quacknet.writeAndReadWeightBias 

 1import numpy as np
 2
 3class writeAndRead: 
 4    def write(self, pathToWeight="ExampleCode/MNISTExample/WeightsAndBiases/weights.txt", pathToBias="ExampleCode/MNISTExample/WeightsAndBiases/biases.txt"):
 5        weightFile = open(pathToWeight, "w")
 6        for a in range(len(self.weights)):
 7            for b in range(len(self.weights[a])):
 8                currLine = " ".join(map(str, self.weights[a][b]))
 9                weightFile.write(currLine + "\n")
10            weightFile.write("\n")
11        weightFile.close()
12
13        biasFile = open(pathToBias, "w")
14        for a in range(len(self.biases)):
15            currLine = " ".join(map(str, self.biases[a]))
16            biasFile.write(currLine + "\n")
17        biasFile.close()
18    
19    def read(self, pathToWeight="ExampleCode/MNISTExample/WeightsAndBiases/weights.txt", pathToBias="ExampleCode/MNISTExample/WeightsAndBiases/biases.txt"):
20        weightFile = open(pathToWeight, "r")
21        layerWeights = []
22        for line in weightFile:
23            line = line.strip()
24            if(line == ""):
25                if(len(layerWeights) > 0):
26                    self.weights.append(np.array(layerWeights))
27                layerWeights = []
28            else:
29                l = []
30                for i in line.split():
31                    l.append(float(i))
32                layerWeights.append(l)
33        
34        if(len(layerWeights) > 0):
35            self.weights.append(layerWeights)
36        weightFile.close()
37
38        biasFile = open(pathToBias, "r")
39        layerWeights = []
40        for line in biasFile:
41            line = line.strip()
42            if(line != ""):
43                line = list(map(float, line.split()))
44                self.biases.append(line)
45        biasFile.close()
class writeAndRead: 
 4class writeAndRead: 
 5    def write(self, pathToWeight="ExampleCode/MNISTExample/WeightsAndBiases/weights.txt", pathToBias="ExampleCode/MNISTExample/WeightsAndBiases/biases.txt"):
 6        weightFile = open(pathToWeight, "w")
 7        for a in range(len(self.weights)):
 8            for b in range(len(self.weights[a])):
 9                currLine = " ".join(map(str, self.weights[a][b]))
10                weightFile.write(currLine + "\n")
11            weightFile.write("\n")
12        weightFile.close()
13
14        biasFile = open(pathToBias, "w")
15        for a in range(len(self.biases)):
16            currLine = " ".join(map(str, self.biases[a]))
17            biasFile.write(currLine + "\n")
18        biasFile.close()
19    
20    def read(self, pathToWeight="ExampleCode/MNISTExample/WeightsAndBiases/weights.txt", pathToBias="ExampleCode/MNISTExample/WeightsAndBiases/biases.txt"):
21        weightFile = open(pathToWeight, "r")
22        layerWeights = []
23        for line in weightFile:
24            line = line.strip()
25            if(line == ""):
26                if(len(layerWeights) > 0):
27                    self.weights.append(np.array(layerWeights))
28                layerWeights = []
29            else:
30                l = []
31                for i in line.split():
32                    l.append(float(i))
33                layerWeights.append(l)
34        
35        if(len(layerWeights) > 0):
36            self.weights.append(layerWeights)
37        weightFile.close()
38
39        biasFile = open(pathToBias, "r")
40        layerWeights = []
41        for line in biasFile:
42            line = line.strip()
43            if(line != ""):
44                line = list(map(float, line.split()))
45                self.biases.append(line)
46        biasFile.close()
def write( self, pathToWeight='ExampleCode/MNISTExample/WeightsAndBiases/weights.txt', pathToBias='ExampleCode/MNISTExample/WeightsAndBiases/biases.txt'): 
 5    def write(self, pathToWeight="ExampleCode/MNISTExample/WeightsAndBiases/weights.txt", pathToBias="ExampleCode/MNISTExample/WeightsAndBiases/biases.txt"):
 6        weightFile = open(pathToWeight, "w")
 7        for a in range(len(self.weights)):
 8            for b in range(len(self.weights[a])):
 9                currLine = " ".join(map(str, self.weights[a][b]))
10                weightFile.write(currLine + "\n")
11            weightFile.write("\n")
12        weightFile.close()
13
14        biasFile = open(pathToBias, "w")
15        for a in range(len(self.biases)):
16            currLine = " ".join(map(str, self.biases[a]))
17            biasFile.write(currLine + "\n")
18        biasFile.close()
def read( self, pathToWeight='ExampleCode/MNISTExample/WeightsAndBiases/weights.txt', pathToBias='ExampleCode/MNISTExample/WeightsAndBiases/biases.txt'): 
20    def read(self, pathToWeight="ExampleCode/MNISTExample/WeightsAndBiases/weights.txt", pathToBias="ExampleCode/MNISTExample/WeightsAndBiases/biases.txt"):
21        weightFile = open(pathToWeight, "r")
22        layerWeights = []
23        for line in weightFile:
24            line = line.strip()
25            if(line == ""):
26                if(len(layerWeights) > 0):
27                    self.weights.append(np.array(layerWeights))
28                layerWeights = []
29            else:
30                l = []
31                for i in line.split():
32                    l.append(float(i))
33                layerWeights.append(l)
34        
35        if(len(layerWeights) > 0):
36            self.weights.append(layerWeights)
37        weightFile.close()
38
39        biasFile = open(pathToBias, "r")
40        layerWeights = []
41        for line in biasFile:
42            line = line.strip()
43            if(line != ""):
44                line = list(map(float, line.split()))
45                self.biases.append(line)
46        biasFile.close()