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Merged
merged 5 commits into from
Apr 17, 2018
Merged

Update conv1d #499

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8b88ee1
update conv1d with tf.layers
zsdonghao Apr 17, 2018
dfbaddf
update conv1d fun --> class
zsdonghao Apr 17, 2018
1907ebf
yapf
zsdonghao Apr 17, 2018
6861a61
fix super
zsdonghao Apr 17, 2018
a8a6fd6
Merge branch 'master' into update-conv1d
DEKHTIARJonathan Apr 17, 2018
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2 changes: 1 addition & 1 deletion docs/modules/layers.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -484,7 +484,7 @@ APIs may better for you.

1D Convolution
^^^^^^^^^^^^^^^^^^^^^^^
.. autofunction:: Conv1d
.. autoclass:: Conv1d

2D Convolution
^^^^^^^^^^^^^^^^^^^^^^^
Expand Down
116 changes: 66 additions & 50 deletions tensorlayer/layers/convolution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,24 +85,21 @@ def __init__(

if act is None:
act = tf.identity

if W_init_args is None:
W_init_args = {}
if b_init_args is None:
b_init_args = {}

with tf.variable_scope(name):
W = tf.get_variable(name='W_conv1d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
self.outputs = tf.nn.convolution(
self.inputs, W, strides=(stride, ), padding=padding, dilation_rate=(dilation_rate, ), data_format=data_format) # 1.2
self.outputs = tf.nn.convolution(self.inputs, W, strides=(stride, ), padding=padding, dilation_rate=(dilation_rate, )) # 1.2
if b_init:
b = tf.get_variable(name='b_conv1d', shape=(shape[-1]), initializer=b_init, dtype=LayersConfig.tf_dtype, **b_init_args)
self.outputs = self.outputs + b

self.outputs = act(self.outputs)

# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
if b_init:
self.all_params.extend([W, b])
Expand Down Expand Up @@ -1260,22 +1257,7 @@ def deconv2d_bilinear_upsampling_initializer(shape):
return bilinear_weights_init


@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def conv1d(
prev_layer,
n_filter=32,
filter_size=5,
stride=1,
dilation_rate=1,
act=tf.identity,
padding='SAME',
data_format="NWC",
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
name='conv1d',
):
class Conv1d(Layer):
"""Simplified version of :class:`Conv1dLayer`.

Parameters
Expand All @@ -1301,17 +1283,12 @@ def conv1d(
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
The arguments for the weight matrix initializer (deprecated).
b_init_args : dictionary
The arguments for the bias vector initializer.
The arguments for the bias vector initializer (deprecated).
name : str
A unique layer name

Returns
-------
:class:`Layer`
A :class:`Conv1dLayer` object.

Examples
---------
>>> x = tf.placeholder(tf.float32, (batch_size, width))
Expand All @@ -1331,25 +1308,67 @@ def conv1d(

"""

if W_init_args is None:
W_init_args = {}
if b_init_args is None:
b_init_args = {}
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self,
prev_layer,
n_filter=32,
filter_size=5,
stride=1,
dilation_rate=1,
act=tf.identity,
padding='SAME',
data_format="channels_last",
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None,
name='conv1d'):

return Conv1dLayer(
prev_layer=prev_layer,
act=act,
shape=(filter_size, int(prev_layer.outputs.get_shape()[-1]), n_filter),
stride=stride,
dilation_rate=dilation_rate,
padding=padding,
data_format=data_format,
W_init=W_init,
b_init=b_init,
W_init_args=W_init_args,
b_init_args=b_init_args,
name=name,
)
super(Conv1d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("Conv1d %s: n_filter:%d filter_size:%s stride:%d pad:%s act:%s dilation_rate:%d" % (name, n_filter, filter_size, stride, padding,
act.__name__, dilation_rate))

self.inputs = prev_layer.outputs
if tf.__version__ > '1.3':
con1d = tf.layers.Conv1D(
filters=n_filter,
kernel_size=filter_size,
strides=stride,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=act,
use_bias=(True if b_init else False),
kernel_initializer=W_init,
bias_initializer=b_init,
name=name)
# con1d.dtype = LayersConfig.tf_dtype # unsupport, it will use the same dtype of inputs
self.outputs = con1d(self.inputs)
new_variables = con1d.weights # new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
self.all_layers.append(self.outputs)
self.all_params.extend(new_variables)
else:
raise RuntimeError("please update TF > 1.3 or downgrade TL < 1.8.4")
# if W_init_args is None:
# W_init_args = {}
# if b_init_args is None:
# b_init_args = {}
# data_format='HWC'
# return Conv1dLayer(

# prev_layer=prev_layer,
# act=act,
# shape=(filter_size, int(prev_layer.outputs.get_shape()[-1]), n_filter),
# stride=stride,
# dilation_rate=dilation_rate,
# padding=padding,
# data_format=data_format,
# W_init=W_init,
# b_init=b_init,
# W_init_args=W_init_args,
# b_init_args=b_init_args,
# name=name,
# )


# TODO: DeConv1d
Expand Down Expand Up @@ -1682,9 +1701,6 @@ def __init__(self,
)
new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)

# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
self.all_params.extend(new_variables)

Expand Down Expand Up @@ -2010,6 +2026,6 @@ def __init__(

# Alias
AtrousConv1dLayer = atrous_conv1d
Conv1d = conv1d
# Conv1d = conv1d
# Conv2d = conv2d
# DeConv2d = deconv2d