Tensorflow Dimension size must be evenly divisible by N but is M for 'linear/linear_model/x/Reshape'

Question

I've been trying to use tensorflow's tf.estimator, but I'm getting the following errors regarding the shape of input/output data.

ValueError: Dimension size must be evenly divisible by 9 but is 12 for 'linear/linear_model/x/Reshape' (op: 'Reshape') with input shapes: [4,3], [2] and with input tensors computed as partial shapes: input[1] = [?,9].

Here is the code:

data_size = 3
iterations = 10
learn_rate = 0.005
# generate test data
input = np.random.rand(data_size,3)
output = np.dot(input, [2, 3 ,7]) + 4
output = np.transpose([output])

feature_columns = [tf.feature_column.numeric_column("x", shape=(data_size, 3))]
estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)

input_fn = tf.estimator.inputs.numpy_input_fn({"x":input}, output, batch_size=4, num_epochs=None, shuffle=True)

estimator.train(input_fn=input_fn, steps=iterations)

input data shape is shape=(3, 3):

[[ 0.06525168  0.3171153   0.61675511]
 [ 0.35166298  0.71816544  0.62770994]
 [ 0.77846666  0.20930611  0.1710842 ]]

output data shape is shape=(3, 1)

[[  9.399135  ]
 [ 11.25179188]
 [  7.38244104]]

I have sense it is related to input data, output data and batch_size, because when input data changed to 1 row it works. When input data rows count equal to batch_size(data_size = 10 and batch_size=10) then it throws other error:

ValueError: Shapes (1, 1) and (10, 1) are incompatible

Any help with the errors would be much appreciated.