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Create codespell.yml (#1698)

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* fixup! Format Python code with psf/black push

* Create codespell.yml

* fixup! Format Python code with psf/black push
This commit is contained in:
Christian Clauss
2020-01-18 13:24:33 +01:00
committed by GitHub
parent c01d178798
commit bfcb95b297
78 changed files with 206 additions and 188 deletions
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24
neural_network/convolution_neural_network.py
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@ -1,12 +1,12 @@
"""
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Name - - CNN - Convolution Neural Network For Photo Recognizing
Goal - - Recognize Handing Writting Word Photo
Goal - - Recognize Handing Writing Word Photo
Detail：Total 5 layers neural network
* Convolution layer
* Pooling layer
* Input layer layer of BP
* Hiden layer of BP
* Hidden layer of BP
* Output layer of BP
Author: Stephen Lee
Github: 245885195@qq.com
@ -116,7 +116,7 @@ class CNN:
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(focus)
# caculate the feature map of every single kernel, and saved as list of matrix
# calculate the feature map of every single kernel, and saved as list of matrix
data_featuremap = []
Size_FeatureMap = int((size_data - size_conv) / conv_step + 1)
for i_map in range(num_conv):
@ -163,12 +163,12 @@ class CNN:
featuremap_pooled.append(map_pooled)
return featuremap_pooled
def _expand(self, datas):
def _expand(self, data):
# expanding three dimension data to one dimension list
data_expanded = []
for i in range(len(datas)):
shapes = np.shape(datas[i])
data_listed = datas[i].reshape(1, shapes[0] * shapes[1])
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)
@ -185,7 +185,7 @@ class CNN:
self, out_map, pd_pool, num_map, size_map, size_pooling
):
"""
calcluate the gradient from the data slice of pool layer
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]
@ -217,7 +217,7 @@ class CNN:
all_mse = []
mse = 10000
while rp < n_repeat and mse >= error_accuracy:
alle = 0
error_count = 0
print("-------------Learning Time %d--------------" % rp)
for p in range(len(datas_train)):
# print('------------Learning Image: %d--------------'%p)
@ -246,7 +246,7 @@ class CNN:
bp_out3 = self.sig(bp_net_k)
# --------------Model Leaning ------------------------
# calcluate error and gradient---------------
# calculate error and gradient---------------
pd_k_all = np.multiply(
(data_teach - bp_out3), np.multiply(bp_out3, (1 - bp_out3))
)
@ -285,11 +285,11 @@ class CNN:
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))
alle = alle + errors
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
rp = rp + 1
mse = alle / patterns
mse = error_count / patterns
all_mse.append(mse)
def draw_error():