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Multiple input #31
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you may want to use >>> x = tf.placeholder(tf.float32, shape=[None, 784])
>>> inputs = tl.layers.InputLayer(x, name='input_layer')
>>> x2 = tf.placeholder(tf.float32, shape=[None, 784])
>>> inputs2 = tl.layers.InputLayer(x, name='input_layer2')
>>> net1 = tl.layers.DenseLayer(inputs, n_units=800, act = tf.nn.relu, name='relu1_1')
>>> net2 = tl.layers.DenseLayer(inputs2, n_units=300, act = tf.nn.relu, name='relu2_1')
>>> network = tl.layers.ConcatLayer(layer = [net1, net2], name ='concat_layer') for multi-output, just simply reuse a layer. >>> net1 = tl.layers.DenseLayer(inputs, n_units=800, act = tf.nn.relu, name='relu1_1')
>>> net_out1 = tl.layers.DenseLayer(net1, n_units=800, name='out1')
>>> net_out2 = tl.layers.DenseLayer(net1, n_units=800, name='out2') |
I means how to input two different data. |
@HzFu you can do exactly like the script I send you. |
I wonder can we use tl.iterate.minibatches to generate "feed_dict" to feed multiple inputs into the session? I tried but it seems that minibatches() only takes 4 augments, which mean single input |
@xjtuljy in that case, you may need to extend
|
OK I see, thanks! |
thank you very much!!! |
Can the tensorlayer support multi-input and multi-output models ?
Is there any multi-input demo or example?
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