I am confused about rank and shape concept of TensorFlow. I have read the details from here and did run some code to clear my concept about them. But I am still confused and need help to understand.
x = tf.placeholder(tf.float32, shape=[2, 12])
print(x.get_shape()) # ==> (2, 12)
print(x[0, :].get_shape()) # ==> (12,)
print(x[1, :].get_shape()) # ==> (12,)
print(x[2, :].get_shape()) # ==> (12,)
print(x[120, :].get_shape()) # ==> (12,)
I thought x is like a 2d matrix where 2 is number of rows and 12 is number of columns. Then why I am getting shape for x[120, :] as (12, )? How even x[120, :] is possible with the given shape?
Besides, since I thought x is a 2D tensor, its rank is also 2 because dimension and rank is the same thing for tensors (according to my understanding). But when I run:
print(x[0].get_shape())
I am getting this error:
Shape (2, 12) must have rank 1
It means my understanding is wrong about rank and dimension. What I am missing about rank and dimensions? Is rank and dimension two different things? How the rank of tensor x in the above example is 1? How can I set the rank of a tensor? Can anyone explain in details with some comprehensive examples?
I find the link you provide very clear.
The rank of a tensor is the number of dimensions it has
a matrix has 2 dimensions, so its rank is 2
a colored image has 3 dimensions [height, width, 3] so its rank is 3
The shape of a tensor is the detailed number of components in each dimension
a matrix has 2 dimensions, rank 2 and can have a shape like [6, 10], where 6 is the number of rows and 10 the number of columns
a 200x200 colored image (of rank 3) will have a shape [200, 200, 3]
For your examples, x[120, :] is possible to write because TensorFlow is not checking yet if 120 is a valid index. When you create your session and run the code, you will find an error:
res = x[120, :]
with tf.Session():
sess.run(res, feed_dict={x: np.zeros((2, 12))})
InvalidArgumentError: slice index 120 of dimension 0 out of bounds.
As said in my comment, x[0] should work with the latest version of TensorFlow and it should give a tensor of shape (12,), and rank 1.
After some analysis, I found that while declaring tensor the number of square braces used gives the rank of a tensor. And the number of values in a shape is equal to the rank. For example:
a1=tf.constant(
[
[
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5]
]
]
)
Here, I used 3 square braces. So the rank is 3. Also the shape would be in the format (x,y,z) since the rank is 3.
Now the values of x,y and z:
x= Number of commas in the first brace plus 1
so x=0+1=1
similarly
y=3+1=4
z=4+1=5
Finally the rank is 3 and shape is (1,4,5)
Related
My df shape is 2D (6764, 11).
I want to reshape it into 3D with 1691 time steps (i.e., 1/4 of 6764)
df = df.values.reshape((df.shape[0], 1691, df.shape[1]))
I get the error: ValueError: cannot reshape array of size 74404 into shape (6764,1691,11)
Why I get size 74404??? I get is 1674*11, but why is doing this multiplication?
edit
I actually want to reshape my data into [6764, 1691, 11], which is the dimension required for an LSTM model. This dimension stands for [Samples, TimeSteps, Features] where samples are the number of data points, time steps the number of data points I want to analyse/predict, and 11 the inputs (columns) I am using. Any advise on how to achieve this shape without getting the error ? my reference is this
From 2D dataframe you have an array of 6764 x 11 = 74404 values.
Multiplication indicates the number of values you have in the array/dataframe.
from your code (df.shape[0], 1691, df10.shape[1])) it would generate 6764 x 1691x 11 = 125817164 which is not matching to input array values thats why you are getting an error.
Considering you want 1691 series you can reshape your data into (1691 x 4 x 11)
df = df.values.reshape((1691,4, df.shape[1]))
If you need only 1st column that is 6764 values to reshape then use below code although it will generate 2D array with (1691,4) shape.
df = df['column_name'].values.reshape((1691,4))
I don't understand broadcasting. The documentation explains the rules of broadcasting but doesn't seem to define it in English. My guess is that broadcasting is when NumPy fills a smaller dimensional array with dummy data in order to perform an operation. But this doesn't work:
>>> x = np.array([1,3,5])
>>> y = np.array([2,4])
>>> x+y
*** ValueError: operands could not be broadcast together with shapes (3,) (2,)
The error message hints that I'm on the right track, though. Can someone define broadcasting and then provide some simple examples of when it works and when it doesn't?
The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations.
It's basically a way numpy can expand the domain of operations over arrays.
The only requirement for broadcasting is a way aligning array dimensions such that either:
Aligned dimensions are equal.
One of the aligned dimensions is 1.
So, for example if:
x = np.ndarray(shape=(4,1,3))
y = np.ndarray(shape=(3,3))
You could not align x and y like so:
4 x 1 x 3
3 x 3
But you could like so:
4 x 1 x 3
3 x 3
How would an operation like this result?
Suppose we have:
x = np.ndarray(shape=(1,3), buffer=np.array([1,2,3]),dtype='int')
array([[1, 2, 3]])
y = np.ndarray(shape=(3,3), buffer=np.array([1,1,1,1,1,1,1,1,1]),dtype='int')
array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
The operation x + y would result in:
array([[2, 3, 4],
[2, 3, 4],
[2, 3, 4]])
I hope you caught the drift. If you did not, you can always check the official documentation here.
Cheers!
1.What is Broadcasting?
Broadcasting is a Tensor operation. Helpful in Neural Network (ML, AI)
2.What is the use of Broadcasting?
Without Broadcasting addition of only identical Dimension(shape) Tensors is supported.
Broadcasting Provide us the Flexibility to add two Tensors of Different Dimension.
for Example: adding a 2D Tensor with a 1D Tensor is not possible without broadcasting see the image explaining Broadcasting pictorially
Run the Python example code understand the concept
x = np.array([1,3,5,6,7,8])
y = np.array([2,4,5])
X=x.reshape(2,3)
x is reshaped to get a 2D Tensor X of shape (2,3), and adding this 2D Tensor X with 1D Tensor y of shape(1,3) to get a 2D Tensor z of shape(2,3)
print("X =",X)
print("\n y =",y)
z=X+y
print("X + y =",z)
You are almost correct about smaller Tensor, no ambiguity, the smaller tensor will be broadcasted to match the shape of the larger tensor.(Small vector is repeated but not filled with Dummy Data or Zeros to Match the Shape of larger).
3. How broadcasting happens?
Broadcasting consists of two steps:
1 Broadcast axes are added to the smaller tensor to match the ndim of
the larger tensor.
2 The smaller tensor is repeated alongside these new axes to match the full shape
of the larger tensor.
4. Why Broadcasting not happening in your code?
your code is working but Broadcasting can not happen here because both Tensors are different in shape but Identical in Dimensional(1D).
Broadcasting occurs when dimensions are nonidentical.
what you need to do is change Dimension of one of the Tensor, you will experience Broadcasting.
5. Going in Depth.
Broadcasting(repetition of smaller Tensor) occurs along broadcast axes but since both the Tensors are 1 Dimensional there is no broadcast Axis.
Don't Confuse Tensor Dimension with the shape of tensor,
Tensor Dimensions are not same as Matrices Dimension.
Broadcasting is numpy trying to be smart when you tell it to perform an operation on arrays that aren't the same dimension. For example:
2 + np.array([1,3,5]) == np.array([3, 5, 7])
Here it decided you wanted to apply the operation using the lower dimensional array (0-D) on each item in the higher-dimensional array (1-D).
You can also add a 0-D array (scalar) or 1-D array to a 2-D array. In the first case, you just add the scalar to all items in the 2-D array, as before. In the second case, numpy will add row-wise:
In [34]: np.array([1,2]) + np.array([[3,4],[5,6]])
Out[34]:
array([[4, 6],
[6, 8]])
There are ways to tell numpy to apply the operation along a different axis as well. This can be taken even further with applying an operation between a 3-D array and a 1-D, 2-D, or 0-D array.
>>> x = np.array([1,3,5])
>>> y = np.array([2,4])
>>> x+y
*** ValueError: operands could not be broadcast together with shapes (3,) (2,)
Broadcasting is how numpy do math operations with array of different shapes. Shapes are the format the array has, for example the array you used, x , has 3 elements of 1 dimension; y has 2 elements and 1 dimension.
To perform broadcasting there are 2 rules:
1) Array have the same dimensions(shape) or
2)The dimension that doesn't match equals one.
for example x has shape(2,3) [or 2 lines and 3 columns];
y has shape(2,1) [or 2 lines and 1 column]
Can you add them? x + y?
Answer: Yes, because the mismatched dimension is equal to 1 (the column in y). If y had shape(2,4) broadcasting would not be possible, because the mismatched dimension is not 1.
In the case you posted:
operands could not be broadcast together with shapes (3,) (2,);
it is because 3 and 2 mismatched altough both have 1 line.
I would like to suggest to try the np.broadcast_arrays, run some demos may give intuitive ideas. Official Document is also helpful. From my current understanding, numpy will compare the dimension from tail to head. If one dim is 1, it will broadcast in the dimension, if one array has more axes, such (256*256*3) multiply (1,), you can view (1) as (1,1,1). And broadcast will make (256,256,3).
I am following a tutorial to implement the K-nearest Neighbor algorithm on a dataset.
I have an array of shape (6003,) and I want to do this:
data = data.reshape((data.shape[0], 3072))
However, I am getting this error:
cannot reshape array of size 6003 into shape (6003,3072)
Any help on this, please? Thanks!
when you reshape a numpy array the total number elements shouldn't change.
e.g. a =[2,3,4,5,1,7] if you want to reshape this to a 2Darray then the dimensions multiplied should be equal to the total number elements in the original array a.
this means you can reshape array a in to dimension of (1,6) (2,3),(6,1),(3,2).
the title of your question does give away the error by the way.
Reshaping array of shape (x,) into an array of shape (x,y)
is impossible because you are trying to add more elements into your original data.
an array of shape (x,) can only be reshaped into an array of shape (x/y,y)
I hope this helps.
You are trying to reshape into an incompatible shape. Now, what do I mean by that? Look at this example:
a = np.array([[1, 2, 3],
[4, 5, 6],
])
The shape of this array is:
a.shape
>> (2, 3)
Array a has 2 x 3 = 6 elements. Let's try to reshape it into a (2, 6) array
a.reshape(2, 6)
This raises
>> ValueError: cannot reshape array of size 6 into shape (2,6)
Notice that we were trying to make an array that has 2 x 3 = 6 elements into an array that would have 2 x 6 = 12 elements. But NumPy cannot add those extra elements into your original array and give that your desired shape. So it raises ValueError.
In your case, you are trying to make an array with 6003 elements into an array that will have 6003 x 3072 = 18441216 elements!
I would like to replace values under condition.
NumPy version would go like this
intensity=np.where(
np.abs(intensity)<1e-4,
1e-4,
intensity)
But TensorFlow has a bit different usage for tf.where()
When I tried this
intensity=tf.where(
tf.math.abs(intensity)<1e-4,
1e-4,
intensity)
I got this error
ValueError: Shapes must be equal rank, but are 0 and 4 for 'Select' (op: 'Select') with input shapes: [?,512,512,1], [], [?,512,512,1].
Does that mean I should 4 dimensional tensor for 1e-4?
Following code passed the error
# Create an array which has small value (1e-4),
# whose shape is (2,512,512,1)
small_val=np.full((2,512,512,1),1e-4).astype("float32")
# Convert numpy array to tf.constant
small_val=tf.constant(small_val)
# Use tf.where()
intensity=tf.where(
tf.math.abs(intensity)<1e-4,
small_val,
intensity)
# Error doesn't occur
print(intensity.shape)
# (2, 512, 512, 1)
I don't understand broadcasting. The documentation explains the rules of broadcasting but doesn't seem to define it in English. My guess is that broadcasting is when NumPy fills a smaller dimensional array with dummy data in order to perform an operation. But this doesn't work:
>>> x = np.array([1,3,5])
>>> y = np.array([2,4])
>>> x+y
*** ValueError: operands could not be broadcast together with shapes (3,) (2,)
The error message hints that I'm on the right track, though. Can someone define broadcasting and then provide some simple examples of when it works and when it doesn't?
The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations.
It's basically a way numpy can expand the domain of operations over arrays.
The only requirement for broadcasting is a way aligning array dimensions such that either:
Aligned dimensions are equal.
One of the aligned dimensions is 1.
So, for example if:
x = np.ndarray(shape=(4,1,3))
y = np.ndarray(shape=(3,3))
You could not align x and y like so:
4 x 1 x 3
3 x 3
But you could like so:
4 x 1 x 3
3 x 3
How would an operation like this result?
Suppose we have:
x = np.ndarray(shape=(1,3), buffer=np.array([1,2,3]),dtype='int')
array([[1, 2, 3]])
y = np.ndarray(shape=(3,3), buffer=np.array([1,1,1,1,1,1,1,1,1]),dtype='int')
array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
The operation x + y would result in:
array([[2, 3, 4],
[2, 3, 4],
[2, 3, 4]])
I hope you caught the drift. If you did not, you can always check the official documentation here.
Cheers!
1.What is Broadcasting?
Broadcasting is a Tensor operation. Helpful in Neural Network (ML, AI)
2.What is the use of Broadcasting?
Without Broadcasting addition of only identical Dimension(shape) Tensors is supported.
Broadcasting Provide us the Flexibility to add two Tensors of Different Dimension.
for Example: adding a 2D Tensor with a 1D Tensor is not possible without broadcasting see the image explaining Broadcasting pictorially
Run the Python example code understand the concept
x = np.array([1,3,5,6,7,8])
y = np.array([2,4,5])
X=x.reshape(2,3)
x is reshaped to get a 2D Tensor X of shape (2,3), and adding this 2D Tensor X with 1D Tensor y of shape(1,3) to get a 2D Tensor z of shape(2,3)
print("X =",X)
print("\n y =",y)
z=X+y
print("X + y =",z)
You are almost correct about smaller Tensor, no ambiguity, the smaller tensor will be broadcasted to match the shape of the larger tensor.(Small vector is repeated but not filled with Dummy Data or Zeros to Match the Shape of larger).
3. How broadcasting happens?
Broadcasting consists of two steps:
1 Broadcast axes are added to the smaller tensor to match the ndim of
the larger tensor.
2 The smaller tensor is repeated alongside these new axes to match the full shape
of the larger tensor.
4. Why Broadcasting not happening in your code?
your code is working but Broadcasting can not happen here because both Tensors are different in shape but Identical in Dimensional(1D).
Broadcasting occurs when dimensions are nonidentical.
what you need to do is change Dimension of one of the Tensor, you will experience Broadcasting.
5. Going in Depth.
Broadcasting(repetition of smaller Tensor) occurs along broadcast axes but since both the Tensors are 1 Dimensional there is no broadcast Axis.
Don't Confuse Tensor Dimension with the shape of tensor,
Tensor Dimensions are not same as Matrices Dimension.
Broadcasting is numpy trying to be smart when you tell it to perform an operation on arrays that aren't the same dimension. For example:
2 + np.array([1,3,5]) == np.array([3, 5, 7])
Here it decided you wanted to apply the operation using the lower dimensional array (0-D) on each item in the higher-dimensional array (1-D).
You can also add a 0-D array (scalar) or 1-D array to a 2-D array. In the first case, you just add the scalar to all items in the 2-D array, as before. In the second case, numpy will add row-wise:
In [34]: np.array([1,2]) + np.array([[3,4],[5,6]])
Out[34]:
array([[4, 6],
[6, 8]])
There are ways to tell numpy to apply the operation along a different axis as well. This can be taken even further with applying an operation between a 3-D array and a 1-D, 2-D, or 0-D array.
>>> x = np.array([1,3,5])
>>> y = np.array([2,4])
>>> x+y
*** ValueError: operands could not be broadcast together with shapes (3,) (2,)
Broadcasting is how numpy do math operations with array of different shapes. Shapes are the format the array has, for example the array you used, x , has 3 elements of 1 dimension; y has 2 elements and 1 dimension.
To perform broadcasting there are 2 rules:
1) Array have the same dimensions(shape) or
2)The dimension that doesn't match equals one.
for example x has shape(2,3) [or 2 lines and 3 columns];
y has shape(2,1) [or 2 lines and 1 column]
Can you add them? x + y?
Answer: Yes, because the mismatched dimension is equal to 1 (the column in y). If y had shape(2,4) broadcasting would not be possible, because the mismatched dimension is not 1.
In the case you posted:
operands could not be broadcast together with shapes (3,) (2,);
it is because 3 and 2 mismatched altough both have 1 line.
I would like to suggest to try the np.broadcast_arrays, run some demos may give intuitive ideas. Official Document is also helpful. From my current understanding, numpy will compare the dimension from tail to head. If one dim is 1, it will broadcast in the dimension, if one array has more axes, such (256*256*3) multiply (1,), you can view (1) as (1,1,1). And broadcast will make (256,256,3).