ValueError: Cannot feed value of shape (4,) for Tensor 'Placeholder_36:0', which has shape '(?, 4)'

Question

I am trying to implement tensorflow regression model ,my data shape is train_X=(200,4) and train_Y=(200,). i am getting shape error ,here is my piece of code please can anyone mention where i am doing mistake.

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

df=df.drop('Time',axis=1)

print(df.describe()) #to understand the dataset

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 Graph Input

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

Y = tf.placeholder("float")

Set model weights

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

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

Construct a linear model

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

Mean squared error

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

Gradient descent

Note, minimize() knows to modify W and b because Variable objects are

trainable=True by default

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

Initialize the variables (i.e. assign their default value)

init = tf.global_variables_initializer()

Start training

with tf.Session() as 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

i changed shape and problem solved

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