ef slide2(y): l = y.shape[0] y = tf.reshape(y, [1, -1, 1]) input = keras.Input(shape=(l, 1)) output = Conv1D(filters=1, kernel_size=3, padding='causal', activation='linear', trainable=False, use_bias=False)(input) model = keras.Model(inputs=input, outputs=output) weights_list = model.get_weights() weights = np.ones(3) / 3 weights_list[0] = weights.reshape((3, 1, 1)) model.set_weights(weights_list) result = model.predict(y) result = tf.reshape(result, [-1, 1]) return result怎么使用 tf.stop_gradient 函数来将其视为常数

如果你想把 `Conv1D` 操作视为常数，可以使用 `tf.stop_gradient` 函数将其包装起来。修改代码如下：

def slide2(y):
    l = y.shape[0]
    y = tf.reshape(y, [1, -1, 1])
    input = keras.Input(shape=(l, 1))
    output = Conv1D(filters=1, kernel_size=3, padding='causal', activation='linear', trainable=False, use_bias=False)(input)
    output = tf.stop_gradient(output)  # 将 output 视为常数
    model = keras.Model(inputs=input, outputs=output)
    weights_list = model.get_weights()
    weights = np.ones(3) / 3
    weights_list[0] = weights.reshape((3, 1, 1))
    model.set_weights(weights_list)
    result = model.predict(y)
    result = tf.reshape(result, [-1, 1])
    return result

这样，`output` 就会被视为常数，不会参与梯度计算。

相关问题

y_0 = y_0.reshape(1, -1)

这行代码是将y_0数组进行了形状变换，将原本的一维数组变成了一个形状为(1, n)的二维数组（其中n为y_0数组的元素个数）。这个变换的目的是为了适配后续机器学习模型的输入数据格式要求。其中的"-1"表示这个维度的长度将会根据数组中的元素个数自动计算得出。具体来说，这行代码的目的是将y_0的形状变为(1, n)，其中1表示样本数量，n表示特征数量。

新数据前面多了一列无用的，每列用逗号隔开，改代码data = pd.read_csv('/home/w123/Documents/data-analysis/40-0-data/ratio/40-0-ratio.txt') y = data.iloc[:, :-1].values.reshape(-1, 1) X = data.iloc[:, -1].values.reshape(-1, 1) regressor = LinearRegression() regressor.fit(X, y) y_pred = regressor.predict(X) print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor.intercept_[0], regressor.coef_[0][0])) plt.scatter(X, y, color='blue') plt.plot(X, y_pred, color='red') data2 = pd.read_csv('/home/w123/Documents/data-analysis/40-0-data/ratio/40-5-ratio.txt') y2 = data2.iloc[:, :-1].values.reshape(-1, 1) X2 = data2.iloc[:, -1].values.reshape(-1, 1) regressor2 = LinearRegression() regressor2.fit(X2, y2) y2_pred = regressor2.predict(X2) print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor2.intercept_[0], regressor2.coef_[0][0])) plt.scatter(X2, y2, color='green') plt.plot(X2, y2_pred, color='orange') plt.legend(['Regression Line 2', 'Observations 2']) #3 data3 = pd.read_csv('/home/w123/Documents/data-analysis/40-0-data/ratio/40-10-ratio.txt') y3 = data3.iloc[:, :-1].values.reshape(-1, 1) X3 = data3.iloc[:, -1].values.reshape(-1, 1) regressor3 = LinearRegression() regressor3.fit(X3, y3) y3_pred = regressor3.predict(X3) print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor3.intercept_[0], regressor.coef_[0][0])) plt.scatter(X3, y3, color='purple') plt.plot(X3, y3_pred, color='yellow') plt.title('Linear Regression') plt.xlabel('Independent Variable') plt.ylabel('Dependent Variable') plt.legend(['Regression Line 1', 'Observations 1', 'Regression Line 2', 'Observations 2', 'Regression Line 3', 'Observations 3']) plt.show()

你想问如何处理新数据前面多出来的一列无用的数据，对吗？可以使用`usecols`参数来指定读取哪些列，例如`usecols=[1]`来只读取第二列数据。修改后的代码如下：

data = pd.read_csv('/home/w123/Documents/data-analysis/40-0-data/ratio/40-0-ratio.txt', usecols=[1, 2])
y = data.iloc[:, :-1].values.reshape(-1, 1)
X = data.iloc[:, -1].values.reshape(-1, 1)

regressor = LinearRegression()
regressor.fit(X, y)
y_pred = regressor.predict(X)
print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor.intercept_[0], regressor.coef_[0][0]))

plt.scatter(X, y, color='blue')
plt.plot(X, y_pred, color='red')

data2 = pd.read_csv('/home/w123/Documents/data-analysis/40-0-data/ratio/40-5-ratio.txt', usecols=[1, 2])
y2 = data2.iloc[:, :-1].values.reshape(-1, 1)
X2 = data2.iloc[:, -1].values.reshape(-1, 1)

regressor2 = LinearRegression()
regressor2.fit(X2, y2)
y2_pred = regressor2.predict(X2)
print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor2.intercept_[0], regressor2.coef_[0][0]))

plt.scatter(X2, y2, color='green')
plt.plot(X2, y2_pred, color='orange')
plt.legend(['Regression Line 2', 'Observations 2'])

data3 = pd.read_csv('/home/w123/Documents/data-analysis/40-0-data/ratio/40-10-ratio.txt', usecols=[1, 2])
y3 = data3.iloc[:, :-1].values.reshape(-1, 1)
X3 = data3.iloc[:, -1].values.reshape(-1, 1)

regressor3 = LinearRegression()
regressor3.fit(X3, y3)
y3_pred = regressor3.predict(X3)
print("Regression Function: y = {:.2f} + {:.2f}x".format(regressor3.intercept_[0], regressor.coef_[0][0]))

plt.scatter(X3, y3, color='purple')
plt.plot(X3, y3_pred, color='yellow')

plt.title('Linear Regression')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.legend(['Regression Line 1', 'Observations 1', 'Regression Line 2', 'Observations 2', 'Regression Line 3', 'Observations 3'])
plt.show()