"逻辑门的梯度下降算法"

Question

我试图通过实现逻辑门（与和或门）将梯度下降算法理解为自学。 我已经编写了相同的算法，但是在 np.array(numpy) 中给出输入时，实现中的代码似乎存在错误，数组大小似乎不匹配。 请专家对此提出您的想法，以纠正错误。

这是我的代码

# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt

成本函数和梯度下降算法

def mean_squared_error(y_true, y_predicted):
    
    # Calculating the loss or cost
    cost = np.sum((y_true-y_predicted)**2) / len(y_true)
    return cost

# Gradient Descent Function
# Here iterations, learning_rate, stopping_threshold
# are hyperparameters that can be tuned
def gradient_descent(x, y, iterations = 100, learning_rate = 0.0001,
                    stopping_threshold = 1e-6):
    
    # Initializing weight, bias, learning rate and iterations
    current_weight = 0.1
    current_bias = 0.01
    iterations = iterations
    learning_rate = learning_rate
    n = float(len(x))
    
    costs = []
    weights = []
    previous_cost = None
    
    # Estimation of optimal parameters
    for i in range(iterations):
        
        # Making predictions
        y_predicted = (current_weight * x) + current_bias
        
        # Calculationg the current cost
        current_cost = mean_squared_error(y, y_predicted)

        # If the change in cost is less than or equal to
        # stopping_threshold we stop the gradient descent
        if previous_cost and abs(previous_cost-current_cost)<=stopping_threshold:
            break
        
        previous_cost = current_cost

        costs.append(current_cost)
        weights.append(current_weight)
        
        # Calculating the gradients
        weight_derivative = -(2/n) * sum(x * (y-y_predicted))
        bias_derivative = -(2/n) * sum(y-y_predicted)
        
        # Updating weights and bias
        current_weight = current_weight - (learning_rate * weight_derivative)
        current_bias = current_bias - (learning_rate * bias_derivative)
                
        # Printing the parameters for each 1000th iteration
        #print(f"Iteration {i+1}: Cost {current_cost}, Weight \
        #{current_weight}, Bias {current_bias}")
  
  # Visualizing the weights and cost at for all iterations
    plt.figure(figsize = (8,6))
    plt.plot(weights, costs)
    plt.scatter(weights, costs, marker='o', color='red')
    plt.title("Cost vs Weights")
    plt.ylabel("Cost")
    plt.xlabel("Weight")
    plt.show()
    
    return current_weight, current_bias

定义主（）

def main():
    
    # Data
    X = np.array([[0,0],[0,1],[1,0],[1,1]])
    Y = np.array([[0],[1],[1],[1]])

    # Estimating weight and bias using gradient descent
    estimated_weight, eatimated_bias = gradient_descent(X, Y, iterations=2000)
    #print(f"Estimated Weight: {estimated_weight}\nEstimated Bias: {eatimated_bias}")

    # Making predictions using estimated parameters
    Y_pred = estimated_weight*X + eatimated_bias

    # Plotting the regression line
    plt.figure(figsize = (8,6))
    plt.scatter(X, Y, marker='o', color='red')
    plt.plot([min(X), max(X)], [min(Y_pred), max(Y_pred)], color='blue',markerfacecolor='red',
            markersize=10,linestyle='dashed')
    plt.xlabel("X")
    plt.ylabel("Y")
    plt.show()

主功能

if __name__=="__main__":
    main()

错误是：

/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  return array(a, dtype, copy=False, order=order, subok=True)

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

TypeError: only size-1 arrays can be converted to Python scalars


The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)

<ipython-input-12-c20cdc2df320> in <module>()
      1 if __name__=="__main__":
----> 2         main()

9 frames

/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     81 
     82     """
---> 83     return array(a, dtype, copy=False, order=order)
     84 
     85 

ValueError: setting an array element with a sequence.

谢谢你的帮助