Creating tensors from Python objects

We can create tensors from Python objects such as lists and NumPy arrays, using the tf.convert_to_tensor() operation with the following signature:

tf.convert_to_tensor(
  value,
  dtype=None,
  name=None,
  preferred_dtype=None
  )

Let's create some tensors and print them for practice:

1. Create and print a 0-D Tensor:

tf_t=tf.convert_to_tensor(5.0,dtype=tf.float64)

print('tf_t : ',tf_t)
print('run(tf_t) : ',tfs.run(tf_t))

The output is as follows: 

tf_t :  Tensor("Const_1:0", shape=(), dtype=float64)
run(tf_t) : 5.0

1. Create and print a 1-D Tensor:

a1dim = np.array([1,2,3,4,5.99])
print("a1dim Shape : ",a1dim.shape)

tf_t=tf.convert_to_tensor(a1dim,dtype=tf.float64)

print('tf_t : ',tf_t)
print('tf_t[0] : ',tf_t[0])
print('tf_t[0] : ',tf_t[2])
print('run(tf_t) : \n',tfs.run(tf_t))

The output is as follows:

a1dim Shape :  (5,)
tf_t :  Tensor("Const_2:0", shape=(5,), dtype=float64)
tf_t[0] :  Tensor("strided_slice:0", shape=(), dtype=float64)
tf_t[0] :  Tensor("strided_slice_1:0", shape=(), dtype=float64)
run(tf_t) : 
 [ 1.    2.    3.    4.    5.99]

1. Create and print a 2-D Tensor:

a2dim = np.array([(1,2,3,4,5.99),
                  (2,3,4,5,6.99),
                  (3,4,5,6,7.99)
                 ])
print("a2dim Shape : ",a2dim.shape)

tf_t=tf.convert_to_tensor(a2dim,dtype=tf.float64)

print('tf_t : ',tf_t)
print('tf_t[0][0] : ',tf_t[0][0])
print('tf_t[1][2] : ',tf_t[1][2])
print('run(tf_t) : \n',tfs.run(tf_t))

The output is as follows:

a2dim Shape :  (3, 5)
tf_t :  Tensor("Const_3:0", shape=(3, 5), dtype=float64)
tf_t[0][0] :  Tensor("strided_slice_3:0", shape=(), dtype=float64)
tf_t[1][2] :  Tensor("strided_slice_5:0", shape=(), dtype=float64)
run(tf_t) : 
 [[ 1.    2.    3.    4.    5.99]
  [ 2.    3.    4.    5.    6.99]
  [ 3.    4.    5.    6.    7.99]]

1. Create and print a 3-D Tensor:

a3dim = np.array([[[1,2],[3,4]],
                  [[5,6],[7,8]]
                 ])
print("a3dim Shape : ",a3dim.shape)

tf_t=tf.convert_to_tensor(a3dim,dtype=tf.float64)

print('tf_t : ',tf_t)
print('tf_t[0][0][0] : ',tf_t[0][0][0])
print('tf_t[1][1][1] : ',tf_t[1][1][1])
print('run(tf_t) : \n',tfs.run(tf_t))

The output is as follows:

a3dim Shape :  (2, 2, 2)
tf_t :  Tensor("Const_4:0", shape=(2, 2, 2), dtype=float64)
tf_t[0][0][0] :  Tensor("strided_slice_8:0", shape=(), dtype=float64)
tf_t[1][1][1] :  Tensor("strided_slice_11:0", shape=(), dtype=float64)
run(tf_t) : 
 [[[ 1.  2.][ 3.  4.]]
  [[ 5.  6.][ 7.  8.]]]

TensorFlow can seamlessly convert NumPy ndarray to TensorFlow tensor and vice-versa.