mxnet.test_utils¶

Tools for testing.

Functions

`almost_equal`(a, b[, rtol, atol, equal_nan])	Test if two numpy arrays are almost equal.
`almost_equal_ignore_nan`(a, b[, rtol, atol])	Test that two NumPy arrays are almost equal (ignoring NaN in either array).
`assert_almost_equal`(a, b[, rtol, atol, …])	Test that two numpy arrays are almost equal.
`assert_almost_equal_ignore_nan`(a, b[, rtol, …])	Test that two NumPy arrays are almost equal (ignoring NaN in either array).
`assert_exception`(f, exception_type, *args, …)	Test that function f will throw an exception of type given by exception_type
`assign_each`(the_input, function)	Return ndarray composed of passing each array value through some function
`assign_each2`(input1, input2, function)	Return ndarray composed of passing two array values through some function
`check_consistency`(sym, ctx_list[, scale, …])	Check symbol gives the same output for different running context
`check_numeric_gradient`(sym, location[, …])	Verify an operation by checking backward pass via finite difference method.
`check_speed`(sym[, location, ctx, N, …])	Check the running speed of a symbol.
`check_symbolic_backward`(sym, location, …)	Compares a symbol’s backward results with the expected ones.
`check_symbolic_forward`(sym, location, expected)	Compares a symbol’s forward results with the expected ones.
`chi_square_check`(generator, buckets, probs)	Run the chi-square test for the generator.
`create_sparse_array`(shape, stype[, …])	Create a sparse array, For Rsp, assure indices are in a canonical format
`create_sparse_array_zd`(shape, stype, density)	Create sparse array, using only rsp_indices to determine density
`default_context`()	Get default context for regression test.
`default_dtype`()	Get default data type for regression test.
`discard_stderr`()	Discards error output of a routine if invoked as:
`download`(url[, fname, dirname, overwrite, …])	Download an given URL
`find_max_violation`(a, b[, rtol, atol])	Finds and returns the location of maximum violation.
`gen_buckets_probs_with_ppf`(ppf, nbuckets)	Generate the buckets and probabilities for chi_square test when the ppf (Quantile function)
`get_atol`([atol])	Get default numerical threshold for regression test.
`get_bz2_data`(data_dir, data_name, url, …)	Download and extract bz2 data.
`get_cifar10`()	Downloads CIFAR10 dataset into a directory in the current directory with the name data, and then extracts all files into the directory data/cifar.
`get_im2rec_path`([home_env])	Get path to the im2rec.py tool
`get_mnist`()	Download and load the MNIST dataset
`get_mnist_iterator`(batch_size, input_shape)	Returns training and validation iterators for MNIST dataset
`get_mnist_pkl`()	Downloads MNIST dataset as a pkl.gz into a directory in the current directory with the name data
`get_mnist_ubyte`()	Downloads ubyte version of the MNIST dataset into a directory in the current directory with the name data and extracts all files in the zip archive to this directory.
`get_rtol`([rtol])	Get default numerical threshold for regression test.
`get_zip_data`(data_dir, url, data_origin_name)	Download and extract zip data.
`list_gpus`()	Return a list of GPUs
`mean_check`(generator, mu, sigma[, nsamples])	Test the generator by matching the mean.
`np_reduce`(dat, axis, keepdims, numpy_reduce_func)	Compatible reduce for old version of NumPy.
`numeric_grad`(executor, location[, …])	Calculates a numeric gradient via finite difference method.
`rand_ndarray`(shape[, stype, density, dtype, …])
`rand_shape_2d`([dim0, dim1])
`rand_shape_3d`([dim0, dim1, dim2])
`rand_shape_nd`(num_dim[, dim])
`rand_sparse_ndarray`(shape, stype[, density, …])	Generate a random sparse ndarray.
`random_arrays`(*shapes)	Generate some random numpy arrays.
`random_sample`(population, k)	Return a k length list of the elements chosen from the population sequence.
`retry`(n)	Retry n times before failing for stochastic test cases.
`same`(a, b)	Test if two NumPy arrays are the same.
`same_array`(array1, array2)	Check whether two NDArrays sharing the same memory block
`set_default_context`(ctx)	Set default context.
`set_env_var`(key, val[, default_val])	Set environment variable
`shuffle_csr_column_indices`(csr)	Shuffle CSR column indices per row This allows validation of unordered column indices, which is not a requirement for a valid CSR matrix
`simple_forward`(sym[, ctx, is_train])	A simple forward function for a symbol.
`var_check`(generator, sigma[, nsamples])	Test the generator by matching the variance.
`verify_generator`(generator, buckets, probs)	Verify whether the generator is correct using chi-square testing.

Classes

DummyIter(real_iter)

A dummy iterator that always returns the same batch of data (the first data batch of the real data iter).