ValueError：无法为具有形状“（？，4）”的张量“Placeholder_36：0”提供形状（4，）的值

Question

我正在尝试实现 tensorflow 回归模型，我的数据形状是 train_X=(200,4) 和 train_Y=(200,)。我收到形状错误，这是我的一段代码，请任何人指出我在哪里做错了。

df=pd.read_csv('all.csv')

df=df.drop('时间',axis=1)

print(df.describe()) #了解数据集

train_Y=df["power"]

train_X=df.drop('power',axis=1)

train_X=numpy.asarray(train_X)

train_Y=numpy.asarray(train_Y)

n_samples = train_X.shape[0]

tf 图形输入

X = tf.placeholder('float',[None,len(train_X[0])])

Y = tf.placeholder("float")

设置模型权重

W = tf.Variable(rng.randn(), name="weight")

b = tf.Variable(rng.randn(), name="bias")

构建线性模型

pred = tf.add(tf.multiply(X, W), b)

均方误差

成本 = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)

梯度下降

注意，minimize() 知道要修改 W 和 b，因为 Variable 对象是

trainable=默认为真

优化器 = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

初始化变量（即赋予它们的默认值）

init = tf.global_variables_initializer()

开始训练

使用 tf.Session() 作为 sess：

# Run the initializer

sess.run(init)

# Fit all training data

for epoch in range(training_epochs):

    for (x, y) in zip(train_X, train_Y):

        sess.run(optimizer, feed_dict={X: x, Y: y})

    # Display logs per epoch step
    if (epoch+1) % display_step == 0:
        c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
        print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
            "W=", sess.run(W), "b=", sess.run(b))

print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

# Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()enter code here

Answer 1

我改变了形状并解决了问题

train_y = np.reshape(train_y, (-1, 1))