es ist 8 uhr 5

data_gen.py

-import numpy as np
-
-
-class Rectangle:
-    
-    def __init__(self, x, y, width, height):
-        
-        self.x=x
-        self.y=y
-        self.width=width
-        self.height=height
-        self.matrix_size=10
-        self.matrix=self._init_zeros_matrix()
-        self.fill()
-        
-    def _init_zeros_matrix(self):
-       return np.zeros(shape=(self.matrix_size, self.matrix_size), dtype=np.float32)
-     
-    def add_noise(self):
-        
-        new_matrix=np.zeros(shape=(self.matrix_size, self.matrix_size), dtype=np.float32)
-        
-        for i in range(10):
-            for j in range(10):
-                new_matrix[i][j]+=self.matrix[i][j]+np.random.poisson(lam=2.0)
-        return new_matrix
-        
-    def fill(self):
-        
-        for i in range(self.width):
-            for j in range(self.height):
-                self.matrix[self.x+i][self.y+j]=10.0
-                           
-    def get_matrix(self):
-        return self.matrix
-    
-                
-rect1=Rectangle(0,0,width=3, height=5)
-rect1=Rectangle(0,0,width=3, height=5)
-recatangle=Rectangle(0,0,width=3, height=5)
-recatangle=Rectangle(0,0,width=3, height=5)   
-    
-rectangles={}
-rectangles[0]= Rectangle(0,0,2,2)
-rectangles[1]= Rectangle(3,3,2,2)
-rectangles[2]= Rectangle(5,5,2,2)
-rectangles[3]= Rectangle(7,7,2,2)
-
-rect_data=[]
-for i in range(1,10001):
-    index=i%4
-    rect_data.append((rectangles[index].add_noise(),index))
-
-
-with open('data.txt', 'w'):
-    for datapoint in range(len(rect_data)):
-        
-        
-        
-        
-    
-    
\ No newline at end of file
+import numpy as np
+
+class Rectangle:
+    
+    def __init__(self, x, y, width, height):
+        
+        self.x=x
+        self.y=y
+        self.width=width
+        self.height=height
+        self.matrix_size=10
+        self.matrix=self._init_zeros_matrix()
+        self.fill()
+        
+    def _init_zeros_matrix(self):
+       return np.zeros(shape=(self.matrix_size, self.matrix_size), dtype=np.float32)
+     
+    def add_noise(self):
+        
+        new_matrix=np.zeros(shape=(self.matrix_size, self.matrix_size), dtype=np.float32)
+        
+        for i in range(10):
+            for j in range(10):
+                new_matrix[i][j]+=self.matrix[i][j]+np.random.poisson(lam=2.0)
+        return new_matrix
+        
+    def fill(self):
+        
+        for i in range(self.width):
+            for j in range(self.height):
+                self.matrix[self.x+i][self.y+j]=10.0
+                           
+    def get_matrix(self):
+        return self.matrix
+     
+   
+  
+def gen_data():
+    
+    rectangles={0:Rectangle(0,0,2,2),
+                1:Rectangle(3,3,2,2),
+                2:Rectangle(5,5,2,2),
+                3:Rectangle(7,7,2,2),}
+    rect_data=[]
+    labels=[]
+    
+    for i in range(0,10):
+        label=i%4
+        rect=np.array(rectangles[label].add_noise())
+        rect_reshaped= np.reshape(rect,[1,100])
+        rect_data.append(rect_reshaped)
+        labels.append(label)
+    return rect_data, labels
+    
+rect_data, labels=gen_data()
+        
+

model.py

+import numpy as np
+from data_gen import gen_data
+class Model:
+    n_input = 100 # number is
+    n_output=4
+    n_h1=50
+    
+    def __init__(self, nodes=[n_input,n_h1, n_output]):
+        
+        self.nodes=nodes
+        self.num_layer=len(self.nodes)
+        self.weights=[]
+        
+        for i in range(0,self.num_layer-1):
+            temp_weights=np.random.random((self.nodes[i], self.nodes[i+1]))
+            self.weights.append(temp_weights)
+    
+    def activation(self, x, derivative=False):
+        '''Activation function (sigmoid by default)
+        @param x: input data
+        @param derivative: boolean if we need a derivative of the sigmoid'''
+ 
+        if not derivative:
+            return 1/(1+np.exp(-x))
+        else: 
+            return self.activation(x, False)*(1-self.activation(x, False))
+    
+    def forward_step(self, x):
+        
+        z_array=[] # dot product solution, before activation
+        a_array=[] 
+        a_array.append(x) # the inner states
+        outputs=[]
+
+        #Forward propagation
+                
+        for i in range(0, self.num_layer-1):
+            z=np.dot(self.weights[i].T,a_array[i])
+            z_array.append(z)
+            a=self.activation(z_array[i], False)
+            a_array.append(a)
+            
+        outputs.append(a_array[-1])
+        
+        return outputs
+    
+    def train_all(self, X, Y):
+        '''
+        @param x: all 10000 reshaped matrices
+        @param y: all 10000 labels
+        
+        
+        '''
+        
+        for i in np.arange(0,len(X)):
+            
+            x=np.reshape(X[i], [100,1])
+            y=Y[i]
+            self.train_one(x)
+                    
+    def train_one(self, x):
+        
+        out=self.forward_step(x)
+        print(out)
+        sd
+                
+                
+X, Y=gen_data()
+
+model=Model()
+model.train_all(X,Y)
+
+