我正在尝试使用张量流来训练具有正则化的多元线性回归模型.出于某种原因,我无法获得以下代码的训练片段来计算我想用于梯度下降更新的错误.我在设置图表时做错了吗？
def normalize_data(matrix):
averages = np.average(matrix,0)
mins = np.min(matrix,0)
maxes = np.max(matrix,0)
ranges = maxes - mins
return ((matrix - averages)/ranges)
def run_regression(X, Y, X_test, Y_test, lambda_value = 0.1, normalize=False, batch_size=10):
x_train = normalize_data(X) if normalize else X
y_train = Y
x_test = X_test
y_test = Y_test
session = tf.Session()
# Calculate number of features for X and Y
x_features_length = len(X[0])
y_features_length = len(Y[0])
# Build Tensorflow graph parts
x = tf.placeholder('float', [None, x_features_length], name="X")
y = tf.placeholder('float', [None, …