if test_Y[0][i] > 0.5: prediction_Y[0][i] = 1 else: prediction_Y[0][i] = 0 return prediction_Y if __name__ == "__main__": train_x, train_y, test_x, test_y, list_classes = load_dataset() #归一化 train_x = train_x / 255 test_x = test_x / 255 #参数初始化 params = model.params_init_model(train_x.shape[0], 2, [5, 1], init_method="He") W1 = params["W1"] b1 = params["b1"] W2 = params["W2"] b2 = params["b2"] learning_rate = 0.01 echo_num = 5000 for echo in range(echo_num): #正向传播 A1, Z1 = model.l_layer_forward_model(train_x, W1, b1, acitvation="relu") #第1层 A2, Z2 = model.l_layer_forward_model(A1, W2, b2, acitvation="sigmoid") #第2层
if test_Y[0][i] > 0.5: prediction_Y[0][i] = 1 else: prediction_Y[0][i] = 0 return prediction_Y if __name__ == "__main__": train_X, train_Y, test_x, test_y, list_classes = load_dataset() #归一化 train_X = train_X / 255 test_x = test_x / 255 #参数初始化 params = model.params_init_model(train_X.shape[0], 4, [20, 7, 5, 1], init_method="He") W1 = params["W1"] b1 = params["b1"] W2 = params["W2"] b2 = params["b2"] W3 = params["W3"] b3 = params["b3"] W4 = params["W4"] b4 = params["b4"] learning_rate = 0.0075 echo_num = 1000 for echo in range(echo_num): batches = model.mini_batch(train_X, train_Y) for batch in batches: train_x, train_y = batch
for i in range(test_Y.shape[1]): if test_Y[0][i] > 0.5: prediction_Y[0][i] = 1 else: prediction_Y[0][i] = 0 return prediction_Y if __name__ == "__main__": train_x, train_y, test_x, test_y, list_classes = load_dataset() #归一化 train_x = train_x / 255 test_x = test_x / 255 #参数初始化 params = model.params_init_model(train_x.shape[0], 1, [1]) W1 = params["W1"] b1 = params["b1"] learning_rate = 0.001 echo_num = 1000 for echo in range(echo_num): #正向传播 A1, Z1 = model.l_layer_forward_model(train_x, W1, b1, acitvation="sigmoid") #第1层 #计算代价 J, dJ = model.cost_model(A1, train_y) #反向传播