mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-18 01:00:15 +00:00
358 lines
14 KiB
Python
358 lines
14 KiB
Python
"""
|
|
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
|
|
Name - - CNN - Convolution Neural Network For Photo Recognizing
|
|
Goal - - Recognize Handing Writing Word Photo
|
|
Detail: Total 5 layers neural network
|
|
* Convolution layer
|
|
* Pooling layer
|
|
* Input layer layer of BP
|
|
* Hidden layer of BP
|
|
* Output layer of BP
|
|
Author: Stephen Lee
|
|
Github: 245885195@qq.com
|
|
Date: 2017.9.20
|
|
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
|
|
"""
|
|
|
|
import pickle
|
|
|
|
import numpy as np
|
|
from matplotlib import pyplot as plt
|
|
|
|
|
|
class CNN:
|
|
def __init__(
|
|
self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
|
|
):
|
|
"""
|
|
:param conv1_get: [a,c,d], size, number, step of convolution kernel
|
|
:param size_p1: pooling size
|
|
:param bp_num1: units number of flatten layer
|
|
:param bp_num2: units number of hidden layer
|
|
:param bp_num3: units number of output layer
|
|
:param rate_w: rate of weight learning
|
|
:param rate_t: rate of threshold learning
|
|
"""
|
|
self.num_bp1 = bp_num1
|
|
self.num_bp2 = bp_num2
|
|
self.num_bp3 = bp_num3
|
|
self.conv1 = conv1_get[:2]
|
|
self.step_conv1 = conv1_get[2]
|
|
self.size_pooling1 = size_p1
|
|
self.rate_weight = rate_w
|
|
self.rate_thre = rate_t
|
|
rng = np.random.default_rng()
|
|
self.w_conv1 = [
|
|
np.asmatrix(-1 * rng.random((self.conv1[0], self.conv1[0])) + 0.5)
|
|
for i in range(self.conv1[1])
|
|
]
|
|
self.wkj = np.asmatrix(-1 * rng.random((self.num_bp3, self.num_bp2)) + 0.5)
|
|
self.vji = np.asmatrix(-1 * rng.random((self.num_bp2, self.num_bp1)) + 0.5)
|
|
self.thre_conv1 = -2 * rng.random(self.conv1[1]) + 1
|
|
self.thre_bp2 = -2 * rng.random(self.num_bp2) + 1
|
|
self.thre_bp3 = -2 * rng.random(self.num_bp3) + 1
|
|
|
|
def save_model(self, save_path):
|
|
# save model dict with pickle
|
|
model_dic = {
|
|
"num_bp1": self.num_bp1,
|
|
"num_bp2": self.num_bp2,
|
|
"num_bp3": self.num_bp3,
|
|
"conv1": self.conv1,
|
|
"step_conv1": self.step_conv1,
|
|
"size_pooling1": self.size_pooling1,
|
|
"rate_weight": self.rate_weight,
|
|
"rate_thre": self.rate_thre,
|
|
"w_conv1": self.w_conv1,
|
|
"wkj": self.wkj,
|
|
"vji": self.vji,
|
|
"thre_conv1": self.thre_conv1,
|
|
"thre_bp2": self.thre_bp2,
|
|
"thre_bp3": self.thre_bp3,
|
|
}
|
|
with open(save_path, "wb") as f:
|
|
pickle.dump(model_dic, f)
|
|
|
|
print(f"Model saved: {save_path}")
|
|
|
|
@classmethod
|
|
def read_model(cls, model_path):
|
|
# read saved model
|
|
with open(model_path, "rb") as f:
|
|
model_dic = pickle.load(f) # noqa: S301
|
|
|
|
conv_get = model_dic.get("conv1")
|
|
conv_get.append(model_dic.get("step_conv1"))
|
|
size_p1 = model_dic.get("size_pooling1")
|
|
bp1 = model_dic.get("num_bp1")
|
|
bp2 = model_dic.get("num_bp2")
|
|
bp3 = model_dic.get("num_bp3")
|
|
r_w = model_dic.get("rate_weight")
|
|
r_t = model_dic.get("rate_thre")
|
|
# create model instance
|
|
conv_ins = CNN(conv_get, size_p1, bp1, bp2, bp3, r_w, r_t)
|
|
# modify model parameter
|
|
conv_ins.w_conv1 = model_dic.get("w_conv1")
|
|
conv_ins.wkj = model_dic.get("wkj")
|
|
conv_ins.vji = model_dic.get("vji")
|
|
conv_ins.thre_conv1 = model_dic.get("thre_conv1")
|
|
conv_ins.thre_bp2 = model_dic.get("thre_bp2")
|
|
conv_ins.thre_bp3 = model_dic.get("thre_bp3")
|
|
return conv_ins
|
|
|
|
def sig(self, x):
|
|
return 1 / (1 + np.exp(-1 * x))
|
|
|
|
def do_round(self, x):
|
|
return round(x, 3)
|
|
|
|
def convolute(self, data, convs, w_convs, thre_convs, conv_step):
|
|
# convolution process
|
|
size_conv = convs[0]
|
|
num_conv = convs[1]
|
|
size_data = np.shape(data)[0]
|
|
# get the data slice of original image data, data_focus
|
|
data_focus = []
|
|
for i_focus in range(0, size_data - size_conv + 1, conv_step):
|
|
for j_focus in range(0, size_data - size_conv + 1, conv_step):
|
|
focus = data[
|
|
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
|
|
]
|
|
data_focus.append(focus)
|
|
# calculate the feature map of every single kernel, and saved as list of matrix
|
|
data_featuremap = []
|
|
size_feature_map = int((size_data - size_conv) / conv_step + 1)
|
|
for i_map in range(num_conv):
|
|
featuremap = []
|
|
for i_focus in range(len(data_focus)):
|
|
net_focus = (
|
|
np.sum(np.multiply(data_focus[i_focus], w_convs[i_map]))
|
|
- thre_convs[i_map]
|
|
)
|
|
featuremap.append(self.sig(net_focus))
|
|
featuremap = np.asmatrix(featuremap).reshape(
|
|
size_feature_map, size_feature_map
|
|
)
|
|
data_featuremap.append(featuremap)
|
|
|
|
# expanding the data slice to One dimenssion
|
|
focus1_list = []
|
|
for each_focus in data_focus:
|
|
focus1_list.extend(self.Expand_Mat(each_focus))
|
|
focus_list = np.asarray(focus1_list)
|
|
return focus_list, data_featuremap
|
|
|
|
def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
|
|
# pooling process
|
|
size_map = len(featuremaps[0])
|
|
size_pooled = int(size_map / size_pooling)
|
|
featuremap_pooled = []
|
|
for i_map in range(len(featuremaps)):
|
|
feature_map = featuremaps[i_map]
|
|
map_pooled = []
|
|
for i_focus in range(0, size_map, size_pooling):
|
|
for j_focus in range(0, size_map, size_pooling):
|
|
focus = feature_map[
|
|
i_focus : i_focus + size_pooling,
|
|
j_focus : j_focus + size_pooling,
|
|
]
|
|
if pooling_type == "average_pool":
|
|
# average pooling
|
|
map_pooled.append(np.average(focus))
|
|
elif pooling_type == "max_pooling":
|
|
# max pooling
|
|
map_pooled.append(np.max(focus))
|
|
map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)
|
|
featuremap_pooled.append(map_pooled)
|
|
return featuremap_pooled
|
|
|
|
def _expand(self, data):
|
|
# expanding three dimension data to one dimension list
|
|
data_expanded = []
|
|
for i in range(len(data)):
|
|
shapes = np.shape(data[i])
|
|
data_listed = data[i].reshape(1, shapes[0] * shapes[1])
|
|
data_listed = data_listed.getA().tolist()[0]
|
|
data_expanded.extend(data_listed)
|
|
data_expanded = np.asarray(data_expanded)
|
|
return data_expanded
|
|
|
|
def _expand_mat(self, data_mat):
|
|
# expanding matrix to one dimension list
|
|
data_mat = np.asarray(data_mat)
|
|
shapes = np.shape(data_mat)
|
|
data_expanded = data_mat.reshape(1, shapes[0] * shapes[1])
|
|
return data_expanded
|
|
|
|
def _calculate_gradient_from_pool(
|
|
self, out_map, pd_pool, num_map, size_map, size_pooling
|
|
):
|
|
"""
|
|
calculate the gradient from the data slice of pool layer
|
|
pd_pool: list of matrix
|
|
out_map: the shape of data slice(size_map*size_map)
|
|
return: pd_all: list of matrix, [num, size_map, size_map]
|
|
"""
|
|
pd_all = []
|
|
i_pool = 0
|
|
for i_map in range(num_map):
|
|
pd_conv1 = np.ones((size_map, size_map))
|
|
for i in range(0, size_map, size_pooling):
|
|
for j in range(0, size_map, size_pooling):
|
|
pd_conv1[i : i + size_pooling, j : j + size_pooling] = pd_pool[
|
|
i_pool
|
|
]
|
|
i_pool = i_pool + 1
|
|
pd_conv2 = np.multiply(
|
|
pd_conv1, np.multiply(out_map[i_map], (1 - out_map[i_map]))
|
|
)
|
|
pd_all.append(pd_conv2)
|
|
return pd_all
|
|
|
|
def train(
|
|
self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
|
|
):
|
|
# model training
|
|
print("----------------------Start Training-------------------------")
|
|
print((" - - Shape: Train_Data ", np.shape(datas_train)))
|
|
print((" - - Shape: Teach_Data ", np.shape(datas_teach)))
|
|
rp = 0
|
|
all_mse = []
|
|
mse = 10000
|
|
while rp < n_repeat and mse >= error_accuracy:
|
|
error_count = 0
|
|
print(f"-------------Learning Time {rp}--------------")
|
|
for p in range(len(datas_train)):
|
|
# print('------------Learning Image: %d--------------'%p)
|
|
data_train = np.asmatrix(datas_train[p])
|
|
data_teach = np.asarray(datas_teach[p])
|
|
data_focus1, data_conved1 = self.convolute(
|
|
data_train,
|
|
self.conv1,
|
|
self.w_conv1,
|
|
self.thre_conv1,
|
|
conv_step=self.step_conv1,
|
|
)
|
|
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
|
|
shape_featuremap1 = np.shape(data_conved1)
|
|
"""
|
|
print(' -----original shape ', np.shape(data_train))
|
|
print(' ---- after convolution ',np.shape(data_conv1))
|
|
print(' -----after pooling ',np.shape(data_pooled1))
|
|
"""
|
|
data_bp_input = self._expand(data_pooled1)
|
|
bp_out1 = data_bp_input
|
|
|
|
bp_net_j = np.dot(bp_out1, self.vji.T) - self.thre_bp2
|
|
bp_out2 = self.sig(bp_net_j)
|
|
bp_net_k = np.dot(bp_out2, self.wkj.T) - self.thre_bp3
|
|
bp_out3 = self.sig(bp_net_k)
|
|
|
|
# --------------Model Leaning ------------------------
|
|
# calculate error and gradient---------------
|
|
pd_k_all = np.multiply(
|
|
(data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
|
|
)
|
|
pd_j_all = np.multiply(
|
|
np.dot(pd_k_all, self.wkj), np.multiply(bp_out2, (1 - bp_out2))
|
|
)
|
|
pd_i_all = np.dot(pd_j_all, self.vji)
|
|
|
|
pd_conv1_pooled = pd_i_all / (self.size_pooling1 * self.size_pooling1)
|
|
pd_conv1_pooled = pd_conv1_pooled.T.getA().tolist()
|
|
pd_conv1_all = self._calculate_gradient_from_pool(
|
|
data_conved1,
|
|
pd_conv1_pooled,
|
|
shape_featuremap1[0],
|
|
shape_featuremap1[1],
|
|
self.size_pooling1,
|
|
)
|
|
# weight and threshold learning process---------
|
|
# convolution layer
|
|
for k_conv in range(self.conv1[1]):
|
|
pd_conv_list = self._expand_mat(pd_conv1_all[k_conv])
|
|
delta_w = self.rate_weight * np.dot(pd_conv_list, data_focus1)
|
|
|
|
self.w_conv1[k_conv] = self.w_conv1[k_conv] + delta_w.reshape(
|
|
(self.conv1[0], self.conv1[0])
|
|
)
|
|
|
|
self.thre_conv1[k_conv] = (
|
|
self.thre_conv1[k_conv]
|
|
- np.sum(pd_conv1_all[k_conv]) * self.rate_thre
|
|
)
|
|
# all connected layer
|
|
self.wkj = self.wkj + pd_k_all.T * bp_out2 * self.rate_weight
|
|
self.vji = self.vji + pd_j_all.T * bp_out1 * self.rate_weight
|
|
self.thre_bp3 = self.thre_bp3 - pd_k_all * self.rate_thre
|
|
self.thre_bp2 = self.thre_bp2 - pd_j_all * self.rate_thre
|
|
# calculate the sum error of all single image
|
|
errors = np.sum(abs(data_teach - bp_out3))
|
|
error_count += errors
|
|
# print(' ----Teach ',data_teach)
|
|
# print(' ----BP_output ',bp_out3)
|
|
rp = rp + 1
|
|
mse = error_count / patterns
|
|
all_mse.append(mse)
|
|
|
|
def draw_error():
|
|
yplot = [error_accuracy for i in range(int(n_repeat * 1.2))]
|
|
plt.plot(all_mse, "+-")
|
|
plt.plot(yplot, "r--")
|
|
plt.xlabel("Learning Times")
|
|
plt.ylabel("All_mse")
|
|
plt.grid(True, alpha=0.5)
|
|
plt.show()
|
|
|
|
print("------------------Training Complished---------------------")
|
|
print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
|
|
if draw_e:
|
|
draw_error()
|
|
return mse
|
|
|
|
def predict(self, datas_test):
|
|
# model predict
|
|
produce_out = []
|
|
print("-------------------Start Testing-------------------------")
|
|
print((" - - Shape: Test_Data ", np.shape(datas_test)))
|
|
for p in range(len(datas_test)):
|
|
data_test = np.asmatrix(datas_test[p])
|
|
data_focus1, data_conved1 = self.convolute(
|
|
data_test,
|
|
self.conv1,
|
|
self.w_conv1,
|
|
self.thre_conv1,
|
|
conv_step=self.step_conv1,
|
|
)
|
|
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
|
|
data_bp_input = self._expand(data_pooled1)
|
|
|
|
bp_out1 = data_bp_input
|
|
bp_net_j = bp_out1 * self.vji.T - self.thre_bp2
|
|
bp_out2 = self.sig(bp_net_j)
|
|
bp_net_k = bp_out2 * self.wkj.T - self.thre_bp3
|
|
bp_out3 = self.sig(bp_net_k)
|
|
produce_out.extend(bp_out3.getA().tolist())
|
|
res = [list(map(self.do_round, each)) for each in produce_out]
|
|
return np.asarray(res)
|
|
|
|
def convolution(self, data):
|
|
# return the data of image after convoluting process so we can check it out
|
|
data_test = np.asmatrix(data)
|
|
data_focus1, data_conved1 = self.convolute(
|
|
data_test,
|
|
self.conv1,
|
|
self.w_conv1,
|
|
self.thre_conv1,
|
|
conv_step=self.step_conv1,
|
|
)
|
|
data_pooled1 = self.pooling(data_conved1, self.size_pooling1)
|
|
|
|
return data_conved1, data_pooled1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
"""
|
|
I will put the example on other file
|
|
"""
|