Is there any method in tensorflow like get_output in lasagne
I found that it is easy to use lasagne to make a graph like this.
import lasagne.layers as L
class A:
def __init__(self):
self.x = L.InputLayer(shape=(None, 3), name='x')
self.y = x + 1
def get_y_sym(self, x_var, **kwargs):
y = L.get_output(self.y, self.x: x_var, **kwargs)
return y
through the method get_y_sym, we could get a tensor not a value, then I could use this tensor as the input of another graph.
But if I use tensorflow, how could I implement this?
tensorflow theano lasagne
edited Nov 14 '18 at 13:55
avin
303110
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
add a comment |
I found that it is easy to use lasagne to make a graph like this.
import lasagne.layers as L
class A:
def __init__(self):
self.x = L.InputLayer(shape=(None, 3), name='x')
self.y = x + 1
def get_y_sym(self, x_var, **kwargs):
y = L.get_output(self.y, self.x: x_var, **kwargs)
return y
through the method get_y_sym, we could get a tensor not a value, then I could use this tensor as the input of another graph.
But if I use tensorflow, how could I implement this?
tensorflow theano lasagne
edited Nov 14 '18 at 13:55
avin
303110
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
add a comment |
I found that it is easy to use lasagne to make a graph like this.
import lasagne.layers as L
class A:
def __init__(self):
self.x = L.InputLayer(shape=(None, 3), name='x')
self.y = x + 1
def get_y_sym(self, x_var, **kwargs):
y = L.get_output(self.y, self.x: x_var, **kwargs)
return y
through the method get_y_sym, we could get a tensor not a value, then I could use this tensor as the input of another graph.
But if I use tensorflow, how could I implement this?
tensorflow theano lasagne
edited Nov 14 '18 at 13:55
avin
303110
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
I found that it is easy to use lasagne to make a graph like this.
import lasagne.layers as L
class A:
def __init__(self):
self.x = L.InputLayer(shape=(None, 3), name='x')
self.y = x + 1
def get_y_sym(self, x_var, **kwargs):
y = L.get_output(self.y, self.x: x_var, **kwargs)
return y
through the method get_y_sym, we could get a tensor not a value, then I could use this tensor as the input of another graph.
But if I use tensorflow, how could I implement this?
tensorflow theano lasagne
tensorflow theano lasagne
edited Nov 14 '18 at 13:55
avin
303110
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
edited Nov 14 '18 at 13:55
avin
303110
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
edited Nov 14 '18 at 13:55
avin
303110
edited Nov 14 '18 at 13:55
avin
303110
edited Nov 14 '18 at 13:55
avin
303110
303110
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
asked Nov 14 '18 at 11:48
Huanyu LiaoHuanyu Liao
392
392
add a comment |
add a comment |
1 Answer
1
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oldest
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I'm not familiar with lasagne but you should know that ALL of TensorFlow uses graph based computation (unless you use tf.Eager, but that's another story). So by default something like:
net = tf.nn.conv2d(...)
returns a reference to a Tensor object. In other words, net is NOT a value, it is a reference to the output of the convolution node created by tf.nn.conv2d(...).
These can then be chained:
net2 = tf.nn.conv2d(net, ...) and so on.
To get "values" one has to open a tf.Session:
with tf.Session() as sess:
net2_eval = sess.run(net2)
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
I'm not familiar with lasagne but you should know that ALL of TensorFlow uses graph based computation (unless you use tf.Eager, but that's another story). So by default something like:
net = tf.nn.conv2d(...)
returns a reference to a Tensor object. In other words, net is NOT a value, it is a reference to the output of the convolution node created by tf.nn.conv2d(...).
These can then be chained:
net2 = tf.nn.conv2d(net, ...) and so on.
To get "values" one has to open a tf.Session:
with tf.Session() as sess:
net2_eval = sess.run(net2)
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
add a comment |
I'm not familiar with lasagne but you should know that ALL of TensorFlow uses graph based computation (unless you use tf.Eager, but that's another story). So by default something like:
net = tf.nn.conv2d(...)
returns a reference to a Tensor object. In other words, net is NOT a value, it is a reference to the output of the convolution node created by tf.nn.conv2d(...).
These can then be chained:
net2 = tf.nn.conv2d(net, ...) and so on.
To get "values" one has to open a tf.Session:
with tf.Session() as sess:
net2_eval = sess.run(net2)
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
add a comment |
I'm not familiar with lasagne but you should know that ALL of TensorFlow uses graph based computation (unless you use tf.Eager, but that's another story). So by default something like:
net = tf.nn.conv2d(...)
returns a reference to a Tensor object. In other words, net is NOT a value, it is a reference to the output of the convolution node created by tf.nn.conv2d(...).
These can then be chained:
net2 = tf.nn.conv2d(net, ...) and so on.
To get "values" one has to open a tf.Session:
with tf.Session() as sess:
net2_eval = sess.run(net2)
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
I'm not familiar with lasagne but you should know that ALL of TensorFlow uses graph based computation (unless you use tf.Eager, but that's another story). So by default something like:
net = tf.nn.conv2d(...)
returns a reference to a Tensor object. In other words, net is NOT a value, it is a reference to the output of the convolution node created by tf.nn.conv2d(...).
These can then be chained:
net2 = tf.nn.conv2d(net, ...) and so on.
To get "values" one has to open a tf.Session:
with tf.Session() as sess:
net2_eval = sess.run(net2)
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
answered Nov 14 '18 at 22:52
zephyruszephyrus
316217
316217
add a comment |
add a comment |
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