Suppose I have a Tensorflow tensor. How do I get the dimensions (shape) of the tensor as integer values? I know there are two methods, **tensor.get_shape()** and** tf.shape(tensor)**, but I can't get the shape values as integer int32 values.

For example, below I've created a 2-D tensor, and I need to get the number of rows and columns as int32 so that I can call** reshape()** to create a tensor of **shape (num_rows * num_cols, 1)**. However, the method tensor.get_shape() returns values as Dimension type, not int32.

import tensorflow as tf

import numpy as np

sess = tf.Session()

tensor = tf.convert_to_tensor(np.array([[1001,1002,1003],[3,4,5]]), dtype=tf.float32)

sess.run(tensor)

# array([[ 1001., 1002., 1003.],

# [ 3., 4., 5.]], dtype=float32)

tensor_shape = tensor.get_shape()

tensor_shape

# TensorShape([Dimension(2), Dimension(3)])

print tensor_shape

# (2, 3)

num_rows = tensor_shape[0] # ???

num_cols = tensor_shape[1] # ???

tensor2 = tf.reshape(tensor, (num_rows*num_cols, 1))

TypeError: Expected int32, got Dimension(6) of type 'Dimension' instead.