I have a 1k rows and 14 columns dataframe containing numpy arrays like shown below.
Here a subset of 2 rows and 3 columns :
[5,4,74,-12] [ 78,1,2,-9] [5 ,1,1,2]
[10,4,4,-1] [ 8,15,21,-19] [1,1,0,0]
where each cell is a numpy array of shape (4,1).
I couldn't find the right placeholder to input my whole dataframe as it needs to be processed by row batches.
Could anyone have an idea ?
I tried this to find the proper placeholder for my dataframe but its not correct:
x = tf.placeholder(tf.int32,[None,14],name='x')
with tf.Session() as sess:
print(sess.run(x,feed_dict={x:Data}))
It gives ValueError: setting an array element with a sequence.
Does anyone have an idea please ?
You did not specify in which format your data is available, so I assume it is a numpy array. In this case, you can do it like this:
n_columns = 14
n_elements_per_column = 4
x = tf.placeholder(tf.int32, [None, n_columns, n_elements_per_column], name='x')
with tf.Session() as sess:
print(sess.run(x,feed_dict={x:Data}))
Related
I have a situation similar to the following:
import numpy as np
a = np.random.rand(55, 1, 3)
b = np.random.rand(55, 626, 3)
Here the shapes represent the number of observations, then the number of time slices per observation, then the number of dimensions of the observation at the given time slice. So b is a full representation of 3 dimensions for each of the 55 observations at one new time interval.
I'd like to stack a and b into an array with shape 55, 627, 3. How can one accomplish this in numpy? Any suggestions would be greatly appreciated!
To follow up on Divakar's answer above, the axis argument in numpy is the index of a given dimension within an array's shape. Here I want to stack a and b by virtue of their middle shape value, which is at index = 1:
import numpy as np
a = np.random.rand(5, 1, 3)
b = np.random.rand(5, 100, 3)
# create the desired result shape: 55, 627, 3
stacked = np.concatenate((b, a), axis=1)
# validate that a was appended to the end of b
print(stacked[:, -1, :], '\n\n\n', a.squeeze())
This returns:
[[0.72598529 0.99395887 0.21811998]
[0.9833895 0.465955 0.29518207]
[0.38914048 0.61633291 0.0132326 ]
[0.05986115 0.81354865 0.43589306]
[0.17706517 0.94801426 0.4567973 ]]
[[0.72598529 0.99395887 0.21811998]
[0.9833895 0.465955 0.29518207]
[0.38914048 0.61633291 0.0132326 ]
[0.05986115 0.81354865 0.43589306]
[0.17706517 0.94801426 0.4567973 ]]
A purist might use instead np.all(stacked[:, -1, :] == a.squeeze()) to validate this equivalence. All glory to #Divakar!
Strictly for the curious, the use case for this concatenation is a kind of wonky data preparation pipeline for a Long Short Term Memory Neural Network. In that kind of network, the training data shape should be number_of_observations, number_of_time_intervals, number_of_dimensions_per_observation. I am generating new predictions of each object at a new time interval, so those predictions have shape number_of_observations, 1, number_of_dimensions_per_observation. To visualize the sequence of observations' positions over time, I want to add the new positions to the array of previous positions, hence the question above.
I know that numpy array has a method called shape that returns [No.of rows, No.of columns], and shape[0] gives you the number of rows, shape[1] gives you the number of columns.
a = numpy.array([[1,2,3,4], [2,3,4,5]])
a.shape
>> [2,4]
a.shape[0]
>> 2
a.shape[1]
>> 4
However, if my array only have one row, then it returns [No.of columns, ]. And shape[1] will be out of the index. For example
a = numpy.array([1,2,3,4])
a.shape
>> [4,]
a.shape[0]
>> 4 //this is the number of column
a.shape[1]
>> Error out of index
Now how do I get the number of rows of an numpy array if the array may have only one row?
Thank you
The concept of rows and columns applies when you have a 2D array. However, the array numpy.array([1,2,3,4]) is a 1D array and so has only one dimension, therefore shape rightly returns a single valued iterable.
For a 2D version of the same array, consider the following instead:
>>> a = numpy.array([[1,2,3,4]]) # notice the extra square braces
>>> a.shape
(1, 4)
Rather then converting this to a 2d array, which may not be an option every time - one could either check the len() of the tuple returned by shape or just check for an index error as such:
import numpy
a = numpy.array([1,2,3,4])
print(a.shape)
# (4,)
print(a.shape[0])
try:
print(a.shape[1])
except IndexError:
print("only 1 column")
Or you could just try and assign this to a variable for later use (or return or what have you) if you know you will only have 1 or 2 dimension shapes:
try:
shape = (a.shape[0], a.shape[1])
except IndexError:
shape = (1, a.shape[0])
print(shape)
I'm aware similar questions have been asked before, and I've tried everything suggested in them, but I'm still stumped. I have a dataset with 2 columns: The first with vectors representing words stored as a 1x10000 sparse csr matrix (so a matrix in each cell), and the second contains integer ratings which I will use for classification. When I run the following code
for index, row in data.iterrows():
print(row)
print(row[0].shape)
I get the correct output for all the rows
Name: 0, dtype: object
(1, 10000)
Vector (0, 0)\t1.0\n (0, 1)\t1.0\n (0, 2)\t1.0\n ...
Rating 5
Now when I try passing my data in any SKlearn classifier like so:
uniform_random_classifier = DummyClassifier(strategy='uniform')
uniform_random_classifier.fit(data["Vectors"], data["Ratings"])
I get the following error:
array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: setting an array element with a sequence.
What am I doing wrong? I've made sure all my sparse matrices are the same size and I've tried reshaping my data in various ways, but with no luck, and the Sklearn classifiers are supposed to be able to deal with csr matrices.
Update: Converting the entire "Vectors" column into one large 2-D matrix did the trick, but for completeness sake the following is the code I used to generate my dataframe if anyone is curious and wants to try solving the original issue. Assume data is a pandas dataframe with rows that look like
"560 420 222" 5.0
"2345 2344 2344 5" 3.0
def vectorize(feature, size):
"""Given a numeric string generated from a vocabulary table return a binary vector representation of
each feature"""
vector = sparse.lil_matrix((1, size))
for number in feature.split(' '):
try:
vector[0, int(number) - 1] = 1
except ValueError:
pass
return vector
def vectorize_dataset(data, vectorize, size):
"""Given a dataset in the appropriate "num num num..." format, a specific vectorization format, and a vector size,
returns the dataset in vectorized form"""
result_data = pd.DataFrame(index=range(data.shape[0]), columns=["Vector", "Rating"])
for index, row in data.iterrows():
# All the mixing up of decodings and encoding has made it so that Pandas incorrectly parses EOF chars
if type(row[0]) == type('str'):
result_data.iat[index, 0] = vectorize(row[0], size).tocsr()
result_data.iat[index, 1] = data.loc[index][1]
return result_data
Hi tensorflow beginner here... I'm trying to get the value of a certain elements in an 2 dim tensor, in my case class scores from a probability matrix.
The probability matrix is (1000,81) with batchsize 1000 and number of classes 81. ClassIDs is (1000,) and contains the index for the highest class score for each sample. How do I get the corresponding class score from the probability matrix using tf.gather?
class_ids = tf.cast(tf.argmax(probs, axis=1), tf.int32)
class_scores = tf.gather_nd(probs,class_ids)
class_scores should be a tensor of shape (1000,) containing the highest class_score for each sample.
Right now I'm using a workaround that looks like this:
class_score_count = []
for i in range(probs.shape[0]):
prob = probs[i,:]
class_score = prob[class_ids[i]]
class_score_count.append(class_score)
class_scores = tf.stack(class_score_count, axis=0)
Thanks for the help!
You can do it with tf.gather_nd like this:
class_ids = tf.cast(tf.argmax(probs, axis=1), tf.int32)
# If shape is not dynamic you can use probs.shape[0].value instead of tf.shape(probs)[0]
row_ids = tf.range(tf.shape(probs)[0], dtype=tf.int32)
idx = tf.stack([row_ids, class_ids], axis=1)
class_scores = tf.gather_nd(probs, idx)
You could also just use tf.reduce_max, even though it would actually compute the maximum again it may not be much slower if your data is not too big:
class_scores = tf.reduce_max(probs, axis=1)
you need to run the tensor class_ids to get the values
the values will be a bumpy array
you can access numpy array normally by a loop
you have to do something like this :
predictions = sess.run(tf.argmax(probs, 1), feed_dict={x: X_data})
predictions variable has all the information you need
tensorflow only returns those tensor values which you run explicitly
I think this is what the batch_dims argument for tf.gather is for.
I would like to choose only particular columns in each row of a tensor, using it for an RNN
seq_len=[11,12,20,30] #This is the sequence length, assume 4 sequences
array=tf.ones([4,30]) #Assuming this is the array I want to index from
function(array,seq_len) #apply required function
Output=(first 11 elements from row 0, first 12 from row 2, first 20 from row 3 etc), perhaps obtained as a flat tensor
You can use tf.sequence_mask and tf.boolean_mask to get them flattened:
mask = tf.sequence_mask(seq_len, MAX_LENGTH) # Replace MAX_LENGTH with the size of array on the right dimension, 30 in your case
output= tf.boolean_mask(array, mask=mask)
A tensor in tensorflow can be sliced just like a numpy array and then concatenated into one tensor. Assuming you measure the sequence length from the first element.
Use [row_idx,column_idx] to slice the tensor. slice = array[0,:] would assign the first row to slice.
flat_slices = tf.concat([slice,slice]) will flatten them into one tensor.
import tensorflow as tf
seq_len = [11,12,20,30]
array = tf.ones([4,30])
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
flatten = array[0,:seq_len[0]]
for i in range(1,len(seq_len)):
row = array[i,:seq_len[i]]
flatten = tf.concat([flatten, row])
print(sess.run(flatten))