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diff --git a/tensorflow/docs_src/install/migration.md b/tensorflow/docs_src/install/migration.md deleted file mode 100644 index 19315ace2d..0000000000 --- a/tensorflow/docs_src/install/migration.md +++ /dev/null @@ -1,336 +0,0 @@ -# Transition to TensorFlow 1.0 - - -The APIs in TensorFlow 1.0 have changed in ways that are not all backwards -compatible. That is, TensorFlow programs that worked on TensorFlow 0.n won't -necessarily work on TensorFlow 1.0. We have made this API changes to ensure an -internally-consistent API, and do not plan to make backwards-breaking changes -throughout the 1.N lifecycle. - -This guide walks you through the major changes in the API and how to -automatically upgrade your programs for TensorFlow 1.0. This guide not -only steps you through the changes but also explains why we've made them. - -## How to upgrade - -If you would like to automatically port your code to 1.0, you can try our -`tf_upgrade.py` script. While this script handles many cases, manual changes -are sometimes necessary. - Get this script from our -[GitHub tree](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/compatibility). - -To convert a single 0.n TensorFlow source file to 1.0, enter a -command of the following format: - -<pre> -$ <b>python tf_upgrade.py --infile</b> <i>InputFile</i> <b>--outfile</b> <i>OutputFile</i> -</pre> - -For example, the following command converts a 0.n TensorFlow -program named `test.py` to a 1.0 TensorFlow program named `test_1.0.py`: - -<pre> -$ <b>python tf_upgrade.py --infile test.py --outfile test_1.0.py</b> -</pre> - -The `tf_upgrade.py` script also generates a file named `report.txt`, which -details all the changes it performed and makes additional suggestions about -changes you might need to make manually. - -To upgrade a whole directory of 0.n TensorFlow programs to 1.0, -enter a command having the following format: - -<pre> -$ <b>python tf_upgrade.py --intree</b> <i>InputDir</i> <b>--outtree</b> <i>OutputDir</i> -</pre> - -For example, the following command converts all the 0.n TensorFlow programs -in the `/home/user/cool` directory, creating their 1.0 equivalents in -the `/home/user/cool_1.0` directory: - -<pre> -$ <b>python tf_upgrade.py --intree /home/user/cool --outtree /home/user/cool_1.0</b> -</pre> - -### Limitations - -There are a few things to watch out for. Specifically: - - * You must manually fix any instances of `tf.reverse()`. - The `tf_upgrade.py` script will warn you about `tf.reverse()` in - stdout and in the `report.txt` file. - * On reordered arguments, `tf_upgrade.py` tries to minimally reformat - your code, so it cannot automatically change the actual argument order. - Instead, `tf_upgrade.py` makes your function invocations order-independent - by introducing keyword arguments. - * Constructions like `tf.get_variable_scope().reuse_variables()` - will likely not work. We recommend deleting those lines and replacing - them with lines such as the following: - - <pre class="prettyprint"> - with tf.variable_scope(tf.get_variable_scope(), reuse=True): - ... - </pre> - - * Analogously to `tf.pack` and `tf.unpack`, we're renamed - `TensorArray.pack` and `TensorArray.unpack` to - `TensorArray.stack` and `TensorArray.unstack`. However, `TensorArray.pack` - and `TensorArray.unpack` cannot be detected lexically since they are - indirectly related to the `tf` namespace e.g. - `foo = tf.TensorArray(); foo.unpack()` - -## Upgrading your code manually - -Instead of running `tf_upgrade.py`, you may manually upgrade your code. -The remainder of this document provides a comprehensive list of -all backward incompatible changes made in TensorFlow 1.0. - - -### Variables - -Variable functions have been made more consistent and less confusing. - -* `tf.VARIABLES` - * should be renamed to `tf.GLOBAL_VARIABLES` -* `tf.all_variables` - * should be renamed to `tf.global_variables` -* `tf.initialize_all_variables` - * should be renamed to `tf.global_variables_initializer` -* `tf.initialize_local_variables` - * should be renamed to `tf.local_variables_initializer` -* `tf.initialize_variables` - * should be renamed to `tf.variables_initializer` - -### Summary functions - -Summary functions have been consolidated under the `tf.summary` namespace. - -* `tf.audio_summary` - * should be renamed to `tf.summary.audio` -* `tf.contrib.deprecated.histogram_summary` - * should be renamed to `tf.summary.histogram` -* `tf.contrib.deprecated.scalar_summary` - * should be renamed to `tf.summary.scalar` -* `tf.histogram_summary` - * should be renamed to `tf.summary.histogram` -* `tf.image_summary` - * should be renamed to `tf.summary.image` -* `tf.merge_all_summaries` - * should be renamed to `tf.summary.merge_all` -* `tf.merge_summary` - * should be renamed to `tf.summary.merge` -* `tf.scalar_summary` - * should be renamed to `tf.summary.scalar` -* `tf.train.SummaryWriter` - * should be renamed to `tf.summary.FileWriter` - -### Numeric differences - - -Integer division and `tf.floordiv` now uses flooring semantics. This is to -make the results of `np.divide` and `np.mod` consistent with `tf.divide` and -`tf.mod`, respectively. In addition we have changed the rounding algorithm -used by `tf.round` to match NumPy. - - -* `tf.div` - - * The semantics of `tf.divide` division have been changed to match Python -semantics completely. That is, `/` in Python 3 and future division mode in -Python 2 will produce floating point numbers always, `//` will produce floored -division. However, even `tf.div` will produce floored integer division. -To force C-style truncation semantics, you must use `tf.truncatediv`. - - * Consider changing your code to use `tf.divide`, which follows Python semantics for promotion. - -* `tf.mod` - - * The semantics of `tf.mod` have been changed to match Python semantics. In -particular, flooring semantics are used for integers. If you wish to have -C-style truncation mod (remainders), you can use `tf.truncatemod` - - -The old and new behavior of division can be summarized with this table: - -| Expr | TF 0.11 (py2) | TF 0.11 (py3) | TF 1.0 (py2) | TF 1.0 (py3) | -|---------------------|---------------|---------------|--------------|--------------| -| tf.div(3,4) | 0 | 0 | 0 | 0 | -| tf.div(-3,4) | 0 | 0 | -1 | -1 | -| tf.mod(-3,4) | -3 | -3 | 1 | 1 | -| -3/4 | 0 | -0.75 | -1 | -0.75 | -| -3/4tf.divide(-3,4) | N/A | N/A | -0.75 | -1 | - -The old and new behavior of rounding can be summarized with this table: - -| Input | Python | NumPy | C++ round() | TensorFlow 0.11(floor(x+.5)) | TensorFlow 1.0 | -|-------|--------|-------|-------------|------------------------------|----------------| -| -3.5 | -4 | -4 | -4 | -3 | -4 | -| -2.5 | -2 | -2 | -3 | -2 | -2 | -| -1.5 | -2 | -2 | -2 | -1 | -2 | -| -0.5 | 0 | 0 | -1 | 0 | 0 | -| 0.5 | 0 | 0 | 1 | 1 | 0 | -| 1.5 | 2 | 2 | 2 | 2 | 2 | -| 2.5 | 2 | 2 | 3 | 3 | 2 | -| 3.5 | 4 | 4 | 4 | 4 | 4 | - - - -### NumPy matching names - - -Many functions have been renamed to match NumPy. This was done to make the -transition between NumPy and TensorFlow as easy as possible. There are still -numerous cases where functions do not match, so this is far from a hard and -fast rule, but we have removed several commonly noticed inconsistencies. - -* `tf.inv` - * should be renamed to `tf.reciprocal` - * This was done to avoid confusion with NumPy's matrix inverse `np.inv` -* `tf.list_diff` - * should be renamed to `tf.setdiff1d` -* `tf.listdiff` - * should be renamed to `tf.setdiff1d` -* `tf.mul` - * should be renamed to `tf.multiply` -* `tf.neg` - * should be renamed to `tf.negative` -* `tf.select` - * should be renamed to `tf.where` - * `tf.where` now takes 3 arguments or 1 argument, just like `np.where` -* `tf.sub` - * should be renamed to `tf.subtract` - -### NumPy matching arguments - -Arguments for certain TensorFlow 1.0 methods now match arguments in certain -NumPy methods. To achieve this, TensorFlow 1.0 has changed keyword arguments -and reordered some arguments. Notably, TensorFlow 1.0 now uses `axis` rather -than `dimension`. TensorFlow 1.0 aims to keep the tensor argument first on -operations that modify Tensors. (see the `tf.concat` change). - - -* `tf.argmax` - * keyword argument `dimension` should be renamed to `axis` -* `tf.argmin` - * keyword argument `dimension` should be renamed to `axis` -* `tf.concat` - * keyword argument `concat_dim` should be renamed to `axis` - * arguments have been reordered to `tf.concat(values, axis, name='concat')`. -* `tf.count_nonzero` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.expand_dims` - * keyword argument `dim` should be renamed to `axis` -* `tf.reduce_all` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_any` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_join` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_logsumexp` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_max` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_mean` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_min` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_prod` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reduce_sum` - * keyword argument `reduction_indices` should be renamed to `axis` -* `tf.reverse` - * `tf.reverse` used to take a 1D `bool` tensor to control which dimensions were reversed. Now we use a Tensor of axis indices. - * For example `tf.reverse(a, [True, False, True])` now must be `tf.reverse(a, [0, 2])` -* `tf.reverse_sequence` - * keyword argument `batch_dim` should be renamed to `batch_axis` - * keyword argument `seq_dim` should be renamed to `seq_axis` -* `tf.sparse_concat` - * keyword argument `concat_dim` should be renamed to `axis` -* `tf.sparse_reduce_sum` - * keyword argument `reduction_axes` should be renamed to `axis` -* `tf.sparse_reduce_sum_sparse` - * keyword argument `reduction_axes` should be renamed to `axis` -* `tf.sparse_split` - * keyword argument `split_dim` should be renamed to `axis` - * arguments have been reordered to `tf.sparse_split(keyword_required=KeywordRequired(), sp_input=None, num_split=None, axis=None, name=None, split_dim=None)`. -* `tf.split` - * keyword argument `split_dim` should be renamed to `axis` - * keyword argument `num_split` should be renamed to `num_or_size_splits` - * arguments have been reordered to `tf.split(value, num_or_size_splits, axis=0, num=None, name='split')`. -* `tf.squeeze` - * keyword argument `squeeze_dims` should be renamed to `axis` -* `tf.svd` - * arguments have been reordered to `tf.svd(tensor, full_matrices=False, compute_uv=True, name=None)`. - -### Simplified math variants - -Batched versions of math operations have been removed. Now the functionality is -contained in the non-batched versions. Similarly,`tf.complex_abs` has had its -functionality moved to `tf.abs` - -* `tf.batch_band_part` - * should be renamed to `tf.band_part` -* `tf.batch_cholesky` - * should be renamed to `tf.cholesky` -* `tf.batch_cholesky_solve` - * should be renamed to `tf.cholesky_solve` -* `tf.batch_fft` - * should be renamed to `tf.fft` -* `tf.batch_fft3d` - * should be renamed to `tf.fft3d` -* `tf.batch_ifft` - * should be renamed to `tf.ifft` -* `tf.batch_ifft2d` - * should be renamed to `tf.ifft2d` -* `tf.batch_ifft3d` - * should be renamed to `tf.ifft3d` -* `tf.batch_matmul` - * should be renamed to `tf.matmul` -* `tf.batch_matrix_determinant` - * should be renamed to `tf.matrix_determinant` -* `tf.batch_matrix_diag` - * should be renamed to `tf.matrix_diag` -* `tf.batch_matrix_inverse` - * should be renamed to `tf.matrix_inverse` -* `tf.batch_matrix_solve` - * should be renamed to `tf.matrix_solve` -* `tf.batch_matrix_solve_ls` - * should be renamed to `tf.matrix_solve_ls` -* `tf.batch_matrix_transpose` - * should be renamed to `tf.matrix_transpose` -* `tf.batch_matrix_triangular_solve` - * should be renamed to `tf.matrix_triangular_solve` -* `tf.batch_self_adjoint_eig` - * should be renamed to `tf.self_adjoint_eig` -* `tf.batch_self_adjoint_eigvals` - * should be renamed to `tf.self_adjoint_eigvals` -* `tf.batch_set_diag` - * should be renamed to `tf.set_diag` -* `tf.batch_svd` - * should be renamed to `tf.svd` -* `tf.complex_abs` - * should be renamed to `tf.abs` - -### Misc Changes - -Several other changes have been made, including the following: - -* `tf.image.per_image_whitening` - * should be renamed to `tf.image.per_image_standardization` -* `tf.nn.sigmoid_cross_entropy_with_logits` - * arguments have been reordered to `tf.nn.sigmoid_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, name=None)`. -* `tf.nn.softmax_cross_entropy_with_logits` - * arguments have been reordered to `tf.nn.softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, dim=-1, name=None)`. -* `tf.nn.sparse_softmax_cross_entropy_with_logits` - * arguments have been reordered to `tf.nn.sparse_softmax_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, name=None)`. -* `tf.ones_initializer` - * should be changed to a function call i.e. `tf.ones_initializer()` -* `tf.pack` - * should be renamed to `tf.stack` -* `tf.round` - * The semantics of `tf.round` now match Banker's rounding. -* `tf.unpack` - * should be renamed to `tf.unstack` -* `tf.zeros_initializer` - * should be changed to a function call i.e. `tf.zeros_initializer()` - |