2-1 Data Structure

    TensorFlow Program = Data Structure of Tensor + Algorithm in Graph

    Tensor and graph are key concepts of TensorFlow.

    The fundamental data structure in TensorFlow is Tensor, which is multi-dimentional array. Tensor is similar with the in numpy.

    There are two types of tensor accoring to the behavior: constant and variable.

    The value of constant cannot be re-assigned in the graph, while variable can be re-assigned through operators such as assign.

    1. True
    2. True
    3. True
    4. False

    Each data type can be represented by tensor in different rank.

    Scalars are tensors with rank = 0, arrays are with rank = 1, matrix are with rank = 2

    Colorful image has three channels (RGB), which can be represented as a tensor with rank = 3.

    There is a temporal dimension for video so it could be represented as a rank 4 tensor.

    An intuitive way to understand: the number of the square brackets equals to the rank of the tensor.

    1. scalar = tf.constant(True)  #A scalar is a rank 0 tensor
    3. print(tf.rank(scalar))
    4. print(scalar.numpy().ndim)  # tf.rank equals to the ndim function in numpy
    1. tf.Tensor(0, shape=(), dtype=int32)
    2. 0
    1. vector = tf.constant([1.0,2.0,3.0,4.0]) #A vector is a rank 1 tensor
    3. print(tf.rank(vector))
    4. print(np.ndim(vector.numpy()))
    1. tf.Tensor(1, shape=(), dtype=int32)
    2. 1
    1. matrix = tf.constant([[1.0,2.0],[3.0,4.0]]) #A matrix is a rank 2 tensor
    3. print(tf.rank(matrix).numpy())
    4. print(np.ndim(matrix))
    1. 2
    2. 2
    1. tf.Tensor(
    2. [[[1. 2.]
    3.   [3. 4.]]
    6. tf.Tensor(3, shape=(), dtype=int32)
    1. tensor4 = tf.constant([[[[1.0,1.0],[2.0,2.0]],[[3.0,3.0],[4.0,4.0]]],
    2.                         [[[5.0,5.0],[6.0,6.0]],[[7.0,7.0],[8.0,8.0]]]])  # A rank 4 tensor
    3. print(tensor4)
    4. print(tf.rank(tensor4))
    1. tf.Tensor(
    2. [[[[1. 1.]
    3.    [2. 2.]]
    5.   [[3. 3.]
    6.    [4. 4.]]]
    9.  [[[5. 5.]
    10.    [6. 6.]]
    12.   [[7. 7.]
    13.    [8. 8.]]]], shape=(2, 2, 2, 2), dtype=float32)
    14. tf.Tensor(4, shape=(), dtype=int32)

    The method numpy() is for converting the data type from tensor to numpy array.

    The method shape is for checking up the size of tensor.

    1. h = tf.constant([123,456],dtype = tf.int32)
    2. f = tf.cast(h,tf.float32)
    3. print(h.dtype, f.dtype)
    1. <dtype: 'int32'> <dtype: 'float32'>
    1. y = tf.constant([[1.0,2.0],[3.0,4.0]])
    2. print(y.numpy()) #Convert to np.array
    3. print(y.shape)
    1. [[1. 2.]
    2. (2, 2)
    1. b'\xe4\xbd\xa0\xe5\xa5\xbd \xe4\xb8\x96\xe7\x95\x8c'
    2. Hello World

    2. Variable Tensor

    The trainable parameters in the models are usually defined as variables.

    1. # The value of a constant is NOT changeable. Re-assignment creates a new space in the memory.
    2. c = tf.constant([1.0,2.0])
    3. print(c)
    4. print(id(c))
    5. c = c + tf.constant([1.0,1.0])
    6. print(c)
    7. print(id(c))
    1. tf.Tensor([1. 2.], shape=(2,), dtype=float32)
    2. 5276289568
    3. tf.Tensor([2. 3.], shape=(2,), dtype=float32)
    4. 5276290240
    1. # The value of a variable is changeable through re-assigning methods such as assign, assign_add, etc.
    2. v = tf.Variable([1.0,2.0],name = "v")
    3. print(v)
    4. print(id(v))
    5. v.assign_add([1.0,1.0])
    6. print(v)
    7. print(id(v))
    1. <tf.Variable 'v:0' shape=(2,) dtype=float32, numpy=array([1., 2.], dtype=float32)>
    2. 5276259888
    3. <tf.Variable 'v:0' shape=(2,) dtype=float32, numpy=array([2., 3.], dtype=float32)>
    4. 5276259888

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