____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 19, 3) 0
____________________________________________________________________________________________________
input_2 (InputLayer) (None, 19, 20) 0
____________________________________________________________________________________________________
merge_1 (Merge) (None, 19, 23) 0 input_1[0][0]
input_2[0][0]
____________________________________________________________________________________________________
lambda_1 (Lambda) (None, 20, 103) 0 merge_1[0][0]
____________________________________________________________________________________________________
reshape_1 (Reshape) (None, 1, 2060) 0 lambda_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 1, 200) 412200 reshape_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 1, 50) 10050 dense_1[0][0]
____________________________________________________________________________________________________
dense_3 (Dense) (None, 1, 10) 510 dense_2[0][0]
____________________________________________________________________________________________________
dense_4 (Dense) (None, 1, 1) 11 dense_3[0][0]
====================================================================================================
Total params: 422,771
Trainable params: 422,771
Non-trainable params: 0
____________________________________________________________________________________________________
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
noise_input (InputLayer) (None, 1, 100) 0
____________________________________________________________________________________________________
dense_5 (Dense) (None, 1, 100) 10100 noise_input[0][0]
____________________________________________________________________________________________________
dense_6 (Dense) (None, 1, 100) 10100 dense_5[0][0]
____________________________________________________________________________________________________
dense_7 (Dense) (None, 1, 50) 5050 dense_6[0][0]
____________________________________________________________________________________________________
dense_8 (Dense) (None, 1, 57) 2907 dense_7[0][0]
____________________________________________________________________________________________________
reshape_2 (Reshape) (None, 19, 3) 0 dense_8[0][0]
====================================================================================================
Total params: 28,157
Trainable params: 28,157
Non-trainable params: 0
____________________________________________________________________________________________________
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
noise_input (InputLayer) (None, 1, 100) 0
____________________________________________________________________________________________________
dense_9 (Dense) (None, 1, 100) 10100 noise_input[0][0]
____________________________________________________________________________________________________
dense_10 (Dense) (None, 1, 100) 10100 dense_9[0][0]
____________________________________________________________________________________________________
dense_11 (Dense) (None, 1, 380) 38380 dense_10[0][0]
____________________________________________________________________________________________________
reshape_3 (Reshape) (None, 19, 20) 0 dense_11[0][0]
____________________________________________________________________________________________________
lambda_2 (Lambda) (None, 19, 20) 0 reshape_3[0][0]
====================================================================================================
Total params: 58,580
Trainable params: 58,580
Non-trainable params: 0
____________________________________________________________________________________________________
====================
Epoch #0
After 20 iterations
Discriminator Loss = 0.0047345017083
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/scipy/sparse/compressed.py:730: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
SparseEfficiencyWarning)
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
if self._edgecolors == str('face'):
Generator_Loss: 5.39226818085
2
Level #1 Epoch #0 Batch #2
After 20 iterations
Discriminator Loss = 0.00274331355467
Generator_Loss: 5.00458955765
3
After 20 iterations
Discriminator Loss = 0.00540957273915
Generator_Loss: 5.26497173309
4
Level #1 Epoch #0 Batch #4
After 20 iterations
Discriminator Loss = 0.0299781206995
Generator_Loss: 5.81227207184
5
After 20 iterations
Discriminator Loss = 0.0337119176984
Generator_Loss: 5.42659473419
6
Level #1 Epoch #0 Batch #6
After 20 iterations
Discriminator Loss = 0.0330997295678
Generator_Loss: 5.48542070389
7
After 20 iterations
Discriminator Loss = 0.100402034819
Generator_Loss: 4.51286172867
8
Level #1 Epoch #0 Batch #8
After 20 iterations
Discriminator Loss = 0.118518576026
Generator_Loss: 4.84774112701
9
After 20 iterations
Discriminator Loss = 0.125451266766
Generator_Loss: 5.12430334091
10
Level #1 Epoch #0 Batch #10
After 20 iterations
Discriminator Loss = 0.13253980875
Generator_Loss: 5.21964406967
11
After 20 iterations
Discriminator Loss = 0.208805814385
Generator_Loss: 4.31326818466
12
Level #1 Epoch #0 Batch #12
After 20 iterations
Discriminator Loss = 0.239489763975
Generator_Loss: 4.4233584404
13
After 20 iterations
Discriminator Loss = 0.25026935339
Generator_Loss: 4.2036819458
14
Level #1 Epoch #0 Batch #14
After 20 iterations
Discriminator Loss = 0.272627562284
Generator_Loss: 3.60699915886
15
After 20 iterations
Discriminator Loss = 0.339631408453
Generator_Loss: 3.72375679016
16
Level #1 Epoch #0 Batch #16
After 20 iterations
Discriminator Loss = 0.20184931159
Generator_Loss: 3.19971871376
17
After 20 iterations
Discriminator Loss = 0.364580959082
Generator_Loss: 3.25285124779
18
Level #1 Epoch #0 Batch #18
After 20 iterations
Discriminator Loss = 0.485932379961
Generator_Loss: 2.58915328979
19
After 20 iterations
Discriminator Loss = 0.344829201698
Generator_Loss: 2.45035338402
20
Level #1 Epoch #0 Batch #20
After 20 iterations
Discriminator Loss = 0.438456207514
Generator_Loss: 1.5708386898
21
After 20 iterations
Discriminator Loss = 0.414980202913
Generator_Loss: 2.06802582741
22
Level #1 Epoch #0 Batch #22
After 20 iterations
Discriminator Loss = 0.505568742752
Generator_Loss: 1.85510134697
23
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-5-db9248fa6980> in <module>()
13 rule=rule,
14 train_loss=train_loss,
---> 15 verbose=True)
/Users/RoozbehFarhoudi/Documents/Repos/BonsaiNet/train.pyc in train_model(training_data, n_nodes, input_dim, n_epochs, batch_size, n_batch_per_epoch, d_iters, lr_discriminator, lr_generator, d_weight_constraint, g_weight_constraint, m_weight_constraint, rule, train_loss, verbose)
301 d_model.train_on_batch([X_locations_real_first_half,
302 X_parent_real_first_half],
--> 303 y_real_first_half)
304 list_d_loss.append(disc_loss)
305 disc_loss = \
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in train_on_batch(self, x, y, sample_weight, class_weight)
1318 ins = x + y + sample_weights
1319 self._make_train_function()
-> 1320 outputs = self.train_function(ins)
1321 if len(outputs) == 1:
1322 return outputs[0]
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.pyc in __call__(self, inputs)
957 def __call__(self, inputs):
958 assert isinstance(inputs, (list, tuple))
--> 959 return self.function(*inputs)
960
961
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
857 t0_fn = time.time()
858 try:
--> 859 outputs = self.fn()
860 except Exception:
861 if hasattr(self.fn, 'position_of_error'):
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc in rval(p, i, o, n)
910 # default arguments are stored in the closure of `rval`
911 def rval(p=p, i=node_input_storage, o=node_output_storage, n=node):
--> 912 r = p(n, [x[0] for x in i], o)
913 for o in node.outputs:
914 compute_map[o][0] = True
/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/theano/tensor/blas.pyc in perform(self, node, inp, out)
1550 z, = out
1551 try:
-> 1552 z[0] = numpy.asarray(numpy.dot(x, y))
1553 except ValueError as e:
1554 # The error raised by numpy has no shape information, we mean to
KeyboardInterrupt: