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ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 3

I am getting the error ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 3 and the array at index 1 has size 1 while running the below code.
for i in range(6):
print('current b', current_batch)
current_pred = model.predict(current_batch)[0]
print('current pred', current_pred)
test_predictions.append(current_pred)
print('current batch', current_batch)
print('current batch => ', current_batch[:,1:,:])
current_batch = np.append(current_batch[:,1:,:], [[current_pred]], axis=1)
getting this error
Can anyone please explain me why this is happening.
Thanks,

Basically, Numpy is telling you that the shapes of the concatenated matrices should align. For example, it is possible to concatenate a 3x4 matrix with 3x5 matrix so that we get 3x9 matrix (we added dimension 1).
The problem here is that Numpy is telling you that the axis don't align. In my example, that would be trying to concatenate 3x4 matrix with 10x10 matrix. This is not possible as the shapes are not aligned.
This usually means that the you are trying to concatenate wrong things. If you are sure though, try using np.reshape function, which will change the shape of one of the matrices so that they can be concatenated.

As the traceback shows, np.append is actually using np.concatenate. Did you read (study) the docs for either function? Understand what they say about dimensions?
From the display [[current_pred]], converted to array will be (1,1,1) shape. Do you understand that?
current_batch[:,1:,:] is, as best I can tell from the small image (1,5,3)
You are asking to join on axis 1, which is 1 and 5, ok. But it's saying that the last dimension, axis 2, doesn't match. That 1 does not equal 3. Do you understand that?
List append as you do with test_predictions.append(current_pred) works well in an iteration.
np.append does not work well. Even when it works, it is slow. And here it doesn't work, because you aren't taking sufficient care to match dimensions.

Transpose a 1-dimensional array in Numpy without casting to matrix

My goal is to to turn a row vector into a column vector and vice versa. The documentation for numpy.ndarray.transpose says:
For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.)
However, when I try this:
my_array = np.array([1,2,3])
my_array_T = np.transpose(np.matrix(myArray))
I do get the wanted result, albeit in matrix form (matrix([[66],[640],[44]])), but I also get this warning:
PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
my_array_T = np.transpose(np.matrix(my_array))
How can I properly transpose an ndarray then?

A 1D array is itself once transposed, contrary to Matlab where a 1D array doesn't exist and is at least 2D.
What you want is to reshape it:
my_array.reshape(-1, 1)
Or:
my_array.reshape(1, -1)
Depending on what kind of vector you want (column or row vector).
The -1 is a broadcast-like, using all possible elements, and the 1 creates the second required dimension.

If your array is my_array and you want to convert it to a column vector you can do:
my_array.reshape(-1, 1)
For a row vector you can use
my_array.reshape(1, -1)
Both of these can also be transposed and that would work as expected.

IIUC, use reshape
my_array.reshape(my_array.size, -1)

Convert list of NDarrays to dataframe

I'm trying to convert a list ND arrays to a dataframe in order to do a Isomap on it. But this doesn't convert. Anyone how to convert in such that I can do an Isomap on it?
#Creation and filling of list samples*
samples = list()
for i in range(72):
img =misc.imread('Datasets/ALOI/32/32_r'+str(i*5)+'.png' )
samples.append(img)
...
df = pd.DataFrame(samples) #This doesn't work gives
#ValueError: Must pass 2-d input*
...
iso = manifold.Isomap(n_neighbors=4, n_components=3)
iso.fit(df) #The end goal of my DataFrame

That is obvious, isn't it? All images are 2D data, rows and columns. Stacking them in a list causes it to gain a third dimension. DataFrames are by nature 2D. Hence the error.
You have 2 possible fixes:
Create a Panel.
wp = pd.Panel.from_dict(zip(samples, [str(i*5) for i in range(72)]))
Stack your arrays one on top of the other, or side by side:
# On top of another:
df = pd.concat([pd.DataFrame(sample) for sample in samples], axis=0,
keys=[str(i*5) for i in range(72)])
# Side by side:
df = pd.concat([pd.DataFrame(sample) for sample in samples], axis=1,
keys=[str(i*5) for i in range(72)])

Another way to do it is to convert your 2D arrays (images) to 1D arrays (that are expected by sklearn) using the reshape method on the images:
for i in range(yourRange):
img = misc.imread(yourFile)
samples.append(img.reshape(-1))
df = pd.DataFrame(samples)

Olivera almost had it.
the problem
When you run misc.imread, the output is a NxM (2D) array. Putting this in a list, makes it 3D. DataFrame expects a 2D input.
the fix
Before it goes in the list, the array should be 'flattened' using ravel:
img =misc.imread('Datasets/ALOI/32/32_r'+str(i*5)+'.png' ).ravel()
why .reshape(-1) doesn't work
Reshaping the array preserves the array's rank. Instead of converting it to an Nx1 array, you want it to be Nx(nothing), which is what ravel() does.

Multiplying 3D matrix with 2D matrix

I have two matrices to multiply. One is the weight matrix W, whose size is 900x2x2. Another is input matrix I, whose size is 2x2.
I want to perform a summation over c = WI which will be a 900x1 matrix, but when I perform the operation it multiplies them and gives me a 900x2x2 matrix again.
Question #2 (related): So I made both of them 2D and multiplied 900x4 * 4x1, but that gives me an error saying:
ValueError: operands could not be broadcast together with shapes (900,4) (4,1)

It seems you are trying to lose the last two axes of the first array against the only two axes of the second weight array with that matrix-multiplication. We could translate that idea into NumPy code with np.tensordot and assuming arr1 and arr2 as the input arrays respectively, like so -
np.tensordot(arr1,arr2,axes=([1,2],[0,1]))
Another simpler way to put into NumPy code would be with np.einsum, like so -
np.einsum('ijk,jk',arr1,arr2)

How to assign a 1D numpy array to 2D numpy array?

Consider the following simple example:
X = numpy.zeros([10, 4]) # 2D array
x = numpy.arange(0,10) # 1D array
X[:,0] = x # WORKS
X[:,0:1] = x # returns ERROR:
# ValueError: could not broadcast input array from shape (10) into shape (10,1)
X[:,0:1] = (x.reshape(-1, 1)) # WORKS
Can someone explain why numpy has vectors of shape (N,) rather than (N,1) ?
What is the best way to do the casting from 1D array into 2D array?
Why do I need this?
Because I have a code which inserts result x into a 2D array X and the size of x changes from time to time so I have X[:, idx1:idx2] = x which works if x is 2D too but not if x is 1D.

Do you really need to be able to handle both 1D and 2D inputs with the same function? If you know the input is going to be 1D, use
X[:, i] = x
If you know the input is going to be 2D, use
X[:, start:end] = x
If you don't know the input dimensions, I recommend switching between one line or the other with an if, though there might be some indexing trick I'm not aware of that would handle both identically.
Your x has shape (N,) rather than shape (N, 1) (or (1, N)) because numpy isn't built for just matrix math. ndarrays are n-dimensional; they support efficient, consistent vectorized operations for any non-negative number of dimensions (including 0). While this may occasionally make matrix operations a bit less concise (especially in the case of dot for matrix multiplication), it produces more generally applicable code for when your data is naturally 1-dimensional or 3-, 4-, or n-dimensional.

I think you have the answer already included in your question. Numpy allows the arrays be of any dimensionality (while afaik Matlab prefers two dimensions where possible), so you need to be correct with this (and always distinguish between (n,) and (n,1)). By giving one number as one of the indices (like 0 in 3rd row), you reduce the dimensionality by one. By giving a range as one of the indices (like 0:1 in 4th row), you don't reduce the dimensionality.
Line 3 makes perfect sense for me and I would assign to the 2-D array this way.

Here are two tricks that make the code a little shorter.
X = numpy.zeros([10, 4]) # 2D array
x = numpy.arange(0,10) # 1D array
X.T[:1, :] = x
X[:, 2:3] = x[:, None]