Dev Log D-05&06. Forward Propagation Implemented.

import mpmath as mpMath 
mpMath.mp.dps = 5
class Neuron:
    def __init__(self,W):
        # Weight is an vector.
        self.W =  W
        self.B = np.random.uniform(-1, 1)
    def forward(self,X):
        self.wX = X.dot(self.W)
        self.f_out = self.wX + self.B
        return self.f_out
    def tanh(self):
        self.out_tanh = mpMath.tanh(self.f_out)
        return self.out_tanh

neuron = Neuron( np.random.uniform(-1.,1.,size=(784,)).astype(np.float16))

class NLayer:
    def __init__(self,input_dimension,number_of_neurons):
        self.neurons = []
        # A Layer have input and output dimensions.
        # I am considering the neurons as output dimensions. 
        # and Input dimension will be the dimension/lenght of X like 784 in mnist data. 
        print(f'creating NLayer with {input_dimension} input dimension and {number_of_neurons} neurons \n')
        self.num_of_n = number_of_neurons
        self.input_length = input_dimension
        for n in range(0,number_of_neurons):
            # Initialze the weights and bias and create neurons
            W =  np.random.uniform(-1.,1.,size=(self.input_length,)).astype(np.float16)
            self.neurons.append(Neuron(W))

    def forward(self,X):
        # print(f'Shape of X {X.shape}')
        for n in range(self.num_of_n):
            self.neurons[n].forward(X)
            self.neurons[n].tanh()

        # print(f'{len(self.neurons)}')
        self.f_out = np.array([float(neuron.out_tanh) for neuron in self.neurons])

        return self.f_out
            # print(self.neurons[n].out_tanh)
    def display_output(self):
        # can be used when output dimension is a perfect square
        grid_size = int(np.sqrt(len(self.f_out)))
        image_matrix = np.reshape(self.f_out, (grid_size, grid_size))
        plt.imshow(image_matrix, cmap='gray')
        plt.colorbar()
        plt.title("Array Visualization")
        plt.show()
    def display_output1(self):
        indices = np.arange(len(self.f_out))
        plt.scatter(indices, self.f_out, marker='o', color='b')
        plt.title("Array Visualization")
        plt.xlabel("Index")
        plt.ylabel("Output Values")
        plt.show()
        print(indices.dtype)


# layer_1 = NLayer(784,784)

class NNetwork:
    # A network have layers that

    def __init__(self,input_dim,hidden_layer_dim_arr):
        self.neuronsLayers = []
        self.input_dim = input_dim
        self.hidden_layer_dim_arr = hidden_layer_dim_arr
        self.inputL = NLayer(input_dim,input_dim)
        for d in range(0,len(hidden_layer_dim_arr)):
            if d == 0:
                self.neuronsLayers.append(NLayer(input_dim,hidden_layer_dim_arr[d]))
            else:
                self.neuronsLayers.append(NLayer(hidden_layer_dim_arr[d-1],hidden_layer_dim_arr[d]))
    def train(self):
        # update weights 
        pass
    def test(self):
        # Don't update weights. check weights.
        pass
    def predict(self,X):
        self.inputL_out = self.inputL.forward(X)
        # print(f'Forward out of Input layers is {self.inputL_out}')
        self.inputL.display_output1()
        for l in range(0,len(self.neuronsLayers)):
            if l == 0:
                self.neuronsLayers[l].forward(self.inputL_out)
            else:
                self.neuronsLayers[l].forward(self.neuronsLayers[l-1].f_out)
            print(f'Forward output of layer {l} is :--> {self.neuronsLayers[l].f_out}')
            self.neuronsLayers[l].display_output1()

    def __str__(self):
        print(f'Input Dimension {self.input_dim}. Hidden layer dim array {self.hidden_layer_dim_arr}')
        return f'Neural Network with {len(self.neuronsLayers)} hidden layer' 
nn1 = NNetwork(input_dim=784,hidden_layer_dim_arr=[10,10])
print(nn1)
nn1.predict(train_data[0])