update to TensorLayer 1.8.1

.gitignore

@@ -0,0 +1,12 @@
+*.gz
+*.npz
+*.pyc
+*~
+.DS_Store
+.idea
+.spyproject/
+build/
+dist
+docs/_build
+tensorlayer.egg-info
+tensorlayer/__pacache__

main.py

@@ -1,5 +1,4 @@
-import os, sys, pprint, time
-import scipy.misc
+import os, pprint, time
 import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
@@ -111,7 +110,7 @@ def main(_):
         ## load image data
         batch_idxs = min(len(data_files), FLAGS.train_size) // FLAGS.batch_size
 
-        for idx in xrange(0, batch_idxs):
+        for idx in range(0, batch_idxs):
             batch_files = data_files[idx*FLAGS.batch_size:(idx+1)*FLAGS.batch_size]
             ## get real images
             # more image augmentation functions in http://tensorlayer.readthedocs.io/en/latest/modules/prepro.html

tensorlayer/__init__.py

@@ -1,8 +1,5 @@
-"""
-Deep learning and Reinforcement learning library for Researchers and Engineers
-"""
-# from __future__ import absolute_import
-
+"""Deep learning and Reinforcement learning library for Researchers and Engineers"""
+from __future__ import absolute_import
 
 try:
     install_instr = "Please make sure you install a recent enough version of TensorFlow."
@@ -11,21 +8,22 @@
     raise ImportError("__init__.py : Could not import TensorFlow." + install_instr)
 
 from . import activation
-act = activation
 from . import cost
 from . import files
-# from . import init
 from . import iterate
 from . import layers
-from . import ops
 from . import utils
 from . import visualize
-from . import prepro        # was preprocesse
+from . import prepro
 from . import nlp
 from . import rein
+from . import distributed
 
+# alias
+act = activation
+vis = visualize
 
-__version__ = "1.4.5"
+__version__ = "1.8.1"
 
 global_flag = {}
 global_dict = {}

tensorlayer/_logging.py

@@ -0,0 +1,16 @@
+import logging as _logger
+
+logging = _logger.getLogger('tensorlayer')
+logging.setLevel(_logger.INFO)
+_hander = _logger.StreamHandler()
+formatter = _logger.Formatter('[TL] %(message)s')
+_hander.setFormatter(formatter)
+logging.addHandler(_hander)
+
+
+def info(fmt, *args):
+    logging.info(fmt, *args)
+
+
+def warning(fmt, *args):
+    logging.warning(fmt, *args)

tensorlayer/activation.py

@@ -1,109 +1,154 @@
 #! /usr/bin/python
-# -*- coding: utf8 -*-
+# -*- coding: utf-8 -*-
 
+import tensorflow as tf
+from tensorflow.python.util.deprecation import deprecated
 
+__all__ = [
+    'identity',
+    'ramp',
+    'leaky_relu',
+    'swish',
+    'pixel_wise_softmax',
+    'linear',
+    'lrelu',
+]
 
-import tensorflow as tf
 
-def identity(x, name=None):
-    """The identity activation function, Shortcut is ``linear``.
+@deprecated("2018-06-30", "This API will be deprecated soon as tf.identity can do the same thing.")
+def identity(x):
+    """The identity activation function.
+    Shortcut is ``linear``.
 
     Parameters
     ----------
-    x : a tensor input
-        input(s)
-
+    x : Tensor
+        input.
 
     Returns
-    --------
-    A `Tensor` with the same type as `x`.
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
+
     """
     return x
 
-# Shortcut
-linear = identity
 
-def ramp(x=None, v_min=0, v_max=1, name=None):
+def ramp(x, v_min=0, v_max=1, name=None):
     """The ramp activation function.
 
     Parameters
     ----------
-    x : a tensor input
-        input(s)
+    x : Tensor
+        input.
     v_min : float
-        if input(s) smaller than v_min, change inputs to v_min
+        cap input to v_min as a lower bound.
     v_max : float
-        if input(s) greater than v_max, change inputs to v_max
-    name : a string or None
-        An optional name to attach to this activation function.
-
+        cap input to v_max as a upper bound.
+    name : str
+        The function name (optional).
 
     Returns
-    --------
-    A `Tensor` with the same type as `x`.
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
+
     """
     return tf.clip_by_value(x, clip_value_min=v_min, clip_value_max=v_max, name=name)
 
-def leaky_relu(x=None, alpha=0.1, name="LeakyReLU"):
+
+def leaky_relu(x, alpha=0.1, name="lrelu"):
     """The LeakyReLU, Shortcut is ``lrelu``.
 
-    Modified version of ReLU, introducing a nonzero gradient for negative
-    input.
+    Modified version of ReLU, introducing a nonzero gradient for negative input.
 
     Parameters
     ----------
-    x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
-        `int16`, or `int8`.
-    alpha : `float`. slope.
-    name : a string or None
-        An optional name to attach to this activation function.
+    x : Tensor
+        Support input type ``float``, ``double``, ``int32``, ``int64``, ``uint8``,
+        ``int16``, or ``int8``.
+    alpha : float
+        Slope.
+    name : str
+        The function name (optional).
 
     Examples
-    ---------
-    >>> network = tl.layers.DenseLayer(network, n_units=100, name = 'dense_lrelu',
-    ...                 act= lambda x : tl.act.lrelu(x, 0.2))
+    --------
+    >>> net = tl.layers.DenseLayer(net, 100, act=lambda x : tl.act.lrelu(x, 0.2), name='dense')
+
+    Returns
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
 
     References
     ------------
-    - `Rectifier Nonlinearities Improve Neural Network Acoustic Models, Maas et al. (2013) <http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf>`_
+    - `Rectifier Nonlinearities Improve Neural Network Acoustic Models, Maas et al. (2013) <http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf>`__
+
     """
-    with tf.name_scope(name) as scope:
-        # x = tf.nn.relu(x)
-        # m_x = tf.nn.relu(-x)
-        # x -= alpha * m_x
-        x = tf.maximum(x, alpha * x)
+    # with tf.name_scope(name) as scope:
+    # x = tf.nn.relu(x)
+    # m_x = tf.nn.relu(-x)
+    # x -= alpha * m_x
+    x = tf.maximum(x, alpha * x, name=name)
     return x
 
-#Shortcut
-lrelu = leaky_relu
 
-def pixel_wise_softmax(output, name='pixel_wise_softmax'):
+def swish(x, name='swish'):
+    """The Swish function.
+     See `Swish: a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941>`__.
+
+    Parameters
+    ----------
+    x : Tensor
+        input.
+    name: str
+        function name (optional).
+
+    Returns
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
+
+    """
+    with tf.name_scope(name):
+        x = tf.nn.sigmoid(x) * x
+    return x
+
+
+@deprecated("2018-06-30", "This API will be deprecated soon as tf.nn.softmax can do the same thing.")
+def pixel_wise_softmax(x, name='pixel_wise_softmax'):
     """Return the softmax outputs of images, every pixels have multiple label, the sum of a pixel is 1.
     Usually be used for image segmentation.
 
     Parameters
-    ------------
-    output : tensor
-        - For 2d image, 4D tensor [batch_size, height, weight, channel], channel >= 2.
-        - For 3d image, 5D tensor [batch_size, depth, height, weight, channel], channel >= 2.
+    ----------
+    x : Tensor
+        input.
+            - For 2d image, 4D tensor (batch_size, height, weight, channel), where channel >= 2.
+            - For 3d image, 5D tensor (batch_size, depth, height, weight, channel), where channel >= 2.
+    name : str
+        function name (optional)
+
+    Returns
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
 
     Examples
-    ---------
+    --------
     >>> outputs = pixel_wise_softmax(network.outputs)
     >>> dice_loss = 1 - dice_coe(outputs, y_, epsilon=1e-5)
 
     References
-    -----------
-    - `tf.reverse <https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html#reverse>`_
+    ----------
+    - `tf.reverse <https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html#reverse>`__
+
     """
-    with tf.name_scope(name) as scope:
-        return tf.nn.softmax(output)
-        ## old implementation
-        # exp_map = tf.exp(output)
-        # if output.get_shape().ndims == 4:   # 2d image
-        #     evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, True]))
-        # elif output.get_shape().ndims == 5: # 3d image
-        #     evidence = tf.add(exp_map, tf.reverse(exp_map, [False, False, False, False, True]))
-        # else:
-        #     raise Exception("output parameters should be 2d or 3d image, not %s" % str(output._shape))
-        # return tf.div(exp_map, evidence)
+    with tf.name_scope(name):
+        return tf.nn.softmax(x)
+
+
+# Alias
+linear = identity
+lrelu = leaky_relu

tensorlayer/cli/__init__.py

@@ -0,0 +1 @@
+"""The tensorlayer.cli module provides a command-line tool for some common tasks."""

tensorlayer/cli/__main__.py

@@ -0,0 +1,14 @@
+import argparse
+
+from tensorlayer.cli import train
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(prog='tl')
+    subparsers = parser.add_subparsers(dest='cmd')
+    train_parser = subparsers.add_parser('train', help='train a model using multiple local GPUs or CPUs.')
+    train.build_arg_parser(train_parser)
+    args = parser.parse_args()
+    if args.cmd == 'train':
+        train.main(args)
+    else:
+        parser.print_help()