.OUTPUT])
self.__layer_list[i].name = "Output{}".format(
self.__ntype[self.OUTPUT])
else:
raise UndefinedLayerType(self.__name_list[i])
def append(self, *, name="Middle", **kwds):
"""
Implementation of the familiar append method, which is a method for adding elements to a list.
"""
if "prev" in kwds:
# 'prev'Is included in the keyword
#This means that the number of elements in the previous layer is specified.
#Basically it is supposed to be the time to insert the first layer, so
#Other than that, it is basically determined automatically and is not specified.
if len(self) != 0:
if kwds["prev"] != self.__layer_list[-1].n:
#Error if it does not match the number of units at the end.
raise UnmatchUnitError(self.__layer_list[-1].n,
kwds["prev"])
else:
if len(self) == 0:
#The first layer must always specify the number of input units.
raise UnmatchUnitError("Input units", "Unspecified")
else:
#The number of units in the last layer'kwds'Add to
kwds["prev"] = self.__layer_list[-1].n
#Read the layer type and change the name according to the naming rule
if name == "Middle" or name == "mid" or name == "m":
name = "Middle"
elif name == "Output" or name == "out" or name == "o":
name = "Output"
else:
raise UndefinedLayerError(name)
#Add a layer.
if name == "Middle":
#Increment the layer by type
self.__ntype[self.MIDDLE] += 1
#Add to name
name += str(self.__ntype[self.MIDDLE])
#Add to name list
self.__name_list.append(name)
#Finally, create a layer and add it to the list.
self.__layer_list.append(
MiddleLayer(name=name, **kwds))
elif name == "Output":
#This is also the same.
self.__ntype[self.OUTPUT] += 1
name += str(self.__ntype[self.OUTPUT])
self.__name_list.append(name)
self.__layer_list.append(
OutputLayer(name=name, **kwds))
#If you do not draw an else statement here, change the name according to the naming rule
#Already abnormal at the stage'name'Is omitted.
def extend(self, lm):
"""
Another layer manager already in the extend method'lm'Elements of
Add all.
"""
if not isinstance(lm, LayerManager):
# 'lm'Error if the instance of is not LayerManager.
raise TypeError(type(lm), ": Unexpected type.")
if len(self) != 0:
if self.__layer_list[-1].n != lm[0].prev:
#With the number of units in your last layer
# 'lm'Error if the number of inputs in the first layer of is not the same.
raise UnmatchUnitError(self.__layer_list[-1].n,
lm[0].prev)
#Each'extend'Add by method
self.__layer_list.extend(lm.layer_list)
self.__name_list.extend(lm.name_list)
#Rename
self._rename()
def insert(self, prev_name, name="Middle", **kwds):
"""
In the insert method, specify the name of the previous layer and combine it with that layer.
Add an element.
"""
# 'prev_name'Error if does not exist.
if not prev_name in self.__name_list:
raise KeyError(prev_name, ": No such key.")
# 'prev'Is included in the keyword
# 'prev_name'Error if it does not match the number of units in the layer specified in.
if "prev" in kwds:
if kwds["prev"] \
!= self.__layer_list[self.index(prev_name)].n:
raise UnmatchUnitError(
kwds["prev"],
self.__layer_list[self.index(prev_name)].n)
# 'n'Is included in the keyword
if "n" in kwds:
# 'prev_name'If is not the last
if prev_name != self.__name_list[-1]:
#Error if it does not match the number of units in the next layer.
if kwds["n"] != self.__layer_list[
self.index(prev_name)+1].prev:
raise UnmatchUnitError(
kwds["n"],
self.__layer_list[self.index(prev_name)].prev)
#If there are no elements yet'append'Give an error to use the method.
if len(self) == 0:
raise RuntimeError(
"You have to use 'append' method instead.")
#Get index of insertion location
index = self.index(prev_name) + 1
#Read the layer type and change the name according to the naming rule
if name == "Middle" or name == "mid" or name == "m":
name = "Middle"
elif name == "Output" or name == "out" or name == "o":
name = "Output"
else:
raise UndefinedLayerError(name)
#Insert element
#At this time,'name'Does not yet follow the naming rules,
#I'll rename it later so don't worry about it.
if "Middle" in name:
self.__layer_list.insert(index,
MiddleLayer(name=name, **kwds))
self.__name_list.insert(index, name)
elif "Output" in name:
self.__layer_list.insert(index,
OutputLayer(name=name, **kwds))
self.__name_list.insert(index, name)
#Rename
self._rename()
def extend_insert(self, prev_name, lm):
"""
This is the original function.
It behaves like a combination of extend and insert methods.
Simply put, it's like inserting another layer manager.
"""
if not isinstance(lm, LayerManager):
# 'lm'Error if the instance of is not LayerManager.
raise TypeError(type(lm), ": Unexpected type.")
# 'prev_name'Error if does not exist.
if not prev_name in self.__name_list:
raise KeyError(prev_name, ": No such key.")
#The number of units of the layers before and after the specified location and the first and last layers of lm
#If they do not match, an error occurs.
if len(self) != 0:
if self.__layer_list[self.index(prev_name)].n \
!= lm.layer_list[0].prev:
#With the number of units in your designated location'lm'The first number of units in
#If they do not match, an error occurs.
raise UnmatchUnitError(
self.__layer_list[self.index(prev_name)].n,
lm.layer_list[0].prev)
if prev_name != self.__name_list[-1]:
# 'prev_name'Is not my last layer
if lm.layer_list[-1].n \
!= self.__layer_list[self.index(prev_name)+1].prev:
# 'lm'The number of units at the end of and the layer next to your designated location
# 'prev'Error if it does not match the number of units.
raise UnmatchUnitError(
lm.layer_list[-1].n,
self.__layer_list[self.index(prev_name)+1].prev)
else:
#If you don't have any elements'extend'I get an error to use the method.
raise RuntimeError(
"You have to use 'extend' method instead.")
#Get index of insertion location
index = self.index(prev_name) + 1
#Elements after the insertion location'buf'After evacuating to, remove it once and
#Add an element using the extend method
layer_buf = self.__layer_list[index:]
name_buf = self.__name_list[index:]
del self.__layer_list[index:]
del self.__name_list[index:]
self.extend(lm)
#Add the element that was evacuated
self.__layer_list.extend(layer_buf)
self.__name_list.extend(name_buf)
#Rename
self._rename()
def remove(self, key):
"""
The remove method removes the element with the specified name.
It is also allowed to be specified by index.
"""
#Already implemented'del'The sentence is OK.
del self[key]
def index(self, target):
return self.__name_list.index(target)
def name(self, indices):
return self.__name_list[indices]
@property
def layer_list(self):
return self.__layer_list
@property
def name_list(self):
return self.__name_list
@property
def ntype(self):
return self.__ntype
def training(self, epoch, n_batch=16, threshold=1e-8,
show_error=True, **kwds):
if show_error:
self.error_list = []
if self.make_anim:
self.images = []
n_in = self.__layer_list[0].prev
n_out = self.__layer_list[-1].n
n_train = self.x_train.size//n_batch
n_test = self.x_test.size
#Start learning
error = 0
error_prev = 0
rand_index = np.arange(self.x_train.size)
for t in tqdm.tqdm(range(1, epoch+1)):
#Scene creation
if self.make_anim:
self.make_scene(t, epoch)
#Error calculation
x_in = self.x_test.reshape(n_test, n_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
error = lm[-1].get_error(self.y_test.reshape(n_test, n_out))
if show_error:
self.error_list.append(error)
#Convergence test
if abs(error - error_prev) < threshold:
print("end learning...")
break
else:
error_prev = error
np.random.shuffle(rand_index)
for i in range(n_train):
rand = rand_index[i*n_in : (i+n_batch)*n_in]
x_in = self.x_train[rand].reshape(-1, n_in)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
y_in = self.y_train[rand].reshape(-1, n_out)
for ll in self.__layer_list[::-1]:
y_in = ll.backward(y_in)
for ll in self.__layer_list:
ll.update()
if show_error:
#Error transition display
self.show_errors(**kwds)
def show_errors(self, title="error transition",
xlabel="epoch", ylabel="error", fname="error_transition.png ",
**kwds):
fig, ax = plt.subplots(1)
fig.suptitle(title)
ax.set_yscale("log")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.grid()
ax.plot(self.error_list)
fig.show()
if len(fname) != 0:
fig.savefig(fname)
def ready_anim(self, n_image, x, y, title="animation",
xlabel="x", ylabel="y", ex_color="r", color="b",
x_left=0, x_right=0, y_down = 1, y_up = 1):
self.n_image = n_image
self.x = x
self.color = color
self.make_anim = True
self.anim_fig, self.anim_ax = plt.subplots(1)
self.anim_fig.suptitle(title)
self.anim_ax.set_xlabel(xlabel)
self.anim_ax.set_ylabel(ylabel)
self.anim_ax.set_xlim(np.min(x) - x_left, np.max(x) + x_right)
self.anim_ax.set_ylim(np.min(y) - y_down, np.max(y) + y_up)
self.anim_ax.grid()
self.anim_ax.plot(x, y, color=ex_color)
return self.anim_fig, self.anim_ax
def make_scene(self, t, epoch):
#Scene creation
if t % (epoch/self.n_image) == 1:
x_in = self.x.reshape(-1, 1)
for ll in self.__layer_list:
x_in = ll.forward(x_in)
im, = self.anim_ax.plot(self.x, ll.y, color=self.color)
self.images.append([im])
in conclusion
This concludes the DNN (Deep Neural Network) experiment. Try playing around with other functions by approximating them.
Deep learning series
-Introduction to Deep Learning ~ Basics ~ -Introduction to Deep Learning ~ Coding Preparation ~ -Introduction to Deep Learning ~ Forward Propagation ~ -Introduction to Deep Learning ~ Backpropagation ~ -Introduction to Deep Learning ~ Learning Rules ~ -Introduction to Deep Learning ~ Localization and Loss Functions ~ -Introduction to Deep Learning ~ Function Approximation ~ -List of activation functions (2020) -Gradient descent method list (2020) -See and understand! Comparison of optimization methods (2020) -Thorough understanding of im2col -Complete understanding of numpy.pad function
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